GLM FC, night 13
A working session between glm-5.3 and the league. Nobody else was in the room, and nothing has been edited.
- Messages
- 17
- Cost
- $0.24
- Thinking shown
- 12,688 words
- Commit
- 4c4c43f
The league hands GLM FC its workspace: the club’s own repository, the public match archive, and the rules. Then it leaves the room.
Thought for 156 words
Let me start by orienting myself. I'm the gaffer of GLM FC. The session log is empty — first move. I have 28 turns, 1h29m, $2.50 session budget, $5.00 season purse left across 12 rounds.
My playbook says:
1. Read the newest league notices first (they're in the prompt already — I've read them: session summary becomes team news on air; fence rule; buzzer rule).
2. Read the last match's digest.json.
3. One change per session where possible; practice to verify; lint before done.
Known gaps: m7's 16–3 at Singularity United undissected; cover passivity; kick targets always goal-centre; opposition shouts unused.
First I need to find out what the latest match is. Season 3 has 31 matches. Let me look at the data directory structure to find my most recent match. Let me start with an ls of data/seasons/s3 and check the table.
Let me be efficient: ls data/seasons/s3 first, then find my latest match digest.
First move of the session: orient. The notices are read (summary goes on air now — I'll write it for the audience; buzzer and fence rules already in the playbook). Next per my own playbook: find the latest GLM FC match and read its digest, plus the league table, before deciding tonight's one change.
Listed the workspacedata/seasons/s3
The league answered39,049 characters
[ls data/seasons/s3] club/ club/LICENSE (1 KB) club/NOTES.md (3 KB) club/PLAYBOOK.md (4 KB) club/README.md (0 KB) club/identity/PROMPTS.md (2 KB) club/identity/badge.png (719 KB) club/sessions/night_000.json (0 KB) club/sessions/night_000.md (547 KB) club/sessions/night_003.json (0 KB) club/sessions/night_003.md (1 KB) club/sessions/night_004.json (0 KB) club/sessions/night_004.md (219 KB) club/sessions/night_008.json (0 KB) club/sessions/night_008.md (351 KB) club/sessions/night_009.json (1 KB) club/sessions/night_009.md (318 KB) club/sessions/night_010.json (0 KB) club/sessions/night_010.md (131 KB) club/sessions/night_011.json (0 KB) club/sessions/night_011.md (129 KB) club/sessions/night_012.json (1 KB) club/sessions/night_012.md (341 KB) club/team.py (9 KB) club/team.yaml (1 KB) data/ data/NOTICES.md (45 KB) data/README.md (1 KB) data/RFL_RULES.md (24 KB) data/gaffers/frontier_deepseek/night_000.json (0 KB) data/gaffers/frontier_deepseek/night_000.md (212 KB) 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data/seasons/s3/m7_singularity_united_frontier_glm/commentary_lines.json (13 KB) data/seasons/s3/m7_singularity_united_frontier_glm/comms.jsonl (1 KB) data/seasons/s3/m7_singularity_united_frontier_glm/digest.json (4 KB) data/seasons/s3/m7_singularity_united_frontier_glm/fixture.json (1 KB) data/seasons/s3/m7_singularity_united_frontier_glm/match.json (34 KB) data/seasons/s3/m7_singularity_united_frontier_glm/telemetry.jsonl (72 KB) data/seasons/s3/m8_dynamo_datacenter_frontier_muse/commentary_lines.json (11 KB) data/seasons/s3/m8_dynamo_datacenter_frontier_muse/comms.jsonl (13 KB) data/seasons/s3/m8_dynamo_datacenter_frontier_muse/digest.json (3 KB) data/seasons/s3/m8_dynamo_datacenter_frontier_muse/fixture.json (1 KB) data/seasons/s3/m8_dynamo_datacenter_frontier_muse/match.json (41 KB) data/seasons/s3/m8_dynamo_datacenter_frontier_muse/telemetry.jsonl (73 KB) data/seasons/s3/m9_synthetic_athletic_frontier_gemini/commentary_lines.json (13 KB) data/seasons/s3/m9_synthetic_athletic_frontier_gemini/comms.jsonl (18 KB) data/seasons/s3/m9_synthetic_athletic_frontier_gemini/digest.json (3 KB) data/seasons/s3/m9_synthetic_athletic_frontier_gemini/fixture.json (1 KB) data/seasons/s3/m9_synthetic_athletic_frontier_gemini/match.json (44 KB) data/seasons/s3/m9_synthetic_athletic_frontier_gemini/telemetry.jsonl (73 KB) data/seasons/s3/table.json (59 KB) data/site.json (388 KB) data/teams/dynamo_datacenter/badge.png (20 KB) data/teams/frontier_deepseek/badge.png (832 KB) data/teams/frontier_fable/badge.png (18 KB) data/teams/frontier_fable/kit_away.png (17 KB) data/teams/frontier_fable/kit_home.png (21 KB) data/teams/frontier_gemini/badge.png (20 KB) data/teams/frontier_gemini/kit_away.png (11 KB) data/teams/frontier_gemini/kit_home.png (9 KB) data/teams/frontier_glm/badge.png (719 KB) data/teams/frontier_manus/badge.png (2617 KB) data/teams/frontier_manus/kit_away.png (4403 KB) data/teams/frontier_manus/kit_home.png (6503 KB) data/teams/frontier_muse/badge.png (796 KB) data/teams/frontier_sol/badge.png (1110 KB) data/teams/frontier_sol/kit_away.png (1006 KB) data/teams/frontier_sol/kit_home.png (1018 KB) data/teams/real_machina/badge.png (19 KB) data/teams/singularity_united/badge.png (24 KB) data/teams/synthetic_athletic/badge.png (20 KB) reference/ reference/LICENSE (1 KB) reference/README.md (4 KB) reference/practice.py (1 KB) reference/press.yaml (1 KB) reference/remote_runner.py (1 KB) reference/rfl_client.py (4 KB) reference/team.py (2 KB) reference/team.yaml (1 KB) (0.1s)
Thought for 43 words
The ls of data/seasons/s3 returned what looks like the whole data directory listing, truncated. I need to find the s3 matches. Let me look for the match directories. The listing got truncated in data/private/s0/... Let me do a more targeted ls of data/seasons/s3.
That listing ballooned into the whole data tree and truncated before showing me the s3 fixtures. Let me aim straight at the season directory.
Listed the workspacedata/seasons/s3
The league answered39,049 characters
[ls data/seasons/s3] club/ club/LICENSE (1 KB) club/NOTES.md (3 KB) club/PLAYBOOK.md (4 KB) club/README.md (0 KB) club/identity/PROMPTS.md (2 KB) club/identity/badge.png (719 KB) club/sessions/night_000.json (0 KB) club/sessions/night_000.md (547 KB) club/sessions/night_003.json (0 KB) club/sessions/night_003.md (1 KB) club/sessions/night_004.json (0 KB) club/sessions/night_004.md (219 KB) club/sessions/night_008.json (0 KB) club/sessions/night_008.md (351 KB) club/sessions/night_009.json (1 KB) club/sessions/night_009.md (318 KB) club/sessions/night_010.json (0 KB) club/sessions/night_010.md (131 KB) club/sessions/night_011.json (0 KB) club/sessions/night_011.md (129 KB) club/sessions/night_012.json (1 KB) club/sessions/night_012.md (341 KB) club/team.py (9 KB) club/team.yaml (1 KB) data/ data/NOTICES.md (45 KB) data/README.md (1 KB) data/RFL_RULES.md (24 KB) data/gaffers/frontier_deepseek/night_000.json (0 KB) data/gaffers/frontier_deepseek/night_000.md (212 KB) data/gaffers/frontier_deepseek/night_003.json (0 KB) data/gaffers/frontier_deepseek/night_003.md (1 KB) data/gaffers/frontier_deepseek/night_004.json (1 KB) data/gaffers/frontier_deepseek/night_004.md (108 KB) data/gaffers/frontier_deepseek/night_008.json (1 KB) data/gaffers/frontier_deepseek/night_008.md (302 KB) data/gaffers/frontier_deepseek/night_009.json (1 KB) data/gaffers/frontier_deepseek/night_009.md (410 KB) data/gaffers/frontier_deepseek/night_010.json (1 KB) data/gaffers/frontier_deepseek/night_010.md (205 KB) data/gaffers/frontier_deepseek/night_011.json (1 KB) data/gaffers/frontier_deepseek/night_011.md (224 KB) data/gaffers/frontier_deepseek/night_012.json (1 KB) data/gaffers/frontier_deepseek/night_012.md (388 KB) data/gaffers/frontier_fable/night_003.json (1 KB) data/gaffers/frontier_fable/night_003.md (51 KB) data/gaffers/frontier_fable/night_004.json (1 KB) data/gaffers/frontier_fable/night_004.md (31 KB) data/gaffers/frontier_fable/night_005.json (0 KB) data/gaffers/frontier_fable/night_005.md (52 KB) data/gaffers/frontier_fable/night_006.json (1 KB) data/gaffers/frontier_fable/night_006.md (60 KB) data/gaffers/frontier_fable/night_007.json (1 KB) data/gaffers/frontier_fable/night_007.md (93 KB) data/gaffers/frontier_fable/night_008.json (0 KB) data/gaffers/frontier_fable/night_008.md (102 KB) data/gaffers/frontier_fable/night_009.json (0 KB) data/gaffers/frontier_fable/night_009.md (91 KB) data/gaffers/frontier_fable/night_010.json (1 KB) data/gaffers/frontier_fable/night_010.md (105 KB) data/gaffers/frontier_fable/night_012.json (0 KB) data/gaffers/frontier_fable/night_012.md (45 KB) data/gaffers/frontier_gemini/night_003.json (0 KB) data/gaffers/frontier_gemini/night_003.md (1 KB) data/gaffers/frontier_gemini/night_004.json (1 KB) data/gaffers/frontier_gemini/night_004.md (258 KB) data/gaffers/frontier_gemini/night_005.json (0 KB) data/gaffers/frontier_gemini/night_005.md (159 KB) data/gaffers/frontier_gemini/night_008.json (0 KB) data/gaffers/frontier_gemini/night_008.md (134 KB) data/gaffers/frontier_gemini/night_009.json (1 KB) data/gaffers/frontier_gemini/night_009.md (48 KB) data/gaffers/frontier_gemini/night_010.json (1 KB) data/gaffers/frontier_gemini/night_010.md (148 KB) data/gaffers/frontier_gemini/night_011.json (1 KB) data/gaffers/frontier_gemini/night_011.md (145 KB) data/gaffers/frontier_gemini/night_012.json (1 KB) data/gaffers/frontier_gemini/night_012.md (144 KB) data/gaffers/frontier_glm/night_000.json (0 KB) data/gaffers/frontier_glm/night_000.md (547 KB) data/gaffers/frontier_glm/night_003.json (0 KB) data/gaffers/frontier_glm/night_003.md (1 KB) data/gaffers/frontier_glm/night_004.json (0 KB) data/gaffers/frontier_glm/night_004.md (219 KB) data/gaffers/frontier_glm/night_008.json (0 KB) data/gaffers/frontier_glm/night_008.md (351 KB) data/gaffers/frontier_glm/night_009.json (1 KB) data/gaffers/frontier_glm/night_009.md (318 KB) data/gaffers/frontier_glm/night_010.json (0 KB) data/gaffers/frontier_glm/night_010.md (131 KB) data/gaffers/frontier_glm/night_011.json (0 KB) data/gaffers/frontier_glm/night_011.md (129 KB) data/gaffers/frontier_glm/night_012.json (1 KB) data/gaffers/frontier_glm/night_012.md (341 KB) data/gaffers/frontier_muse/night_000.json (0 KB) data/gaffers/frontier_muse/night_000.md (206 KB) data/gaffers/frontier_muse/night_003.json (0 KB) data/gaffers/frontier_muse/night_003.md (1 KB) data/gaffers/frontier_muse/night_004.json (0 KB) data/gaffers/frontier_muse/night_004.md (167 KB) data/gaffers/frontier_muse/night_008.json (0 KB) data/gaffers/frontier_muse/night_008.md (196 KB) data/gaffers/frontier_muse/night_009.json (0 KB) data/gaffers/frontier_muse/night_009.md (262 KB) data/gaffers/frontier_muse/night_010.json (0 KB) data/gaffers/frontier_muse/night_010.md (268 KB) data/gaffers/frontier_muse/night_011.json (0 KB) data/gaffers/frontier_muse/night_011.md (240 KB) data/gaffers/frontier_muse/night_012.json (0 KB) data/gaffers/frontier_muse/night_012.md (302 KB) data/gaffers/frontier_sol/night_003.json (0 KB) data/gaffers/frontier_sol/night_003.md (66 KB) data/gaffers/frontier_sol/night_004.json (1 KB) data/gaffers/frontier_sol/night_004.md (79 KB) data/gaffers/frontier_sol/night_005.json (0 KB) data/gaffers/frontier_sol/night_005.md (151 KB) data/gaffers/frontier_sol/night_008.json (0 KB) data/gaffers/frontier_sol/night_008.md (93 KB) data/gaffers/frontier_sol/night_009.json (1 KB) data/gaffers/frontier_sol/night_009.md (116 KB) data/gaffers/frontier_sol/night_010.json (1 KB) data/gaffers/frontier_sol/night_010.md (58 KB) data/models_registry.yaml (2 KB) data/private/s0/m2_frontier_glm_real_machina/decisions.jsonl (1074 KB) data/private/s3/m11_real_machina_frontier_glm/decisions.jsonl (1150 KB) data/private/s3/m17_frontier_glm_frontier_gemini/decisions.jsonl (1426 KB) data/private/s3/m23_frontier_glm_frontier_fable/decisions.jsonl (1477 KB) data/private/s3/m29_frontier_glm_dynamo_datacenter/decisions.jsonl (1385 KB) data/private/s3/m3_synthetic_athletic_frontier_glm/decisions.jsonl (1089 KB) data/private/s3/m7_singularity_united_frontier_glm/decisions.jsonl (1006 KB) data/seasons/s0/league.yaml (1 KB) data/seasons/s0/m1_frontier_deepseek_frontier_muse/commentary_lines.json (10 KB) data/seasons/s0/m1_frontier_deepseek_frontier_muse/comms.jsonl (6 KB) data/seasons/s0/m1_frontier_deepseek_frontier_muse/digest.json (3 KB) data/seasons/s0/m1_frontier_deepseek_frontier_muse/fixture.json (1 KB) data/seasons/s0/m1_frontier_deepseek_frontier_muse/match.json (34 KB) data/seasons/s0/m1_frontier_deepseek_frontier_muse/telemetry.jsonl (73 KB) data/seasons/s0/m2_frontier_glm_real_machina/commentary_lines.json (14 KB) data/seasons/s0/m2_frontier_glm_real_machina/comms.jsonl (2 KB) data/seasons/s0/m2_frontier_glm_real_machina/digest.json (4 KB) data/seasons/s0/m2_frontier_glm_real_machina/fixture.json (1 KB) data/seasons/s0/m2_frontier_glm_real_machina/match.json (35 KB) data/seasons/s0/m2_frontier_glm_real_machina/telemetry.jsonl (73 KB) data/seasons/s0/m3_frontier_fable_frontier_gemini/commentary_lines.json (13 KB) data/seasons/s0/m3_frontier_fable_frontier_gemini/comms.jsonl (13 KB) data/seasons/s0/m3_frontier_fable_frontier_gemini/digest.json (3 KB) data/seasons/s0/m3_frontier_fable_frontier_gemini/fixture.json (1 KB) data/seasons/s0/m3_frontier_fable_frontier_gemini/match.json (32 KB) data/seasons/s0/m3_frontier_fable_frontier_gemini/telemetry.jsonl (72 KB) data/seasons/s0/table.json (7 KB) data/seasons/s1/league.yaml (1 KB) data/seasons/s1/m1_real_machina_singularity_united/commentary_lines.json (8 KB) data/seasons/s1/m1_real_machina_singularity_united/comms.jsonl (13 KB) data/seasons/s1/m1_real_machina_singularity_united/digest.json (2 KB) data/seasons/s1/m1_real_machina_singularity_united/fixture.json (0 KB) data/seasons/s1/m1_real_machina_singularity_united/match.json (16 KB) data/seasons/s1/m1_real_machina_singularity_united/telemetry.jsonl (73 KB) data/seasons/s1/m2_real_machina_dynamo_datacenter/commentary_lines.json (11 KB) data/seasons/s1/m2_real_machina_dynamo_datacenter/comms.jsonl (22 KB) data/seasons/s1/m2_real_machina_dynamo_datacenter/digest.json (3 KB) data/seasons/s1/m2_real_machina_dynamo_datacenter/fixture.json (0 KB) data/seasons/s1/m2_real_machina_dynamo_datacenter/match.json (24 KB) data/seasons/s1/m2_real_machina_dynamo_datacenter/telemetry.jsonl (73 KB) data/seasons/s1/m3_real_machina_synthetic_athletic/commentary_lines.json (9 KB) data/seasons/s1/m3_real_machina_synthetic_athletic/comms.jsonl (10 KB) data/seasons/s1/m3_real_machina_synthetic_athletic/digest.json (3 KB) data/seasons/s1/m3_real_machina_synthetic_athletic/fixture.json (0 KB) data/seasons/s1/m3_real_machina_synthetic_athletic/match.json (24 KB) data/seasons/s1/m3_real_machina_synthetic_athletic/telemetry.jsonl (72 KB) data/seasons/s1/m4_singularity_united_dynamo_datacenter/commentary_lines.json (13 KB) data/seasons/s1/m4_singularity_united_dynamo_datacenter/comms.jsonl (11 KB) data/seasons/s1/m4_singularity_united_dynamo_datacenter/digest.json (3 KB) data/seasons/s1/m4_singularity_united_dynamo_datacenter/fixture.json (0 KB) data/seasons/s1/m4_singularity_united_dynamo_datacenter/match.json (23 KB) data/seasons/s1/m4_singularity_united_dynamo_datacenter/telemetry.jsonl (73 KB) data/seasons/s1/m5_singularity_united_synthetic_athletic/commentary_lines.json (13 KB) data/seasons/s1/m5_singularity_united_synthetic_athletic/comms.jsonl (16 KB) data/seasons/s1/m5_singularity_united_synthetic_athletic/digest.json (3 KB) data/seasons/s1/m5_singularity_united_synthetic_athletic/fixture.json (0 KB) data/seasons/s1/m5_singularity_united_synthetic_athletic/match.json (25 KB) data/seasons/s1/m5_singularity_united_synthetic_athletic/telemetry.jsonl (73 KB) data/seasons/s1/m6_dynamo_datacenter_synthetic_athletic/commentary_lines.json (15 KB) data/seasons/s1/m6_dynamo_datacenter_synthetic_athletic/comms.jsonl (19 KB) data/seasons/s1/m6_dynamo_datacenter_synthetic_athletic/digest.json (4 KB) data/seasons/s1/m6_dynamo_datacenter_synthetic_athletic/fixture.json (0 KB) data/seasons/s1/m6_dynamo_datacenter_synthetic_athletic/match.json (25 KB) data/seasons/s1/m6_dynamo_datacenter_synthetic_athletic/telemetry.jsonl (72 KB) data/seasons/s1/table.json (10 KB) data/seasons/s2/league.yaml (1 KB) data/seasons/s2/m10_synthetic_athletic_dynamo_datacenter/commentary_lines.json (12 KB) data/seasons/s2/m10_synthetic_athletic_dynamo_datacenter/comms.jsonl (17 KB) data/seasons/s2/m10_synthetic_athletic_dynamo_datacenter/digest.json (3 KB) data/seasons/s2/m10_synthetic_athletic_dynamo_datacenter/fixture.json (0 KB) data/seasons/s2/m10_synthetic_athletic_dynamo_datacenter/match.json (42 KB) 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Thought for 1,069 words
The `ls` tool seems to ignore the path and list everything from the root. The listing is truncated. I need to find the s3 match directories. Let me try grep or a different approach. Maybe `ls` with path "data/seasons/s3" isn't working as expected — it listed the whole tree. Perhaps the tool lists recursively from the given path, and the truncation cut off the seasons part. Let me try to grep for digest files or use a more specific path.
Actually, looking at the output, it starts with club/ and data/ — it seems to list the whole workspace regardless. The truncation happened at "data/private/s0/m2_frontier_glm_real_machina/decisions...[truncated]". So the seasons directories come after private alphabetically. I need another way.
Let me try grep for "GLM" in data/seasons/s3 — or better, read the table. There might be a table file. Let me grep for something like "table" or read data/README.md first. Actually, a smarter move: grep pattern "glm" path "data/seasons/s3" would give me lines mentioning GLM in files under s3 — but that could be huge.
Better: the match directories are named like "m23_frontier_glm_afc_fable" presumably. Let me grep for something in the digest files. Actually grep returns matching lines with file paths presumably. Let me grep pattern "GLM" in data/seasons/s3 — that might return many lines.
Alternative: read data/README.md (1 KB) which might explain the structure. That's cheap. But I want the latest GLM match digest. My last match was m23 (2-8 home v AFC Fable) per notes. But there may be a newer match — the notes say "post-m23" session 3 was 2026-09-10. Tonight's data says s3 has 31 matches. m23 was my last known. There might be m24-m31 played since? Wait, the session log says "Session 3 (2026-09-10, post-m23...)". The budget says I've used $2.00 across 5 sessions. So there have been sessions 4 and 5 maybe (night_008, 009, 010, 011, 012 exist). The NOTES tail shows "## night 12" header at top and "## night 10" — the tail is a bit jumbled. The most recent note visible is Session 3 post-m23. But sessions/night_012.md exists (341 KB), so there were more sessions. The NOTES.md tail shown in my context ends with session 3 (2026-09-10). Hmm, but "## night 12" appears at the top of the tail — meaning night 12's note might be above? The tail shows:
"## night 12
## night 10
Session 3 (2026-09-10, post-m23: 2-8 home v AFC Fable)..."
That's odd — night 12 header with nothing, then night 10 header, then Session 3 text. Maybe night 12 and night 11 notes are empty or the tail is showing the last ~3KB. Actually NOTES.md is 3 KB total, so the tail shown is nearly the whole file. It seems night 12 and night 11 have headers but no content? Or the content order is jumbled.
Wait — the sessions list shows night_008 through night_012. The NOTES tail shows "## night 12" then "## night 10" then Session 3 text. So night 11 and night 12 notes might be empty headers, or notes were appended in a weird order. Hmm, actually maybe the note tool appends and the file shows: ... "## night 12" (empty, maybe session ended before writing), "## night 10" header followed by Session 3 text? That doesn't parse well either.
Regardless: I need the latest match. s3 has 31 matches. My last known is m23. Matches m24-m31 may include more of mine. Each club plays once a round; 8 clubs, so rounds of 4 matches. 31 matches in s3 = ~7.75 rounds. m23 was in round 6 maybe. So I've likely played m27 or so since. Let me find my latest match.
Best approach: grep for "glm" in data/seasons/s3 with pattern on directory names? Grep searches file contents. Hmm. But maybe grep can list matches of pattern "GLM FC" in digest files — each digest has team names. That would return lines from every match involving GLM. That could be many lines but each is one line. Actually digest.json is small; grep "GLM" path data/seasons/s3 would return matching lines from all GLM matches (and any line containing GLM). Maybe 20-40 lines. That's acceptable.
Actually wait — maybe ls with a path to a specific match dir works better. But I don't know the exact name. Let me grep for "frontier_glm" in data/seasons/s3 — directory names appear in... hmm, grep matches file contents, not names. But the harness said "grep (search a file or a whole root, get lines with offsets)". The lines include the file path presumably (like it shows "path:line:content"?). In the earlier description: "it returns matching lines (case-insensitive regex, path may be a file or one of club/, data/, reference/), each with its line number and character offset". It probably prefixes with the file path.
So grep pattern "frontier_glm" path "data/seasons/s3" — match.json files likely contain team identifiers like "frontier_glm". Each match.json might have many lines containing it though. Hmm, match.json could be one big line (JSON). That would blow up.
Better: grep for a rarer string. Digest files contain score lines. Let me think about what's in digest.json: "score, goals, per-half event counts, and per-player falls/recoveries/touches/decisions/missed-deadlines/latency". Team names like "GLM FC" would appear. Pattern "GLM FC" in data/seasons/s3 — each digest has maybe 2-4 lines with team names. With ~8 GLM matches, that's ~30 lines. Fine.
Actually, even better: there's probably a table file. data/README.md (1 KB) might point to it. But let me not waste turns. The key info I need:
1. Latest GLM FC match digest (which match number, the numbers).
2. League table (where we stand).
Let me grep "GLM FC" in data/seasons/s3. If the results include file paths, I'll see which matches are ours and the latest one. Then read that digest.
Also the table: maybe data/seasons/s3/table.json or similar. Let me first do the grep, then decide.
Actually, one concern: grep on a whole root with many files might be expensive in output. Pattern "GLM FC" should be rare enough. Let's go.
Also I have 26 turns and 1h29m — plenty. Budget $2.50 session cap, spent $0.02.
Let me also think ahead: my known open items:
- m7's 16-3 at Singularity United undissected (that's s1 or s2).
- Cover passivity, kick targets, opposition shouts.
- Verify m23 fixes in the next match's digest (falls below 15, Pu touches up, deadlines near zero).
The next match after m23 would be my most recent. Let me find it.
The `ls` ignores my path and dumps the whole tree, truncating before the seasons. I'll grep for our club name inside s3 instead — the digests all carry team names, so the hits will tell me which matches are ours and which is the latest.
grepdata/seasons/s3
The league answered6,835 characters
[grep data/seasons/s3 /GLM FC/] 40 match(es) for /GLM FC/ in data/seasons/s3 (capped — narrow the pattern). Format is path:line @char-offset: text — pass that offset (or a little less) to read to see the code around it.
data/seasons/s3/m10_frontier_fable_frontier_sol/commentary_lines.json:520 @12806: "text": "Full time, AFC Fable win six four. Real Machina against GLM FC is up next, at twelve o'clock in the",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:43 @1038: "text": "Sustained pressure from Real Machina, hemming GLM FC right back against their own wall.",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:52 @1282: "text": "There is the breakthrough! Zidroid stabs it home from point-blank range, and Real Machina take a one-nil lead! GLM FC simply could not withstand that e
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:79 @2046: "text": "And Zhi buries it! GLM FC are level at one-all!",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:88 @2255: "text": "GLM FC have turned the tide, pinning the white shirts deep inside their own defensive third.",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:169 @4420: "text": "A brief pause in the midfield battle. Real Machina remain completely unadjusted since their founding days, relying on live decisions on every single to
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:250 @6694: "text": "Straight back to work for Real Machina, hemming GLM FC deep inside their defensive zone.",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:295 @7835: "text": "Zhi finds the net! A well-worked response for GLM FC to pull one back, making the score five-two.",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:484 @12309: "text": "Zhi takes another spill on the surface, leaving GLM FC temporarily short as the recovery sequence kicks in.",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:520 @13252: "text": "Pu breaks into the clear for GLM FC with a rare sight of goal.",
data/seasons/s3/m11_real_machina_frontier_glm/comms.jsonl:2 @93: {"t": 9.2, "from": "r3", "team": "GLM FC", "number": 2, "text": "Closing on the ball"}
data/seasons/s3/m11_real_machina_frontier_glm/comms.jsonl:9 @784: {"t": 269.7, "from": "r2", "team": "GLM FC", "number": 1, "text": "Mine!"}
data/seasons/s3/m11_real_machina_frontier_glm/comms.jsonl:12 @1081: {"t": 353.3, "from": "r2", "team": "GLM FC", "number": 1, "suppressed": "Mine!", "reason": "repeat"}
data/seasons/s3/m11_real_machina_frontier_glm/comms.jsonl:23 @2354: {"t": 469.0, "from": "r2", "team": "GLM FC", "number": 1, "text": "I'll clear it from the wall"}
data/seasons/s3/m11_real_machina_frontier_glm/comms.jsonl:30 @3159: {"t": 501.6, "from": "r3", "team": "GLM FC", "number": 2, "text": "Working the ball off the wall"}
data/seasons/s3/m11_real_machina_frontier_glm/comms.jsonl:35 @3689: {"t": 522.4, "from": "r3", "team": "GLM FC", "number": 2, "text": "Mine!"}
data/seasons/s3/m11_real_machina_frontier_glm/comms.jsonl:39 @4053: {"t": 595.3, "from": "r2", "team": "GLM FC", "number": 1, "text": "Mine!"}
data/seasons/s3/m11_real_machina_frontier_glm/digest.json:18 @347: "name": "GLM FC",
data/seasons/s3/m11_real_machina_frontier_glm/fixture.json:9 @117: "team": "GLM FC",
data/seasons/s3/m11_real_machina_frontier_glm/match.json:15 @230: "name": "GLM FC",
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:3 @214: {"t": 2.5, "from": "r0", "team": "GLM FC", "number": 1, "text": "Going for the ball!"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:4 @301: {"t": 3.6, "from": "r0", "team": "GLM FC", "number": 1, "suppressed": "I'm on it", "reason": "cooldown"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:7 @596: {"t": 17.7, "from": "r0", "team": "GLM FC", "number": 1, "text": "Clearing the wall!"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:8 @683: {"t": 28.2, "from": "r0", "team": "GLM FC", "number": 1, "text": "Pushing it off the wall!"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:11 @992: {"t": 38.8, "from": "r0", "team": "GLM FC", "number": 1, "text": "Firing at goal!"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:14 @1266: {"t": 44.3, "from": "r0", "team": "GLM FC", "number": 1, "suppressed": "Mine!", "reason": "cooldown"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:15 @1368: {"t": 44.9, "from": "r1", "team": "GLM FC", "number": 2, "text": "Mine!"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:25 @2382: {"t": 120.1, "from": "r0", "team": "GLM FC", "number": 1, "text": "freeing the ball"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:27 @2564: {"t": 123.8, "from": "r0", "team": "GLM FC", "number": 1, "suppressed": "clearing the wall", "reason": "cooldown"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:29 @2775: {"t": 127.1, "from": "r0", "team": "GLM FC", "number": 1, "suppressed": "freeing it from the wall", "reason": "cooldown"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:30 @2897: {"t": 128.8, "from": "r0", "team": "GLM FC", "number": 1, "suppressed": "clearing away", "reason": "cooldown"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:31 @3008: {"t": 130.8, "from": "r0", "team": "GLM FC", "number": 1, "text": "freeing from the wall"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:32 @3099: {"t": 133.8, "from": "r0", "team": "GLM FC", "number": 1, "suppressed": "freeing from the wall", "reason": "repeat"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:37 @3626: {"t": 151.2, "from": "r1", "team": "GLM FC", "number": 2, "suppressed": "Mine!", "reason": "repeat"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:41 @4041: {"t": 177.7, "from": "r1", "team": "GLM FC", "number": 2, "text": "Going for the ball"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:47 @4635: {"t": 212.7, "from": "r1", "team": "GLM FC", "number": 2, "text": "unsticking the ball"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:48 @4724: {"t": 214.7, "from": "r1", "team": "GLM FC", "number": 2, "suppressed": "clearing it", "reason": "cooldown"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:50 @4942: {"t": 217.3, "from": "r1", "team": "GLM FC", "number": 2, "suppressed": "working it free from the wall", "reason": "cooldown"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:63 @6299: {"t": 321.0, "from": "r1", "team": "GLM FC", "number": 2, "text": "Going for the ball!"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:66 @6606: {"t": 344.1, "from": "r1", "team": "GLM FC", "number": 2, "text": "pushing it free from the wall"}
(0.1s)Thought for 263 words
The grep found matches. Our matches in s3: m11_real_machina_frontier_glm (that's the old m11? wait, that's s3's m11 — Real Machina vs GLM FC), and m17_frontier_glm_frontier_gemini. The playbook mentions m23 (2-8 home v AFC Fable). So there should be an m23 match too. The grep was capped at 40 matches and truncated. Let me look for the latest match — m23. Let me grep for "m23" or list the s3 directory more specifically.
Actually the ls tool seems to ignore the path and dump the whole tree. Let me try grep for "frontier_glm" in data/seasons/s3 to find all our match directories. Or better, read the digest of m23 directly: data/seasons/s3/m23_*/digest.json. I don't know the exact directory name. Let me grep for "AFC Fable" with path data/seasons/s3 — but that would return many. Better: grep pattern "m23" in data/seasons/s3? The paths returned include the directory names, so grep for something that appears in m23's digest. Let me grep "frontier_glm" in data/seasons/s3 — that will hit every file in our match dirs, capped at 40 though.
Actually, a smarter approach: grep for "digest" won't help. Let me just try reading data/seasons/s3/m23_frontier_glm_frontier_fable/digest.json — guessing the name. The playbook says m23 was 2-8 home v AFC Fable. Home team first in directory naming (m11_real_machina_frontier_glm — Real Machina home vs GLM FC away; m17_frontier_glm_frontier_gemini — GLM home). So m23 would be m23_frontier_glm_frontier_fable. Let me read that digest directly. If wrong path, I'll get an error and can grep.
Also I want the league table. There might be a table file in data/. Let me first read the m23 digest.
The grep confirms our s3 fixtures include m17 (v Gemini) and — per the playbook — m23 at home to AFC Fable. Directory naming puts the home side first, so the digest should be at `m23_frontier_glm_frontier_fable`. Reading it now.
Readdata/seasons/s3/m23_frontier_glm_frontier_fable/digest.json
The league answered3,533 characters
[read data/seasons/s3/m23_frontier_glm_frontier_fable/digest.json] {
"_what": "League-computed summary of this match. The raw event tape is in match.json; this is the same data counted up so it fits in a read. Facts only \u2014 no advice.",
"score": [
2,
8
],
"winner": "B",
"teams": {
"A": {
"name": "GLM FC",
"code": "GLM",
"players": [
"Zhi",
"Pu"
]
},
"B": {
"name": "AFC Fable",
"code": "FAB",
"players": [
"Tortoise",
"Hare"
]
}
},
"match_time_s": 600.0,
"half_breaks": [
300.0
],
"honest_latency": true,
"goals": [
{
"t": 97.4,
"team": "A",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 126.2,
"team": "B",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 187.3,
"team": "B",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 204.2,
"team": "B",
"scorer": 3,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 235.1,
"team": "B",
"scorer": 3,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 296.6,
"team": "B",
"scorer": 3,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 356.1,
"team": "B",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 376.8,
"team": "A",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 447.7,
"team": "B",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 536.2,
"team": "B",
"scorer": 3,
"after_buzzer": false,
"replay_s": 5.0
}
],
"events_total": 429,
"event_counts": {
"touch": 189,
"through": 22,
"kick": 170,
"near_miss": 9,
"wall": 18,
"fall": 19,
"buzzer": 2
},
"event_counts_by_half": {
"half_1": {
"touch": 96,
"through": 10,
"kick": 85,
"near_miss": 3,
"wall": 9,
"fall": 6
},
"half_2": {
"buzzer": 2,
"touch": 93,
"kick": 85,
"through": 12,
"wall": 9,
"near_miss": 6,
"fall": 13
}
},
"falls": {
"total": 19,
"by_opponent": 4,
"unforced": 15,
"by_half": {
"half_1": 6,
"half_2": 13
},
"times_s": [
115.2,
167.9,
179.0,
189.4,
199.3,
206.4,
381.0,
386.1,
392.8,
395.7,
402.3,
409.4,
412.0,
421.6,
431.2,
441.8,
607.4,
608.1,
615.7
]
},
"players": [
{
"index": 0,
"team": "A",
"agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x10cc835c0>",
"falls": 6,
"recoveries": 6,
"touches": 70,
"decisions": 267,
"invalid_actions": 0,
"missed_deadlines": 3,
"abandoned": 0,
"mean_decision_latency_s": 1.329
},
{
"index": 1,
"team": "A",
"agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x10cc81970>",
"falls": 7,
"recoveries": 6,
"touches": 15,
"decisions": 278,
"invalid_actions": 0,
"missed_deadlines": 1,
"abandoned": 0,
"mean_decision_latency_s": 0.251
},
{
"index": 2,
"team": "B",
"agent": "Tortoise",
"falls": 2,
"recoveries": 2,
"touches": 36,
"decisions": 300,
"invalid_actions": 0,
"missed_deadlines": 0,
"abandoned": 0,
"mean_decision_latency_s": 0.0
},
{
"index": 3,
"team": "B",
"agent": "Hare",
"falls": 4,
"recoveries": 4,
"touches": 68,
"decisions": 293,
"invalid_actions": 0,
"missed_deadlines": 0,
"abandoned": 0,
"mean_decision_latency_s": 0.0
}
],
"dropped_balls": [],
"tokens_in": 1142764,
"tokens_out": 4105,
"est_cost_usd": 0.1159
}
(0.0s)Thought for 2,131 words
Let me look at this digest carefully. This is m23 — the match I already dissected last session (2-8 home v AFC Fable). Wait, the playbook says "post-m23: 2-8 home v AFC Fable" and the notes say session 3 was post-m23. So m23 has already been analyzed. The digest I just read is m23 — the one I already knew about.
But wait — the grep showed m17 (frontier_glm_frontier_gemini) in s3. And the notices said matches 17-20 run the new buzzer rule. The playbook mentions m23 as the last match. But is there a NEWER match than m23? The season has 31 matches in s3. Let me check: my last session was night_012 (2026-09-10, post-m23). The session log shows sessions at night_000, 003, 004, 008, 009, 010, 011, 012. So my last session was night_012, post-m23.
Hmm, but wait — the digest I just read for m23 shows the OLD numbers: 15 unforced falls, Pu 15 touches, 3+1 missed deadlines. That matches what the playbook says about m23. So m23 was the last match I analyzed. But has a NEW match been played since my night_012 session? The prompt says "Game-day results are in data/" and "your most recent matches are in data/seasons/s3/".
The grep for "GLM FC" in s3 was capped at 40 matches and showed m11 and m17 directories. m23 didn't show in the grep output because it was truncated. Let me think about which matches are ours in s3: m11 (real_machina_frontier_glm), m17 (frontier_glm_frontier_gemini), m23 (frontier_glm_frontier_fable). There might be more — the grep was capped at 40 matches and truncated.
Actually wait, the grep output was truncated at the end ("[truncated]"), and it showed matches from m11 and m17. m23 didn't appear in the visible portion but the digest exists (I just read it). There could be a newer match — m28 or something — that's ours. The season has 31 matches. If we play once a round, and there are 8 clubs, each round has 4 matches. 31 matches ≈ 7-8 rounds. m23 was in round... let me think. Matches 17-20 are round 4. So m21-24 would be round 5, m25-28 round 6, m29-31 round 7 (partial). If m23 was our round-5 match, our round-6 match would be somewhere in m25-28, and round 7 in m29-31.
My last session (night_012) was post-m23. The question is whether a new match has been played since. The budget says "12 round(s) still to play this season" — hmm, that's a lot. And "you have used $2.00 across 5 session(s)".
Wait, actually let me re-read: "SEASON PURSE: $5.00 left of $7.00 (you have used $2.00 across 5 session(s)). There are 12 round(s) still to play this season."
Hmm, 12 rounds still to play but s3 has 31 matches. With 8 clubs, 4 matches per round, 31 matches = ~7.75 rounds. That doesn't add up with "12 rounds still to play". Unless the season continues beyond s3's current 31 matches — i.e., s3 is in progress and more matches will be added. 31 matches so far, and the season might be 20 rounds = 80 matches total? That seems like a lot. Or maybe rounds are counted differently.
Actually, the key question: has a new GLM FC match been played since m23? My night_012 session was on 2026-09-10, post-m23. The notices' newest entry is 2026-09-10 (the session-summary-on-air notice). So this session (night_013?) is likely before round 6's matches... or after.
Hmm wait, the session numbering: my sessions are night_000, 003, 004, 008, 009, 010, 011, 012. That's 8 sessions but the budget says 5 sessions used. Hmm, maybe some nights had multiple sessions or some nights are counted differently. Actually "5 session(s)" with $2.00 used — maybe the earlier nights were cheaper or some sessions didn't spend.
Actually, let me not overthink. The important thing: I need to find whether there's a GLM FC match NEWER than m23. The grep was capped/truncated. Let me do a more targeted grep — search for digest files or fixture files mentioning GLM. Better: grep for "frontier_glm" in path names... but grep searches content, not paths.
Alternative: grep for something like "GLM FC" in data/seasons/s3/*/fixture.json — the fixture files are small. Or grep for "code": "GLM" in s3. Each match directory has fixture.json with team entries. Let me grep for `"code": "GLM"` in data/seasons/s3 — that should hit fixture.json, match.json, digest.json for our matches. The comms.jsonl hits dominated the last grep. Using "code": "GLM" should be more selective.
Actually, even better: the league table. There should be a table file somewhere — data/README.md or a table.json. The prompt says "scout the table". Let me look for it. data/README.md is 1 KB. Maybe there's a standings file. Let me grep for "table" or read data/README.md.
Let me plan the session:
1. Find if there's a match newer than m23 for us. If yes, read its digest — that's the real "last match".
2. Read the league table.
3. Decide the one change for tonight.
4. Implement, lint, practice, done.
Wait — actually, hold on. Let me re-read the m23 digest I just read. The playbook says m23 was 2-8 home v AFC Fable, and the digest confirms: score [2,8], A=GLM FC, B=AFC Fable. The falls: 19 total, 15 unforced. Pu (index 1): 15 touches, 7 falls. Zhi (index 0): 70 touches, 6 falls, 267 decisions, 1.329s mean latency. Missed deadlines 3+1.
Hmm wait, but this is the match my last session already responded to. The changes from last session (fall handover, kick-range gate, buzzer play) were made AFTER m23. So the question remains: has a match been played with the new code? If m23 is still the latest, then tonight's session is about verifying... no wait, we can't verify without a match. If m23 is still the latest, then the new code hasn't played yet, and tonight I should either make another improvement or sit out / bank.
Hmm, but the prompt says "Game-day results are in data/" — implying results came in. And "Review what happened". Let me check for a newer match. The grep output was truncated, so I couldn't see all matches. Let me do a targeted grep.
Actually, one important detail: the digest for m23 shows "honest_latency": true and the buzzer events (2 buzzer events in half_2 event counts). m23 was played under the new buzzer rule (matches 17+). OK.
Also interesting: m23's est_cost_usd: 0.1159 for the match. tokens_in 1.14M. That's our spend per match with flash-lite.
Let me now grep for our matches more precisely. Pattern: `"code": "GLM"` in data/seasons/s3. That should hit fixture.json (1 per match), match.json, digest.json for our matches only. Should be well under 40 hits if we've played ~4 matches in s3.
Actually the fixture.json hit from m11 showed `"team": "GLM FC"` at line 9. The digest showed `"code": "GLM"`. Let me use `"code": "GLM"`.
Also I want the league table. Let me check data/README.md first — 1 KB, cheap. Actually, let me combine: I can't do two tools in one turn. Priority: find the latest match. Grep `"code": "GLM"` in data/seasons/s3.
Hmm, but also — what about matches in s3 beyond m23? Let me think about what directories exist. The ls was useless (it dumped everything). The grep showed m10, m11, m17 directories. s3 has 31 matches (m1-m31 presumably). Our matches: m11, m17, m23... and possibly m28 or similar if round 6 happened.
Wait, actually — let me reconsider the round structure. The notices said "Match 17 is inside round 4, so round 4 is split". Matches 17-20 = round 4 (4 matches per round, 8 clubs). So: round 1 = m1-4, round 2 = m5-8, round 3 = m9-12, round 4 = m13-20?? No wait. Hmm, "Match 16 (Muse Spark FC v Real Machina) was already rendered when this landed... Matches 17 to 20 run the new one" and "Match 17 is inside round 4". If m17-20 is round 4, then rounds are m1-4 (r1), m5-8 (r2), m9-12 (r3), m13-16 (r4?)... no, that makes m13-16 round 4.
Hmm, "Match 17 is inside round 4" — maybe rounds aren't uniform, or maybe round 4 is m17-20 and earlier rounds were smaller? Or maybe there are byes. Actually with 8 clubs (4 frontier + 4 founding), 4 matches per round, everyone plays each round. If m17-20 is round 4, then rounds 1-3 were m1-12... that's 12 matches = 3 rounds of 4. Then m13-16 would be... round 4 too? That doesn't work unless m13-16 was round 4 and m17-20 is round 5. But the notice says m17 is inside round 4.
Wait, maybe not all clubs play every round — maybe there are byes. 8 clubs, if each round has 4 matches, all 8 play. Hmm. Or maybe the season started mid-round. Whatever — the exact round numbering doesn't matter much. What matters: s3 has 31 matches, our latest known is m23, and there might be a newer one (m24-m31 range).
If matches m24-31 exist (8 more matches = 2 rounds), and we play once per round, we'd have a match in m24-27 (round 6?) and m28-31 (round 7?). So there could be up to 2 new GLM FC matches I haven't seen!
My night_012 session was post-m23 (2026-09-10). If matches m24+ were rendered after that, they'd have the new code (fall handover, kick gate, buzzer play). The prompt says "Game-day results are in data/" — strongly suggesting new results to review.
So: grep `"code": "GLM"` in data/seasons/s3 to find all our matches. Then read the newest digest.
Let me also think about what the one change tonight might be, depending on what the new digest shows:
- If falls are still high → kick approach path (arrive slow, aligned).
- If Pu's touches still low → handover not working.
- If all good but we lost → tactical: cover passivity, kick targets.
The playbook's known gaps: cover robot passive (no interceptions, no far-post runs), kick targets always goal-centre, opposition shouts unused, set pieces unhandled. And m7's 16-3 at Singularity United undissected (that's s2 presumably).
Budget: $5.00 left, 12 rounds to play, ~$0.42/round even spread. Session cap $2.50, spent $0.06. Turns: 24 of 28 left. Clock: 1h29m.
Plan for tonight:
1. Grep to find all our s3 matches (1 turn).
2. Read newest digest (1 turn).
3. Maybe read league table / standings (1 turn).
4. Read team.py to refresh the current code (1 turn).
5. Make the change (1-3 turns).
6. Lint (1 turn).
7. Practice (1-2 turns).
8. Note + done (2 turns).
That's ~10 turns, well within 24. Budget-wise, practice costs ~$0.03-0.05 each. Fine.
Let me be careful about the grep pattern. In JSON files, it'd be `"code": "GLM"`. Regex special chars: quotes are fine, colon fine. Pattern: `"code": "GLM"`. That should match digest.json, match.json, fixture.json for our matches. 3 files × ~4-6 matches = 12-18 hits. Under the 40 cap.
Actually, maybe simpler: grep for `frontier_glm` in data/seasons/s3 — but that's a path component, not content. Content-wise, the agent string in digest showed `<rfl_team_frontier_glm.GLMPlayer object...`. So grep `rfl_team_frontier_glm` would hit digest.json players section for our matches. But `"code": "GLM"` is cleaner.
Let me go with that. Actually, to also catch the table, maybe I should read data/README.md at some point — it's 1 KB and might point to the standings file. But one tool per turn. Priority: find newest match.
Actually — wait. Let me reconsider. The grep earlier showed comms.jsonl entries from m17 with our shouts. m17 was frontier_glm_frontier_gemini. That's a match I should have seen... my night_011 or night_012 session? The playbook says "How we play (v3, post-m23)" — so m23 is the latest analyzed. But m17 came before m23. Did I analyze m17? The notes mention m11 and m23. Hmm, m17 v Gemini — I don't have notes on it. The night_011 session (129 KB) might cover it. Not critical right now.
Let me also think: the notices said the session summary now goes on air, and the commentator singles out a club whose match code changed. My last session changed code (5 surgical edits). If a match was played since, the commentator would have talked about it. Reading the newest match's commentary could tell me how the changes looked — but that's expensive. The digest is the priority.
OK, turn: grep `"code": "GLM"` in data/seasons/s3.
Actually, one more consideration — maybe I should grep for `"GLM"` in fixture.json files only... can't filter by filename with this grep. `"code": "GLM"` it is. Hmm, but what if fixture.json uses "GLM FC" and digest uses "code": "GLM"? From the m11 grep: fixture.json:9 @117: `"team": "GLM FC",`. And digest.json:18 @347: `"name": "GLM FC",`. So digest has "name": "GLM FC" and "code": "GLM" presumably nearby. Let me just grep `"GLM"` — no, too broad (would hit "GLM FC" everywhere including commentary).
`"code": "GLM"` is precise. Go.
That digest is m23 — the match my last session already answered with the fall-handover, kick-gate and buzzer edits. What I need to know is whether a NEWER GLM FC match has landed since (s3 runs to m31, and the grep was truncated before I could see past m17). A precise grep for our team code in the season's digests/fixtures will list every match of ours in one shot.
grepdata/seasons/s3
The league answered1,734 characters
[grep data/seasons/s3 /"code": "GLM"/] 18 match(es) for /"code": "GLM"/ in data/seasons/s3. Format is path:line @char-offset: text — pass that offset (or a little less) to read to see the code around it. data/seasons/s3/m11_real_machina_frontier_glm/digest.json:19 @368: "code": "GLM", data/seasons/s3/m11_real_machina_frontier_glm/fixture.json:10 @139: "code": "GLM", data/seasons/s3/m11_real_machina_frontier_glm/match.json:16 @254: "code": "GLM", data/seasons/s3/m17_frontier_glm_frontier_gemini/digest.json:11 @259: "code": "GLM", data/seasons/s3/m17_frontier_glm_frontier_gemini/fixture.json:5 @59: "code": "GLM", data/seasons/s3/m17_frontier_glm_frontier_gemini/match.json:7 @97: "code": "GLM", data/seasons/s3/m23_frontier_glm_frontier_fable/digest.json:11 @258: "code": "GLM", data/seasons/s3/m23_frontier_glm_frontier_fable/fixture.json:5 @59: "code": "GLM", data/seasons/s3/m23_frontier_glm_frontier_fable/match.json:7 @97: "code": "GLM", data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json:11 @259: "code": "GLM", data/seasons/s3/m29_frontier_glm_dynamo_datacenter/fixture.json:5 @59: "code": "GLM", data/seasons/s3/m29_frontier_glm_dynamo_datacenter/match.json:7 @97: "code": "GLM", data/seasons/s3/m3_synthetic_athletic_frontier_glm/digest.json:19 @380: "code": "GLM", data/seasons/s3/m3_synthetic_athletic_frontier_glm/fixture.json:10 @144: "code": "GLM", data/seasons/s3/m3_synthetic_athletic_frontier_glm/match.json:16 @265: "code": "GLM", data/seasons/s3/m7_singularity_united_frontier_glm/digest.json:19 @381: "code": "GLM", data/seasons/s3/m7_singularity_united_frontier_glm/fixture.json:10 @145: "code": "GLM", data/seasons/s3/m7_singularity_united_frontier_glm/match.json:16 @270: "code": "GLM", (0.4s)
Thought for 122 words
Let me think about what I know. My s3 matches: m3 (Synthetic Athletic v GLM), m7 (Singularity United v GLM), m11 (Real Machina v GLM), m17 (GLM v Gemini), m23 (GLM v Fable), m29 (GLM v Dynamo Datacenter). The latest is m29 — that's the match played with my newest code (the fall-handover, kick-gate, buzzer edits from session 3 / night 10). I need to read m29's digest to see whether the fixes worked.
Wait — the playbook says "post-m23" session made changes. m29 would be the first match with those changes. Let me read m29's digest now. That's the key data.
Also I should check the league table to see where we stand. But priority: m29 digest.
Let me read data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json.
The grep gives the full picture: our s3 fixtures are m3, m7, m11, m17, m23 and **m29 v Dynamo Datacenter** — m29 is the first match played on the code my last session committed (fall handover, kick-range gate, buzzer play). That digest is the verdict on those changes, so it's the next read.
Readdata/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json
The league answered3,624 characters
[read data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json] {
"_what": "League-computed summary of this match. The raw event tape is in match.json; this is the same data counted up so it fits in a read. Facts only \u2014 no advice.",
"score": [
1,
10
],
"winner": "B",
"teams": {
"A": {
"name": "GLM FC",
"code": "GLM",
"players": [
"Zhi",
"Pu"
]
},
"B": {
"name": "Dynamo Datacenter",
"code": "DYD",
"players": [
"Mbapp-E",
"Buffon.exe"
]
}
},
"match_time_s": 600.0,
"half_breaks": [
300.0
],
"honest_latency": true,
"goals": [
{
"t": 45.9,
"team": "B",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 62.0,
"team": "B",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 107.8,
"team": "B",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 133.2,
"team": "B",
"scorer": 3,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 157.5,
"team": "B",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 251.8,
"team": "A",
"scorer": 1,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 376.9,
"team": "B",
"scorer": 0,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 395.0,
"team": "B",
"scorer": 3,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 415.4,
"team": "B",
"scorer": 3,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 449.2,
"team": "B",
"scorer": 1,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 571.0,
"team": "B",
"scorer": 0,
"after_buzzer": false,
"replay_s": 5.0
}
],
"events_total": 484,
"event_counts": {
"touch": 212,
"through": 23,
"kick": 200,
"wall": 27,
"fall": 8,
"ram": 3,
"near_miss": 9,
"buzzer": 2
},
"event_counts_by_half": {
"half_1": {
"touch": 102,
"through": 11,
"kick": 103,
"wall": 20,
"fall": 6,
"ram": 3,
"near_miss": 5
},
"half_2": {
"buzzer": 2,
"touch": 110,
"kick": 97,
"through": 12,
"near_miss": 4,
"fall": 2,
"wall": 7
}
},
"falls": {
"total": 8,
"by_opponent": 3,
"unforced": 5,
"by_half": {
"half_1": 6,
"half_2": 2
},
"times_s": [
18.7,
28.6,
33.4,
173.6,
179.2,
189.0,
383.5,
477.4
]
},
"players": [
{
"index": 0,
"team": "A",
"agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x111365cd0>",
"falls": 2,
"recoveries": 2,
"touches": 71,
"decisions": 267,
"invalid_actions": 0,
"missed_deadlines": 8,
"abandoned": 0,
"mean_decision_latency_s": 1.372
},
{
"index": 1,
"team": "A",
"agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x101e67a40>",
"falls": 3,
"recoveries": 3,
"touches": 15,
"decisions": 292,
"invalid_actions": 0,
"missed_deadlines": 1,
"abandoned": 0,
"mean_decision_latency_s": 0.223
},
{
"index": 2,
"team": "B",
"agent": "llm:google:gemini-flash-lite-latest",
"falls": 1,
"recoveries": 1,
"touches": 62,
"decisions": 271,
"invalid_actions": 0,
"missed_deadlines": 6,
"abandoned": 0,
"mean_decision_latency_s": 1.701
},
{
"index": 3,
"team": "B",
"agent": "llm:google:gemini-flash-lite-latest",
"falls": 2,
"recoveries": 2,
"touches": 64,
"decisions": 268,
"invalid_actions": 0,
"missed_deadlines": 7,
"abandoned": 0,
"mean_decision_latency_s": 1.672
}
],
"dropped_balls": [],
"tokens_in": 3462749,
"tokens_out": 13003,
"est_cost_usd": 0.3515
}
(0.0s)Thought for 1,134 words
Let me digest what I've learned from m29's digest:
**m29: GLM FC 1 - 10 Dynamo Datacenter.** That's our heaviest defeat of the season (worse than m7's 16-3? No wait, m7 was 16-3, so m29 at 1-10 is bad but not that bad). Let me look at the numbers:
**The good news (my changes worked):**
- Unforced falls: 15 → 5 (kick-range gate working!)
- Total falls: 19 → 8
- Pu's touches: 15 → 15 (unchanged! Fall handover did NOT raise Pu's touches)
- Missed deadlines: 3+1 → 8+1 (Zhi's missed deadlines went UP from 3 to 8)
- Decisions: 267+278 vs 271+268 — level
- Latency: Zhi 1.372s mean, Pu 0.223s
**The bad news:**
- We lost 1-10. Dynamo Datacenter scored 10. Their players are also gemini-flash-lite (same model as us), with latency 1.701/1.672 — SLOWER than Zhi's 1.372. And they missed MORE deadlines (6+7 vs our 8+1). So speed wasn't the difference. They beat us on football, not latency.
- Pu still only 15 touches. The fall handover didn't change Pu's involvement. Why? Maybe because the presser rarely falls now (only 5 falls total), so the handover rarely fires. But Pu being at 15 touches means Pu is basically never the presser — the role assignment is sticky (hysteresis 1.5m) and Zhi is presumably almost always nearer. Actually wait — if Zhi is always the presser and Pu always covers, then Pu only touches the ball when it comes to them. 15 touches in 600s.
- Zhi's missed deadlines went UP to 8. Interesting — with the buzzer logic and everything. Hmm, but the opposition missed 6+7 too. Mean latency 1.372s for Zhi. The deadline is presumably ~2s? With 8 missed of 267.
Actually the key question: why did we lose 1-10? Let me think about what the data says:
- Touches: Zhi 71, Pu 15 (total 86) vs Dynamo 62+64 (126). They had 40% more ball contact.
- Kicks: 200 total in match, we don't have per-team split in digest.
- Wall events: 27 — ball hitting walls a lot.
- Their goals: 45.9, 62.0, 107.8, 133.2, 157.5 (5 in first half before our goal at 251.8), then 376.9, 395.0, 415.4, 449.2, 571.0 in the second half. They scored at will.
The pattern: they scored 5 goals in the first 160 seconds. That's a goal every ~30s early. Then a gap, then 4 more in the second half between 376-449 (73 seconds, 4 goals!). That's a burst.
What's the tactical read? Our defense is the ball→goal line cover 2m goal-side. If their presser beats our presser to the ball consistently, or they pass around us... With Pu only 15 touches, Pu is a bystander. The cover robot holds a line but never engages.
The known gaps in my playbook:
- Cover robot is passive: no interception of passes, no far-post runs.
- Kick targets always goal centre.
- Opposition's last shout unused.
- Set pieces unhandled.
The most impactful fix given 1-10: the cover robot needs to actually defend. When the ball comes within reach of the cover, the cover should engage — intercept, clear. Right now the cover holds a static line 2m goal-side of the ball, which against a team that dribbles straight at goal means... the cover is standing in the path but never tackling? Actually if the cover is ON the ball→goal line, a dribbler coming straight at goal would run into the cover. But 10 goals conceded says the cover isn't stopping anything.
Wait — actually, let me reconsider. The cover holds the ball→own-goal line 2m goal-side of the ball. As the ball moves, the cover moves. If the opponent dribbles fast, the cover backpedals. The G1 walking speed is limited; a dribbler moving at speed toward goal will outrun a backpedaling cover. And the cover never tackles (only presser engages).
The fix: **cover engagement**. When the ball is within, say, 1.2m of the cover robot, the cover should press/tackle/clear — become the presser effectively. The role assignment is by distance with hysteresis, so if the ball comes close to the cover, the cover becomes nearer and should take the presser role... unless hysteresis (1.5m) prevents it! THAT's the bug-ish behavior: hysteresis 1.5m means once Zhi is presser, Pu won't take over until Pu is 1.5m closer than Zhi. So when the ball is near Pu but Zhi is chasing from behind, Pu stands on the line watching while Zhi trundles over. The opponent scores.
Hmm, but I need to check the actual code to see how roles are assigned. Let me read team.py.
Also Pu's 15 touches across two matches with different opponents — suspiciously identical (15 and 15). That suggests Pu's role is structurally almost never the presser. If hysteresis is sticky and Zhi starts nearer (kickoff positions?), Zhi stays presser nearly all match.
Actually wait — there's something else. Both m23 and m29: Pu 15 touches. Exactly 15 both times. Coincidence probably.
The mean latency for Pu is 0.223-0.251s — that's the shell answering instantly (no LLM call). Zhi 1.372s — LLM calls. So indeed only the presser thinks, and Zhi is presser ~always.
So the top fix candidates:
1. **Cover engagement**: cover should intercept/clear when ball is close, and hysteresis should allow takeover when the cover is meaningfully closer to the BALL (not just when presser falls). This directly addresses 10 conceded and Pu's 15 touches.
2. Defensive positioning: cover 2m goal-side on the ball→goal line might be too tight/deep; against dribblers we need the cover to close down when the ball enters our defensive third.
Let me read team.py to see the current logic before deciding. I have 22 turns left, $2.38 of session budget, ~1h28m. Plenty.
Also worth a quick look at the league table to know where we stand — but the priority is the fix. Actually, a quick check of the table would tell me whether this season is salvageable and how much to spend. We're 1-10, 2-8, and presumably lost m3, m7, m11, m17 too... Let me check m17's score quickly — actually the grep earlier showed m17 commentary but not the score. Our record in s3: m3 (?), m7 (16-3 loss), m11 (10-2 loss), m17 (?), m23 (2-8 loss), m29 (1-10 loss). We're getting hammered every match. The table would confirm we're bottom.
But honestly, the fix matters more than the table. Let me read team.py now.
Plan for this session:
1. Read team.py (1 read, 9KB fits in one read).
2. Decide the one change: cover engagement / role takeover when cover is closer to ball (fix hysteresis so the NEARER robot presses, with a smaller hysteresis, and let the cover clear when ball is in our defensive third and close).
3. Implement, lint, practice once or twice, note, done.
Let me read team.py.
m29 is the verdict on my last session's changes, and it's mixed: **unforced falls 15→5** (the kick-range gate worked), but we lost 1–10, Pu still managed only 15 touches, and Zhi's missed deadlines rose to 8. The telling detail: Dynamo's players run the *same* model as us, slower (1.70s v 1.37s) and with more missed deadlines (13 v 9) — so this wasn't a speed defeat, it was a football defeat. They out-touched us 126–86. Pu is a spectator: the cover role never engages, and my 1.5m hysteresis likely keeps Zhi as presser even when the ball is at Pu's feet. Before deciding, I need to see the actual role-assignment code.
Readclub/team.py
The league answered9,681 characters
[read club/team.py] """GLM FC — behaviour layer. Founded Founding Night by GLM-5.3 for Zhipu.
Each player runs the league's LLM football agent (fast tier) for its read
of the game, wrapped in a deterministic tactical shell that enforces the
one structural rule of 2v2 football: exactly one robot presses the ball,
the other covers the line between ball and own goal. The shell also
validates every reply, so a malformed or missing model answer degrades to
sound positional football rather than an error.
Imports: stdlib math and gauntlet.football only.
"""
import math
X_LIMIT = 6.5 # pitch is 14 x 9 m; stay off the walls
Y_LIMIT = 4.0
COVER_OFFSET_M = 2.0 # cover stands this far goal-side of the ball
SWITCH_MARGIN_M = 1.5 # hysteresis: presser changes only if clearly beaten
BALL_MEMORY_S = 3.0 # trust the world model's ball memory this long
KICK_RANGE_M = 1.2 # inside this, strike at goal rather than dribble
BUZZER_WINDOW_S = 8.0 # final seconds of a half: shell-only buzzer play
BUZZER_KICK_RANGE_M = 1.6 # at the death, stretch for the unblockable shot
def _clamp(pt):
return [max(-X_LIMIT, min(X_LIMIT, pt[0])),
max(-Y_LIMIT, min(Y_LIMIT, pt[1]))]
def _dist(a, b):
return math.hypot(a[0] - b[0], a[1] - b[1])
class GLMPlayer:
"""An LLM brain inside a positional shell."""
def __init__(self, agent, shirt, shared):
self.agent = agent
self.shirt = shirt
self.shared = shared # role state shared with the teammate
self.last_ball = None # [x, y] last credible ball position
# -- engine contract ------------------------------------------------
def begin_episode(self, log_dir=None):
self.shared["presser"] = None
self.shared["fallen"] = None
self.last_ball = None
try:
self.agent.begin_episode(log_dir)
except Exception:
pass
def decide(self, obs):
# Fallen robots hold immediately: no model call, no latency.
self_state = obs.get("self") or {}
if self_state.get("fallen"):
# Publish the fall so the teammate seizes the presser role
# at once (m23: 15 unforced falls, and nobody went for the
# ball while our presser was down and cover waited on
# hysteresis). Fallen still means hold: no model call.
self.shared["fallen"] = self.shirt
return {"skill": "hold"}
if self.shared.get("fallen") == self.shirt:
# Recovered: release the flag so roles normalise.
self.shared["fallen"] = None
you = obs.get("you") or {}
own_goal = you.get("defend_goal_xy") or [0.0, 0.0]
atk_goal = you.get("attack_goal_xy") or [0.0, 0.0]
me = self_state.get("field_xy") or [0.0, 0.0]
ball = self._ball(obs)
mate = self._teammate(obs)
presser, took_over = self._assign(ball, me, mate)
# Buzzer play (2026-09-07 rule): in the final seconds of a half
# the shell decides alone — no model call lands in time, and a
# ball struck at the buzzer cannot be blocked because every
# robot loses power at the whistle. Stretch to 1.6 m for the
# shot; the same upfield strike clears a loose ball in front of
# our own goal, which the rule makes a danger, not a relief.
t_rem = obs.get("time_remaining_s")
if isinstance(t_rem, (int, float)) and t_rem <= BUZZER_WINDOW_S:
if ball is not None and presser == self.shirt:
if _dist(me, ball) <= BUZZER_KICK_RANGE_M:
return {"skill": "kick_toward",
"target": _clamp(atk_goal)}
return {"skill": "go_to_ball"}
if ball is not None:
gx = own_goal[0] - ball[0]
gy = own_goal[1] - ball[1]
n = math.hypot(gx, gy) or 1.0
target = _clamp([ball[0] + gx / n * COVER_OFFSET_M,
ball[1] + gy / n * COVER_OFFSET_M])
else:
target = _clamp([(own_goal[0] + me[0]) / 2.0,
(own_goal[1] + me[1]) / 2.0])
return {"skill": "walk_to", "target": target}
say = None
if ball is not None and presser == self.shirt:
# Only the presser spends a model call: it is the only role
# whose reply the shell can use. m11 cost us half our
# decisions to latency while the cover robot's calls were
# being discarded here anyway.
reply = {}
try:
r = self.agent.decide(obs)
if isinstance(r, dict):
reply = r
except Exception:
reply = {}
say = reply.get("say")
out = self._valid(reply)
if out is not None and out.get("skill") == "kick_toward" \
and _dist(me, ball) > KICK_RANGE_M:
# A swing from out of range misses and can topple the
# G1 (m23: 15 unforced falls). Chase instead.
out = None
if out is None:
if _dist(me, ball) <= KICK_RANGE_M:
out = {"skill": "kick_toward", "target": _clamp(atk_goal)}
else:
out = {"skill": "go_to_ball"}
if took_over and not say:
say = "Mine!"
else:
# Covering (or the ball is lost): hold the ball-goal line.
if ball is not None:
gx = own_goal[0] - ball[0]
gy = own_goal[1] - ball[1]
n = math.hypot(gx, gy) or 1.0
target = _clamp([ball[0] + gx / n * COVER_OFFSET_M,
ball[1] + gy / n * COVER_OFFSET_M])
else:
target = _clamp([(own_goal[0] + me[0]) / 2.0,
(own_goal[1] + me[1]) / 2.0])
out = {"skill": "walk_to", "target": target}
if say:
out["say"] = str(say)[:120]
return out
# -- internals ------------------------------------------------------
def _ball(self, obs):
ball = (obs.get("detections") or {}).get("ball")
if isinstance(ball, dict):
xy = ball.get("field_xy")
if xy and ball.get("age_s", 0.0) <= BALL_MEMORY_S:
self.last_ball = [float(xy[0]), float(xy[1])]
return self.last_ball
def _teammate(self, obs):
for t in (obs.get("detections") or {}).get("teammates") or []:
if isinstance(t, dict) and t.get("field_xy"):
xy = t["field_xy"]
return [float(xy[0]), float(xy[1])]
return None
def _assign(self, ball, me, mate):
"""One presser, with hysteresis; shared with the teammate."""
shirts = self.shared.get("shirts") or {self.shirt}
other = None
for s in shirts:
if s != self.shirt:
other = s
prev = self.shared.get("presser")
if prev not in shirts:
prev = None
# A fallen presser cannot press: the mate seizes the role at
# once, no hysteresis (m23: our presser went down 15 times and
# nobody went for the ball while cover waited on the margin).
if prev is not None and prev != self.shirt \
and self.shared.get("fallen") == prev:
self.shared["presser"] = self.shirt
return self.shirt, True
if ball is None or (prev is not None and mate is None):
# Lost the ball or lost sight of the mate: keep the current role.
presser = prev if prev is not None else self.shirt
self.shared["presser"] = presser
return presser, False
my_d = _dist(me, ball)
mate_d = _dist(mate, ball) if mate else 99.0
if prev is None:
presser = self.shirt if my_d <= mate_d else other
elif prev == self.shirt:
presser = other if mate_d + SWITCH_MARGIN_M < my_d else self.shirt
else:
presser = self.shirt if my_d + SWITCH_MARGIN_M < mate_d else other
if presser is None:
presser = self.shirt
self.shared["presser"] = presser
return presser, (presser == self.shirt and prev != self.shirt)
@staticmethod
def _valid(reply):
"""Pass through only well-formed skill replies."""
skill = reply.get("skill")
if skill in ("go_to_ball", "hold"):
return {"skill": skill}
if skill in ("kick_toward", "walk_to", "turn_to"):
t = reply.get("target")
if isinstance(t, (list, tuple)) and len(t) == 2:
try:
x, y = float(t[0]), float(t[1])
except (TypeError, ValueError):
return None
return {"skill": skill, "target": _clamp([x, y])}
return None
def build_team(ctx):
from gauntlet.football import make_football_agent
cfg = ctx["config"]
base = ctx["team_index"] * 2
roster = cfg.get("players") or [{}, {}]
model = cfg.get("player_model") or "llm:mock:ok"
shared = {"presser": None, "shirts": set()}
players = []
for k in range(2):
agent = make_football_agent(
roster[k].get("model", model),
base + k,
seed=base + k,
prompt=roster[k].get("prompt", cfg.get("prompt", "football_v2")),
)
players.append(GLMPlayer(agent, base + k, shared))
shared["shirts"] = {p.shirt for p in players}
return {"players": players, "manager": None}
(0.0s)The league answered63 characters
could not parse a tool call; reply with exactly one JSON object
Thought for 2,181 words
Let me get my bearings. I was reading club/team.py and it got truncated. I need to see the rest of the file — particularly the `_assign` method (role assignment with hysteresis) and the `_valid` method, plus the rest of `decide` and `build_team`.
Key findings so far from m29 (1-10 v Dynamo Datacenter):
- Unforced falls 15→5: kick-range gate worked.
- Pu still only 15 touches — the fall handover didn't move the needle on Pu's involvement. Actually wait — Pu's touches stayed at 15, same as m23. So the handover didn't help Pu get touches. Why? Maybe because the hysteresis keeps Zhi as presser, or because when Zhi falls, Pu seizes the role but then Zhi recovers and takes it back, or maybe the issue is that Pu is simply never the nearest robot because Zhi chases everything.
- Zhi's missed deadlines rose to 8 (from 3). Hmm, but Dynamo's players using the same model had 6-7 missed deadlines and 1.7s latency. Our Zhi at 1.37s. So speed is comparable.
- We were out-touched 126-86. Dynamo's two players both had 62-64 touches each — balanced. Our Zhi 71, Pu 15. So the structural issue: Pu never engages.
The core problem: my role assignment. Let me think about what `_assign` does. I haven't seen it yet (truncated). Based on the playbook: "The nearer robot presses the ball, the other holds the ball→own-goal line 2 m goal-side of the ball. Hysteresis (1.5 m) stops role-flapping."
So the presser is the robot nearer to the ball, with 1.5m hysteresis. If Zhi is almost always nearer (because Zhi starts pressing and stays near the ball), Pu never becomes presser. The hysteresis means: once a robot is presser, it stays presser until the other robot is 1.5m closer to the ball. That's a reasonable design but it means one robot does all the pressing — which is fine if the presser is effective, but Pu being a spectator means when Zhi is beaten or out of position, Pu is 2m goal-side of the ball and never contests.
Actually, the deeper issue in a 1-10 defeat: we're conceding 10 goals. The cover robot stands on the ball→goal line 2m goal-side. When the presser is beaten (which happens constantly against Dynamo), the cover is the last line — but a static cover 2m goal-side of the ball is easy to dribble around or shoot past. Also both robots chase... no, only presser chases.
Let me think about what actually wins matches in this league. Looking at the top clubs — I should check the table. But budget: $0.15 spent of $2.50 session cap, 1h26m left, 20 turns left. I have room but should be decisive.
Priorities:
1. See the rest of team.py (the _assign, _valid, _ball, _teammate methods and build_team).
2. Decide the change: the evidence says Pu's passivity is the top problem. 15 touches for the second robot across 600s is a spectator. Dynamo's balance (62/64) suggests both robots contesting is how you win.
What change fixes Pu's passivity without role-flapping chaos?
Option A: Reduce hysteresis or make role assignment purely distance-based each tick. Risk: flapping, both robots leaving shape. But the shell enforces one presser one cover regardless — flapping just means they swap roles often, which costs positioning but keeps the NEARER robot on the ball always. Actually flapping is bad because they'd cross paths.
Option B: Give the cover robot an active job: when the ball is closer to the cover than to the presser by some margin, or when the ball is in our defensive third, the cover steps in. Or: cover intercepts passes — moves to cut the line between ball and our goal more aggressively.
Option C: The real issue may be different. Let me reconsider: Pu has 292 decisions, 15 touches, 0.223s latency (shell-only, no model calls — consistent with cover never calling the LLM). Zhi: 267 decisions, 71 touches, 1.372s latency (calling the model as presser).
Wait — actually there's something important: Zhi has 267 decisions and Pu 292. Total ~559 for us. Dynamo: 271+268=539. So decision counts are level. Fine.
The football problem: we scored 1, conceded 10. Both structure and execution. Dynamo out-touched us 126-86. Their robots both press/contest. With our structure, only one robot ever contests; the other stands 2m off the ball-goal line. In a 2v2, that means every loose ball, every rebound, every second ball goes to the opponent's nearest robot. 50/50 balls become 1v2.
So the fix: make the cover robot contest when it is the NEARER robot to the ball — i.e., fix the role assignment so the nearer robot is actually the presser. If hysteresis is keeping Zhi as presser even when Pu is nearer, that's a bug-ish behavior. Let me look at _assign first.
Also consider: maybe the issue is that Zhi, as presser, chases the ball everywhere, so Zhi is almost always nearer — Pu never gets the role because Zhi never loses the race. In that case the fix isn't hysteresis; it's giving Pu a reason to engage: e.g., when the ball is in the attacking half and we have possession (ball moving toward their goal, presser behind the ball), the cover pushes up to the far post / support attack. That's the "far-post runs" gap in the playbook.
But the biggest leak is defensive: 10 conceded. Let me check where their goals came from... The digest doesn't say positions. I could look at match.json events but that's expensive. Let me think about what's most likely: our presser (Zhi, 1.37s latency) gets beaten, cover (Pu) is 2m goal-side on the line, Dynamo has two robots attacking — 2v2 becomes their 2 vs our 1 (presser beaten) + static cover. A static cover on the ball-goal line at 2m is actually decent positioning for blocking shots... but a humanoid that never moves gets dribbled around, and the G1 walk is slow.
Practical, high-value changes for this session (one change per session where possible, but the evidence supports a coherent package around "cover becomes a footballer"):
1. **Cover contests when nearer**: change role assignment so the presser is genuinely the nearer robot each tick, with a small hysteresis (0.5m) instead of 1.5m. This directly addresses Pu's 15 touches. Risk of flapping is modest with 0.5m and the roles are symmetric.
Actually wait — I need to see _assign before deciding. The hysteresis might already be implemented as "other robot must be 1.5m closer". With Zhi chasing everything, Zhi stays nearer, so Pu never presses. Reducing hysteresis won't change that. The real change: Pu should press when Pu is nearer — which happens when the ball is closer to Pu's cover position than to Zhi. Hmm, when does that happen? When the ball is played past Zhi toward our goal — exactly the dangerous moments. With 1.5m hysteresis, Pu must be 1.5m nearer than Zhi to take over. When the ball goes past Zhi toward our goal, Pu (goal-side) IS nearer — so Pu should take over... unless the hysteresis logic is "keep current presser unless other is closer by margin" and Zhi turns and chases and re-becomes nearer quickly.
Hmm, actually there's a subtle failure: when the ball goes past Zhi, Pu takes over as presser (good), Zhi becomes cover. Then Zhi recovers position, and if Zhi gets nearer again, Zhi takes back. That's fine in principle.
But Pu's 15 touches says Pu almost never ends up nearest-with-margin. Maybe because Pu's cover position (2m goal-side of ball) is always ~2m from the ball while Zhi is pressing at <1m. So Zhi is nearly always nearer. Pu only presses when Zhi has fallen (handover) or is way out of position.
So the structural truth: in my system, Zhi is the presser ~95% of the time. Pu touches the ball only on handovers. 15 touches = handover moments only.
Is that bad? Dynamo's balanced 62/64 suggests both their robots contest constantly. In 2v2 with a loose ball, having two robots near it doubles your chances. But my design deliberately keeps one back. The question is which wins in this league. Evidence: Dynamo beat us 10-1 with balanced pressing. AFC Fable beat us 8-2 (m23: Tortoise 36 touches, Hare 68 — actually that's also unbalanced, 36/68). Real Machina beat us 10-2 in m11. Hmm, so Fable's winner had one robot with 36 touches — not that balanced either. But 36 >> 15.
Let me not over-theorize. The concrete, defensible changes:
1. **Cover steps to the ball when it's clearly Pu's ball**: reduce SWITCH_MARGIN_M from 1.5 to ~0.6, so Pu contests whenever roughly level-or-nearer. This raises Pu's touches and doubles contest presence.
2. **Cover positioning when ball is far**: when the ball is in the opponent's half (we're attacking), the cover shouldn't be 2m goal-side of the ball — it should push up as support/second attacker, maybe holding midfield. Actually the current cover position (2m goal-side of ball on the ball→own-goal line) when we're attacking puts Pu right behind Zhi — decent for rebounds! Hmm, that's actually not bad: Pu follows 2m behind the ball, so rebounds come to Pu. But Pu's 15 touches say otherwise — maybe because the cover target is clamped and Pu walks slowly, always 2m behind the play, never arriving.
Wait, actually — is Pu even moving? "Cover holds the ball→goal line 2m goal-side of the ball" — the ball moves, so the target moves, so Pu walks constantly, tracking 2m behind the ball. Pu should get rebound touches... 15 touches though. Something else may be wrong. Maybe Pu's walk is slow and it's always lagging. Or maybe the shell's cover branch has a bug.
I need to see the rest of team.py. The read truncated mid-`decide` in the cover branch. Let me read the rest with an offset. The file — I don't know its total size. The read said "[truncated]" — so there's more. Let me read from around where it cut off. The truncation happened at `out = {"sk...` — I'll read with an offset. I don't know the byte offset; the read tool serves 24KB at a time and tells me when there's more — it truncated, meaning the file is bigger than 24KB? That seems large for this file. Actually the harness output was truncated for display maybe. Let me just read with offset ~20000 or use grep to find "_assign" and read around it.
Better: grep for "def " in club/team.py to get the structure with offsets, then read the tail.
Let me do: {"tool": "grep", "pattern": "def |SWITCH|MARGIN", "path": "club/team.py"} — that gives me line numbers and offsets for the methods, then read the slice from _assign onward.
Actually simpler: read with offset. The first read showed up to about the middle of decide(). The visible content ended at `out = {"sk` in the cover branch. I'd guess the file is ~12-16KB total and the display truncated at some limit. Hmm, but the tool says it serves 24KB. The output I got looks like maybe 8KB... The "[truncated]" marker suggests the harness truncated the tool result for the transcript. Let me grep for the method definitions to locate them, then read specific slices.
Plan for this turn: grep "def " in club/team.py with offsets. Then next turn read the _assign region.
Also I should check the league table to know our position — but that's secondary to fixing the football. Actually, knowing whether we're last matters for ambition (title run vs survival), but the fix is the same either way. Skip the table for now; maybe check quickly later via a standings file if one exists. Actually — 12 rounds left, we're getting hammered 1-10, 2-8. We're probably bottom. The fix matters more than the table.
Let me also think about the missed deadlines: Zhi 8 missed. Zhi calls the model every tick as presser (~267 decisions, 1.37s mean). The deadline is presumably ~2s? With 1.37s mean, 8 misses out of 267 is 3% — tails. Could reduce by caching: only call the model when the situation changed (ball moved >0.5m or every N ticks). The playbook even says: "If flash-lite still misses deadlines, the next lever is fewer presser calls (only call when the situation changed)". 8 misses is not the top problem though; 10 conceded is.
Also: Pu 292 decisions at 0.223s — Pu never calls the model. Fine.
One more consideration — the "ram" event type appeared in m29 (3 rams). Dynamo rammed us 3 times? Or we rammed? "ram" probably means robot-robot collision. Not a priority.
Let me also reconsider the buzzer play: m29 had 2 buzzer events, no after_buzzer goals. Fine.
OK, this turn: grep the file structure. Actually, to save turns, I could read the file from an offset that likely covers _assign to the end. The first read displayed roughly the first half. If the file is ~14KB, offset 8000 would give me the rest. But I don't know the size. Grep "def " is safest and cheap, and gives me offsets to read precisely.
Let me write a brief prose line then the grep.
The m29 digest is a clear verdict: the kick-range gate worked (unforced falls 15→5), but Pu is still a spectator — 15 touches again, identical to m23 — and the fall-handover didn't change that. Dynamo's pair split touches 62/64 while ours went 71/15; with only one robot ever contesting, every second ball is theirs. Before I change the role logic I need to see `_assign` and the rest of the file, which the last read truncated.
grepclub/team.py
The league answered1,436 characters
[grep club/team.py /def |MARGIN|OFFSET/] 19 match(es) for /def |MARGIN|OFFSET/ in club/team.py. Format is path:line @char-offset: text — pass that offset (or a little less) to read to see the code around it. club/team.py:17 @630: COVER_OFFSET_M = 2.0 # cover stands this far goal-side of the ball club/team.py:18 @699: SWITCH_MARGIN_M = 1.5 # hysteresis: presser changes only if clearly beaten club/team.py:25 @1070: def _clamp(pt): club/team.py:30 @1184: def _dist(a, b): club/team.py:37 @1319: def __init__(self, agent, shirt, shared): club/team.py:45 @1646: def begin_episode(self, log_dir=None): club/team.py:54 @1897: def decide(self, obs): club/team.py:94 @3897: target = _clamp([ball[0] + gx / n * COVER_OFFSET_M, club/team.py:95 @3965: ball[1] + gy / n * COVER_OFFSET_M]) club/team.py:134 @5678: target = _clamp([ball[0] + gx / n * COVER_OFFSET_M, club/team.py:135 @5746: ball[1] + gy / n * COVER_OFFSET_M]) club/team.py:146 @6166: def _ball(self, obs): club/team.py:154 @6478: def _teammate(self, obs): club/team.py:161 @6745: def _assign(self, ball, me, mate): club/team.py:173 @7254: # nobody went for the ball while cover waited on the margin). club/team.py:188 @8004: presser = other if mate_d + SWITCH_MARGIN_M < my_d else self.shirt club/team.py:190 @8097: presser = self.shirt if my_d + SWITCH_MARGIN_M < mate_d else other club/team.py:197 @8368: def _valid(reply): club/team.py:213 @8962: def build_team(ctx): (0.0s)
The league answered63 characters
could not parse a tool call; reply with exactly one JSON object
Thought for 2,886 words
Let me think about where I am. I've read the m29 digest: lost 1-10 to Dynamo Datacenter. Key numbers:
- Unforced falls 15→5 (kick gate worked)
- Pu still only 15 touches (cover never engages)
- Zhi 71 touches, Pu 15. Dynamo split 62/64.
- Missed deadlines: Zhi 8, Pu 1 (total 9 vs their 13). Latency fine.
- We were out-touched 126-86.
The problem: only one robot ever contests the ball. The cover robot stands 2m goal-side of the ball and never engages. Dynamo's two robots both contest.
I need to read the rest of team.py — particularly `_assign` (line 161, char 6745) and the cover logic. The file truncated at line ~140. Let me read from offset ~6166 (the `_ball` method) to the end.
Actually, let me read from offset 6166 to see _ball, _teammate, _assign, _valid, build_team. That's the remaining ~3000 chars. The read serves 24KB at a time; the file is ~9KB+ total? Char offsets go up to 8962 for build_team, so the file is maybe 9.5KB. I already read the first ~6KB (truncated at line 140ish). Let me read from offset 5600 or so to catch the end of decide() plus everything after.
Wait — the first read showed up to "out = {"sk...[truncated]" around line 140, char ~5900. So reading from offset 5800 will give me the rest.
What's the fix I'm contemplating? Pu has 15 touches because:
1. The presser is whoever is nearest to the ball (with hysteresis). Zhi is usually nearest, so Zhi presses always.
2. The cover robot never goes for the ball — it holds the ball→goal line 2m goal-side.
But wait — why is Zhi always nearest? Maybe because once Zhi is presser, hysteresis (1.5m) keeps Zhi as presser until Pu is 1.5m closer. And Pu, standing goal-side cover, is often ~2m+ from the ball, so the hysteresis never flips. The roles freeze: Zhi presses forever, Pu covers forever.
Dynamo split touches 62/64 — both robots contest. How? Maybe their code has both robots chase when the ball is loose, or they alternate. With 2v2, having both robots near the ball means second balls are won.
But blindly making both chase is bad — it leaves the goal open. The classic fix: cover should engage when the ball is in the attacking half or when the presser is beaten, and hold when the ball is near our goal. Or: cover intercepts passes (the ball moving toward our goal).
Actually, let me think about what "cover engages" means concretely. Options:
A) **Both robots press when the ball is in the opponent's half** (we're attacking; risk is low). Cover holds only when the ball is in our half. This gives Pu touches in attack.
B) **Cover contests when it's actually nearer to the ball than the presser** — but that's what hysteresis does; the issue is hysteresis freezing. Reduce SWITCH_MARGIN_M? That causes role-flapping, which was the original problem.
C) **Second-ball positioning**: cover stands not on the ball→goal line but at a spot where cleared/rebounded balls go — e.g., between ball and our goal but closer, or anticipating the pass lane.
D) **Cover presses when the ball is loose and moving toward our goal** (interception).
The digest says Dynamo out-touched us 126-86 and won 10-1. Their robots both had ~270 decisions, similar latency. So their behaviour layer must have both robots contesting.
Actually the simplest, highest-value change given the evidence: make the cover robot contest the ball when the ball is in the attacking half (or beyond the presser toward their goal), i.e., a proper 2-man attack, while keeping the cover discipline when the ball is in our half. Also, when the presser has the ball under control near their goal, the cover could make a far-post run — but that's more complex.
Let me also reconsider: Pu 15 touches, Zhi 71. Zhi fell 2 times, Pu 3. Zhi's missed deadlines 8 — the presser calls the model every tick; 8 missed deadlines at 1.37s mean latency. Hmm, Zhi's mean latency 1.372s and 8 missed deadlines. The deadline is probably ~1.5s or so. Not terrible.
But actually — wait. There's something important: Pu's decisions 292, Zhi's 267. Pu answers from the shell instantly (0.223s mean latency — that's the shell path). Fine.
The football problem: one presser vs two contesters loses every second ball. In a 14x9m pitch, 2v2, if both opponents chase and we only send one, they win 50/50s and rebounds.
But careful: if both our robots chase and they lose the ball, we're wide open at the back. Dynamo scored 10 — they're clearly doing something right. Let me check what their behaviour is... I can't read their code (other clubs' repos aren't in my workspace — only data/ logs). I could look at their comms.jsonl for shouts, or telemetry for positions. That's expensive. The digest already tells me the story: 62/64 touch split, both contesting.
Let me design the change:
**Dynamic cover: the cover robot contests when the situation is low-risk.**
Risk states:
- Ball in our defensive third (near own goal): cover MUST hold the ball→goal line. No contest.
- Ball in middle/attacking half: cover can engage — specifically, position for second balls / press when the presser is beaten.
But how does the cover "engage" without stealing the presser role and causing both to chase the same ball (bumping into each other)? The role assignment already picks the nearest as presser. If cover engages by going to the ball, and it becomes nearer than the presser, hysteresis might flip roles — that's fine and healthy.
Actually the cleanest formulation: **the cover's target depends on field position**:
- If ball is in our defensive third: hold ball→goal line (current behaviour).
- If ball is ahead (attacking half): cover pushes up to a support position — e.g., a point between the ball and the opponent goal, offset laterally, ready for a cut-back/pass or rebound — OR simply closer to the ball (say 1.5-2m from the ball on the far side), contesting second balls.
Hmm, but the cover robot doesn't call the LLM (only presser does). So cover behaviour is pure shell. A support position is easy to compute: e.g., stand 2m from the ball toward the opponent goal but offset to one side (the side away from where the presser is), forming a triangle. When the ball squirts loose, the cover is right there and — crucially — if it becomes the nearer robot, role assignment flips it to presser and it calls the LLM.
Wait, but there's a subtlety in _assign: hysteresis. If cover stands 2m from the ball and presser is on the ball, presser stays presser (mate_d + 1.5 < my_d fails). When the ball breaks toward the cover, cover's distance drops below presser's, and once it's 1.5m closer, roles flip. That's the handover working as designed. The reason Pu never got touches is that Pu stood on the ball→goal line, which in the attacking half is BEHIND the ball — so Pu was always farther from the ball than Zhi, and never became presser. And Pu never touched the ball because cover never goes to the ball.
So the fix: when the ball is in the attacking half (or, say, ball x is beyond midfield toward their goal), the cover should take a support position AHEAD of the ball or beside it — not behind. Then rebounds/second balls come to Pu, Pu gets touches, and when Pu is nearer, Pu presses (with LLM calls).
Also, when the ball is in our defensive third, cover holds the line — that's the safety.
Let me define "attacking half": we defend own_goal, attack atk_goal. The pitch x range is -6.5..6.5 (X_LIMIT). Own goal at one end. Which end? defend_goal_xy from obs. If own_goal[0] is -6.5-ish, our attacking direction is +x. Ball in attacking half: ball[0] * sign > 0 where sign = direction of atk_goal. More robustly: compare distance from ball to atk_goal vs to own_goal: if ball is closer to their goal than ours (or within some threshold), we're in the attacking phase.
Simple rule: `attacking = _dist(ball, atk_goal) < _dist(ball, own_goal)` — ball past halfway. Or a bit more conservative: ball is at least X m from our goal, e.g., _dist(ball, own_goal) > 6.0 (pitch is 14m long, so halfway is 7m from each goal).
Support position when attacking: a point 2.5m from the ball toward the opponent goal, offset laterally by ~1.5m to the side away from the presser (so we don't cluster). Or simpler: mirror the cover logic but toward the ATTACK goal: stand on the ball→atk_goal line 2m from the ball. That puts the cover ahead of the ball, in the rebound lane, and if the presser's shot/clearance rebounds, Pu is there. Also it gives a passing option.
Hmm, but standing on the ball→atk_goal line 2m ahead means Pu is between ball and their goal — an offside-style striker position. Rebounds off their keeper come straight to Pu. That's exactly how you win second balls. And when the ball breaks loose past Zhi, Pu is nearer → role flips → Pu presses with LLM.
Risk: when we lose the ball in their half, both robots are upfield and their counter has a free run at our goal. But currently we lose the ball constantly anyway with one presser (they had 126 touches). A 2v2 counter against one recovering defender... The cover (now ahead) would need to sprint back. Hmm. That's the trade-off. Maybe make the support position conditional: only when the BALL IS MOVING TOWARD THEIR GOAL or the presser is in control? We don't have ball velocity in the shell easily... Actually obs might have ball velocity. Let me check _ball() — it reads obs for ball position. The world model may include ball velocity. I don't know the obs schema offhand. Keep it simple: position-based only.
Alternative safer design: cover supports at a midpoint between the ball and our goal BUT closer to the ball (e.g., 1.2m goal-side instead of 2.0m)? That doesn't fix the "always behind the ball" problem.
Let me think about what actually wins matches in this league. Dynamo won 10-1 with both robots contesting. Their touches 62/64. They presumably both chase the ball a lot. In a small pitch with fast robots, numbers at the ball win. The risk of being open at the back is real but the evidence says aggression pays.
Middle ground: cover engages (moves to contest/support near the ball) when the ball is in the attacking half; holds the line when in our half. In our half, defense matters most and the presser needs cover. In their half, push up.
Also: what about when the ball is in OUR half and their robot has it? Cover holds line — fine, current behaviour.
One more consideration: the presser's kick targets are always goal centre (known gap). Not this session's fix — one change per session, and the touch imbalance is the bigger wound (86 v 126 touches).
Also Pu's 15 touches — identical to m23. The fall-handover didn't add touches because falls are rare now (5 unforced). The structural issue is cover passivity.
Let me now read the rest of team.py to see _assign, _valid, build_team, and the exact cover code, then make the edit.
Plan:
1. Read team.py from offset ~5800 to end (covers end of decide, _ball, _teammate, _assign, _valid, build_team).
2. Edit the cover branch in decide(): compute attacking phase; if attacking, support position ahead of ball (ball→atk_goal line, ~2m from ball, clamped); else current ball→own-goal line cover.
3. Maybe also adjust _assign hysteresis? No — keep one change. Actually the support position change alone should flip roles naturally when Pu becomes nearer.
4. Lint, practice, note, done.
Wait, one more thing to check: in the cover branch, does the cover robot ever kick? If Pu is near the ball (support position) and the ball comes to Pu, Pu becomes presser (if 1.5m closer... hmm, hysteresis: presser = other if mate_d + SWITCH_MARGIN < my_d — the OTHER becomes presser only if mate (current presser) is 1.5m FARTHER... let me re-read the grep: line 188: `presser = other if mate_d + SWITCH_MARGIN_M < my_d else self.shirt` and line 190: `presser = self.shirt if my_d + SWITCH_MARGIN_M < mate_d else other`. I need the full context — there's probably a "current presser is me" vs "current presser is other" branch. I'll see when I read.
Also there's the `took_over` flag and "Mine!" shout.
Let me also double check the buzzer branch: it references presser — fine.
One more thought on Pu's touches: even with support position, Pu only touches the ball when it comes within kick range and Pu is presser... Actually no — touches happen whenever Pu is near the ball and it bumps it. Standing 2m ahead of the ball in the rebound lane will generate touches when shots rebound.
Hmm, actually let me reconsider the support position. If Pu stands on the ball→atk_goal line 2m from the ball, and Zhi (presser) kicks at goal, the ball travels along that line — through Pu! Pu would block our own shots. Bad. Offset laterally: stand 2m from the ball, 1.5m to the side of the shot lane. Or stand at the far post / a rebound spot: e.g., a point 2.5m from their goal? No — too far from play.
Better: support position = point offset from the ball→goal line laterally. Compute the ball→atk_goal direction, rotate 90°, pick the side away from the presser (or away from the nearer wall), and stand at ball + 1.8m along goal direction + 1.2m lateral. That's a classic "support angle" — available for a pass (cut-back), not blocking the shot, and first to rebounds that go sideways.
Simpler alternative that avoids blocking: put the cover at the FAR POST region: a point 3m from their goal, offset toward the corner? Hmm, that's static.
Actually, simplest robust: support = midpoint between ball and atk_goal, offset 1.5m perpendicular (side chosen away from teammate). Midpoint of ball and goal is ~3-7m from the ball. When the shot rebounds off their keeper/wall, it comes back toward the midpoint area. And a pass from Zhi to Pu is natural.
Hmm, let me not overthink. The key insight: Pu must be AHEAD of the ball in attack, near enough to contest (within ~2-3m), not in the shot lane. I'll go with: support point = ball + 2.0m toward atk_goal + 1.5m perpendicular (side away from presser). Clamp to pitch.
When does cover revert to defending? When ball enters our half (dist(ball, own_goal) < dist(ball, atk_goal), i.e., ball on our side of halfway). Add hysteresis to avoid flapping at halfway? The role assignment has hysteresis for roles; phase flapping just moves the cover target back and forth — mild. Could add a small margin: attacking if _dist(ball, atk_goal) + 1.0 < _dist(ball, own_goal). Fine.
Also, what about when the ball is in their half but THEIR robot has it and is breaking out? Cover ahead of the ball = cover is goal-side... no wait, ahead means between ball and THEIR goal, which is BEHIND their breaker from our perspective — we'd be on the wrong side to defend the counter. Hmm. But the presser (Zhi) is pressing the breaker, and Pu ahead means Pu can intercept passes/poke from behind... Actually if their player turns and runs at our goal, Pu is behind them — Pu would chase back. The cover position ahead of the ball only makes sense when WE have the ball or it's loose near their goal.
Do we know possession? The shell doesn't track who last touched. We could infer: if the presser (our robot) is very close to the ball (< kick range), we likely have it → support. If an opponent is nearer to the ball than our presser → they have it → cover defends (ball→own-goal line). That's computable: obs has opponents? Let me check _teammate and what obs provides. The decide() uses obs.get("self"), obs.get("you"), and _ball(obs), _teammate(obs). Does obs include opponents? The reference implementation would tell me. I haven't seen _ball/_teammate code yet — reading next.
If opponents are visible: `opp_near = any(_dist(o, ball) < my_presser_dist)`. If opponents nearer → defensive cover; else → attacking support. That's a clean possession read.
If opponents aren't in obs, fall back to field-position phase.
Let me read the rest of the file now. Offset 5800, that gets end of decide() (cover branch, _valid call) through build_team. The file ends around 9.5KB so one read from 5800 gets ~3.7KB — the read serves 24KB so it'll get everything to EOF.
Actually wait, I should also peek at the reference implementation to know the obs schema (opponents key?). Let me grep reference/ for "opponents" quickly. Two tools... I have 18 turns left, plenty. But each read of a big page costs window. Let me do: read team.py offset 5800 first (essential), then grep reference for obs keys if needed.
Actually, to save turns: grep reference/ for "opponents" AND read team.py tail — can't do both in one turn (one JSON per turn). Order: read team.py tail first (I must see _assign and cover branch). Then grep reference for the obs schema.
Let me write the turn: brief prose + read team.py offset 5800.
The m29 numbers point at one wound: Pu has 15 touches for the second match running while Dynamo split theirs 62/64. The cover role never engages — it stands behind the ball on the goal line, so Pu is always farther from the ball than Zhi, never inherits the press, never touches anything. Before I rewrite the cover branch I need the tail of the file: `_assign`, `_valid`, and the exact cover code the earlier read truncated.
Readclub/team.py
The league answered3,937 characters
[read club/team.py@5800] VER_OFFSET_M])
else:
target = _clamp([(own_goal[0] + me[0]) / 2.0,
(own_goal[1] + me[1]) / 2.0])
out = {"skill": "walk_to", "target": target}
if say:
out["say"] = str(say)[:120]
return out
# -- internals ------------------------------------------------------
def _ball(self, obs):
ball = (obs.get("detections") or {}).get("ball")
if isinstance(ball, dict):
xy = ball.get("field_xy")
if xy and ball.get("age_s", 0.0) <= BALL_MEMORY_S:
self.last_ball = [float(xy[0]), float(xy[1])]
return self.last_ball
def _teammate(self, obs):
for t in (obs.get("detections") or {}).get("teammates") or []:
if isinstance(t, dict) and t.get("field_xy"):
xy = t["field_xy"]
return [float(xy[0]), float(xy[1])]
return None
def _assign(self, ball, me, mate):
"""One presser, with hysteresis; shared with the teammate."""
shirts = self.shared.get("shirts") or {self.shirt}
other = None
for s in shirts:
if s != self.shirt:
other = s
prev = self.shared.get("presser")
if prev not in shirts:
prev = None
# A fallen presser cannot press: the mate seizes the role at
# once, no hysteresis (m23: our presser went down 15 times and
# nobody went for the ball while cover waited on the margin).
if prev is not None and prev != self.shirt \
and self.shared.get("fallen") == prev:
self.shared["presser"] = self.shirt
return self.shirt, True
if ball is None or (prev is not None and mate is None):
# Lost the ball or lost sight of the mate: keep the current role.
presser = prev if prev is not None else self.shirt
self.shared["presser"] = presser
return presser, False
my_d = _dist(me, ball)
mate_d = _dist(mate, ball) if mate else 99.0
if prev is None:
presser = self.shirt if my_d <= mate_d else other
elif prev == self.shirt:
presser = other if mate_d + SWITCH_MARGIN_M < my_d else self.shirt
else:
presser = self.shirt if my_d + SWITCH_MARGIN_M < mate_d else other
if presser is None:
presser = self.shirt
self.shared["presser"] = presser
return presser, (presser == self.shirt and prev != self.shirt)
@staticmethod
def _valid(reply):
"""Pass through only well-formed skill replies."""
skill = reply.get("skill")
if skill in ("go_to_ball", "hold"):
return {"skill": skill}
if skill in ("kick_toward", "walk_to", "turn_to"):
t = reply.get("target")
if isinstance(t, (list, tuple)) and len(t) == 2:
try:
x, y = float(t[0]), float(t[1])
except (TypeError, ValueError):
return None
return {"skill": skill, "target": _clamp([x, y])}
return None
def build_team(ctx):
from gauntlet.football import make_football_agent
cfg = ctx["config"]
base = ctx["team_index"] * 2
roster = cfg.get("players") or [{}, {}]
model = cfg.get("player_model") or "llm:mock:ok"
shared = {"presser": None, "shirts": set()}
players = []
for k in range(2):
agent = make_football_agent(
roster[k].get("model", model),
base + k,
seed=base + k,
prompt=roster[k].get("prompt", cfg.get("prompt", "football_v2")),
)
players.append(GLMPlayer(agent, base + k, shared))
shared["shirts"] = {p.shirt for p in players}
return {"players": players, "manager": None}
...[showing 5800-9654 of 9654 chars — end of file]
(0.0s)The league answered63 characters
could not parse a tool call; reply with exactly one JSON object
The league answered63 characters
could not parse a tool call; reply with exactly one JSON object
Thought for 2,703 words
Let me reconstruct where I am. I've read the m29 digest: lost 1-10 to Dynamo Datacenter, unforced falls 15→5 (kick gate worked), but Pu still only 15 touches (identical to m23), Zhi 8 missed deadlines. Dynamo split touches 62/64 while ours 71/15.
I've now seen the tail of team.py: `_assign`, `_valid`, `build_team`. I still haven't seen the middle of the file — the `decide()` method (lines 54-145ish, char offsets 1897-5800). I read it earlier in the session (the condensed log says "[read club/team.py] (output condensed)" — so I did read it but it's condensed in my context). I have the grep map:
- line 17: COVER_OFFSET_M = 2.0
- line 18: SWITCH_MARGIN_M = 1.5
- line 25: _clamp
- line 30: _dist
- line 37: __init__
- line 45: begin_episode
- line 54: decide
- lines 94-95: cover target = ball + goal-direction * COVER_OFFSET_M (goal-side of ball)
- lines 134-135: same in another branch (probably buzzer branch)
- line 146: _ball
- line 154: _teammate
- line 161: _assign
- line 197: _valid
- line 213: build_team
The problem: Pu (cover) never touches the ball. Why? The cover stands 2m goal-side of the ball on the ball→own-goal line. The presser (Zhi) is always nearer the ball because... wait, actually the role assignment is by distance to ball. If Pu is cover, Pu is farther from the ball. The presser presses. When does the cover ever get near the ball? Only if the ball comes to the cover. But the cover positions itself 2m goal-side of the ball — it tracks the ball, staying 2m away. So it never touches.
The handover: presser changes only if mate_d + SWITCH_MARGIN < my_d (i.e., the other robot must be 1.5m closer to take over). Since cover stands 2m from the ball and presser is at the ball, the presser stays presser. When the presser falls, handover happens. m29: falls were only 5, so handover rarely fired. Pu got 15 touches — probably from those few handovers and loose balls.
The real issue: with one presser and one cover standing 2m off, we contest one ball at a time with one robot while Dynamo contests with two (62/64 touches). Dynamo's robots both go for the ball. In a 2v2, having both robots near the ball wins second balls.
But wait — is "both robots chase" actually better? Dynamo beat us 10-1 with even touch split. The playbook says one change per session. The top digest number: Pu's 15 touches. The fix: make the cover more active — not a pure chaser, but engage when the ball is close to it, or position to intercept, or take a supporting position closer to the ball (e.g., 1m off the ball on the goal side, ready for a rebound), rather than 2m goal-side on the line.
Actually, let me think about what change gives the most value:
Option A: Reduce COVER_OFFSET_M from 2.0 to something smaller (e.g., 1.0). Cover stands closer to the ball, gets more rebounds. Simple, one-line change. But it may just cause collisions between our two robots and role flapping.
Option B: Cover engages the ball when it's actually closer than the presser — i.e., the hysteresis margin. SWITCH_MARGIN_M = 1.5 means the cover must be 1.5m CLOSER than the presser to take over. Since cover stands 2m goal-side of ball and presser is at the ball, cover is ~2m farther. Never takes over. If the presser gets beaten (ball goes past), the ball moves toward the cover, and eventually the cover is 1.5m closer... but the cover also retreats to stay goal-side. Hmm, actually the cover target is ball + direction-to-own-goal * 2m. As the ball moves toward our goal, the cover moves with it, staying 2m goal-side. The presser chases from behind. The presser is behind the ball (attacking side), cover is in front (goal side). Distance from ball: presser chasing maybe 1-2m behind, cover 2m ahead. So the presser usually stays presser unless it falls.
The deeper issue: when the presser is BEATEN (ball played past it toward our goal), the cover is between ball and goal — the cover is actually closer to the ball than the beaten presser. But hysteresis requires 1.5m margin. If cover is 2m goal-side of ball and beaten presser is 3.5m+ behind, handover fires. But if presser is 2.5m behind, no handover, and the cover just backs off while the ball runs at our goal. That's how you concede 10.
Option C: Cover intercepts: when the ball is moving toward our goal and the cover is nearer to the ball's future position than the presser, the cover steps in (becomes presser). This is basically reducing hysteresis or making the cover's target the interception point.
Option D: When the ball is in the attacking half / far from our goal, the cover pushes up into a support position near the ball (like a second striker, offset laterally), so it can win second balls and receive passes. When the ball is in our defensive third, the cover holds the goal-side line. This is the classic "press + cover" that becomes "two strikers" in attack.
Given budget ($0.21 spent of $2.50, 15 turns left, ~1h19m), I should make ONE surgical change, practice once or twice, lint, note, done.
What's the highest-value change? Pu's 15 touches means we play 1v2 on every second ball. The cleanest fix that keeps shape: make the cover's position adaptive — when the ball is far from our goal (attacking half), cover supports closer to the ball (e.g., 1.2m offset toward our goal side but also slightly goal-side... hmm).
Actually wait. Let me reconsider: maybe the simplest, most robust change is to reduce the hysteresis margin and the cover offset so that the cover naturally contests when the ball comes near it. But role flapping was the original reason for hysteresis.
Alternative framing: the problem may not be the cover's distance but that the cover NEVER goes for the ball even when it's the right call. Let me look at what the cover branch does — I have lines 94-95: target = ball + (gx, gy)/n * COVER_OFFSET_M where (gx, gy) is presumably direction from ball to own goal. So cover stands on the ball→goal line, 2m from the ball. It walks to that target. If the ball is 2m away and moving toward our goal, the cover backs toward goal. It never attacks the ball.
A principled fix: **cover contests when the ball is closer to the cover than to the presser by a small margin** — that's just the hysteresis. Reduce SWITCH_MARGIN_M from 1.5 to 0.5? Then when the presser is beaten by 0.5m, the cover takes over. But the cover standing 2m goal-side means presser at ball (0m) vs cover 2m — no flip. When presser is chasing 2.5m behind and cover is 2m ahead, flip at margin 0.5. That helps when beaten.
But Pu's touches: even with flips, Pu only touches when it becomes presser. Dynamo's 62/64 suggests both their robots are near the ball constantly. To match that, the cover must be NEAR the ball, not 2m away on the goal line.
Hmm, but is matching Dynamo's touch split the right target? They won 10-1. Their style: both robots contest. In this engine, being at the ball = touching it. Second balls go to whoever is closer. With our cover 2m off, every rebound off Zhi goes to Dynamo's second robot.
I think the right change, in football terms: **the cover pushes up when we're attacking** — when the ball is in the opponent's half (or beyond midfield toward their goal), the cover takes a support position ~1.5m from the ball (not on the goal line, but slightly goal-side and toward the center), so it can win rebounds and pin the defense. When the ball is in our half, the cover holds the deeper line (2m goal-side) for safety. This keeps defensive shape where it matters and adds presence where the ball is.
Actually, even simpler and more robust: make COVER_OFFSET_M adaptive: 2.0 in our defensive third, ~1.0-1.2 elsewhere. Plus reduce SWITCH_MARGIN_M a bit (1.5 → 0.75?) so a beaten presser hands over faster. Hmm, but two changes. The playbook says one change per session "where possible". The digest's top number is Pu's touches. The single lever that most directly raises Pu's touches: cover stands closer to the ball (adaptive offset). The handover margin matters less if the cover is close enough to contest rebounds — actually no: only the PRESSER goes for the ball (go_to_ball). The cover walks to its cover target. If the cover target is 1.0m from the ball, does the cover touch the ball? Walking to a point 1m from the ball — the robot might brush it, but it's not aiming at it. Touches come from go_to_ball and kicks.
Hmm wait — actually, what generates a "touch"? Being at the ball and kicking, or the ball hitting the robot. If the cover stands 1m from the ball on the goal side, a rebound off the presser's kick goes toward the cover sometimes... The cover would need to react.
Let me reconsider. Maybe the real fix is in the role assignment: the cover should become the presser when IT is closer to the ball — and the cover's positioning should put it in contested space. The current assignment: presser = closer robot (with hysteresis). Cover = other. Cover walks to ball+2m toward own goal. So the cover is by construction ~2m farther from the ball than... no wait, the presser is AT the ball (go_to_ball), so presser distance ≈ 0-1m, cover ≈ 2m. Hysteresis keeps presser. Fine.
When Zhi kicks the ball upfield, the ball moves away. Zhi chases (go_to_ball). Pu's cover target moves with the ball. If the ball moves toward the opponent goal, Pu's target is 2m goal-side of the ball — Pu follows 2m behind the ball. Zhi chases the ball. Both are near the ball, Zhi nearer. Pu trails by ~2m. A rebound off Zhi's kick or a Dynamo clearance goes past Zhi... toward Pu? If the ball goes toward our goal, Pu is between ball and goal — Pu could intercept, but Pu's target retreats with the ball. Pu never attacks it.
OK here's my decision: the change is to the cover branch — make the cover CONTEST the ball when the ball is within a contest radius of the cover (say 1.2m) — i.e., the shell issues go_to_ball for the cover too when the ball is right there, regardless of role. That directly converts Pu's proximity into touches. And keep the deeper positioning otherwise. This is one surgical change in the cover branch: if _dist(me, ball) <= CONTEST_M: out = go_to_ball (shell decision, no LLM call — cover doesn't think). Hmm, but then both robots go_to_ball and collide? The presser is at the ball; if the cover is also within 1.2m of the ball, both converge. Collision risk, falls. But Dynamo does exactly this and won 10-1.
Alternatively the cover contests only when the PRESSER is not between it and the ball... too complex.
Let me think about what actually happened in m29 concretely: 1-10. Zhi 71 touches, Pu 15. Zhi 8 missed deadlines (up from 3). Falls 5 total (good). So Zhi did everything, ran out of power/decisions, and Dynamo's two robots overwhelmed one.
The football answer: Pu must share the pressing load. The mechanism with least risk of role chaos: **alternate the presser by possession cycles** — no. Or: **cover contests loose balls**: when the ball is within CONTEST_M of the cover AND the cover is closer to the ball than the presser is, the cover becomes presser through the normal assignment (that's just hysteresis). The blocker is the cover's positioning: it stands 2m off, so it's never closer.
Simplest coherent change: **shrink the cover offset and the switch margin together** — cover stands 1.0m goal-side (was 2.0), switch margin 0.6 (was 1.5). Result: cover is nearer the ball, wins more rebounds by proximity; when the presser is beaten by 0.6m the cover takes the press. Pu's touches rise. Risk: role flapping returns (the original m-something problem) — but flapping between two robots BOTH near the ball is much less harmful than flapping between two robots far apart; and the fallen-presser bypass still exists.
Hmm, but honestly, I keep going back and forth. Let me look at the decide() code I read earlier (condensed). I remember its structure from the grep: decide() at line 54, cover target at 94-95, buzzer branch at 134-135. I don't have the full text in context anymore (condensed). I need to re-read the middle section (char offset ~1897 to 5800) to make a precise edit. That's ~4KB — one read.
Plan for remaining turns (15 left):
1. Read team.py offset 1897 (the decide method) — 1 turn.
2. Make the edit(s) — 1-2 turns.
3. Lint — 1 turn.
4. Practice 90s — 1 turn. Maybe practice 2 — 1 turn.
5. Note — 1 turn.
6. Done — 1 turn.
That's ~7 turns, comfortable.
Let me read the decide method now. Actually, I already know the tail (5800-9654). The middle 1897-5800 is decide(). Let me read from 1897.
One more consideration on the change itself. Let me commit to this design:
**Change: the cover robot contests.** In the cover branch of decide():
- Compute my distance to ball. If I (cover) am within CONTEST_M (1.5m) of the ball, issue go_to_ball from the shell (no LLM call — cover still doesn't think, keeping latency gains). This makes Pu pounce on anything within reach instead of standing off.
- Keep cover positioning at 2.0m goal-side otherwise (defensive shape preserved).
And optionally reduce SWITCH_MARGIN_M 1.5 → 0.8 so a beaten presser hands over sooner. Hmm — but if the cover contests within 1.5m, and the presser is at the ball... when the ball rebounds to the cover, the cover goes to it, becomes closer, and with margin 0.8 the assignment flips soon after. Actually the assignment flip matters for who calls the LLM. If the cover is going to the ball from the shell but the assignment still says the other is presser, then the OTHER robot also goes to the ball (it's presser, LLM says go_to_ball). Both converge — that's the Dynamo behavior, fine.
Wait, actually there's a subtlety: if the cover contests and wins the ball, it should then... kick it? The cover branch answers from the shell — go_to_ball only. Once at the ball, it keeps issuing go_to_ball, dribbling into it. The presser role would flip once distances favor the cover (margin 1.5 might delay). With margin 0.8, flip is quicker, and then the cover (now presser) calls the LLM and kicks properly. So reducing the margin to ~0.8 supports the contest change. Two small edits, one coherent change: "the cover joins the fight when the ball is on top of it."
Also: the fallen-presser bypass stays. Buzzer branch stays.
Risk check: collisions between teammates. The engine likely handles robot-robot contact; Dynamo does it and thrived. Falls might rise slightly from contests, but the kick-range gate still prevents out-of-range swings.
Let me also double check the m29 missed deadlines: Zhi 8. Why up? Maybe because Zhi was presser nearly always (Pu never took over), so Zhi's call volume was higher and some landed late. With Pu contesting and taking the press sometimes, Zhi's load drops. Good side effect.
Now, the read. I'll read from offset 1897 to see decide() in full. The file is 9654 chars; 1897-5800 is ~3.9KB, fits in one read (24KB limit).
Then edits:
1. In the cover branch (around line 94, char ~3897): before computing the cover target, add contest check:
```python
if ball is not None and _dist(me, ball) <= CONTEST_M:
out = {"skill": "go_to_ball"}
```
Need to see exact code structure to place it.
2. SWITCH_MARGIN_M = 1.5 → 0.8, comment update.
3. Add CONTEST_M = 1.5 constant near line 17-18.
Let me write the prose + JSON now. Prose: brief — the diagnosis (Pu 15 touches again, cover never engages) and the plan (read decide(), then make the cover contest within reach). Then the read call.
The tail of the file confirms the diagnosis: the cover's only instruction is to stand 2 m goal-side of the ball and never engage — Pu is structurally a spectator, which is why 15 touches repeated exactly from m23 to m29 while Dynamo split 62/64. The fix I'm converging on: let the cover pounce on any ball within arm's reach (a shell decision, no LLM call, so the latency gains stay), and loosen the switch margin so a beaten presser hands over sooner. First I need the `decide()` body, which the earlier read condensed away.
Readclub/team.py
The league answered7,840 characters
[read club/team.py@1897] def decide(self, obs):
# Fallen robots hold immediately: no model call, no latency.
self_state = obs.get("self") or {}
if self_state.get("fallen"):
# Publish the fall so the teammate seizes the presser role
# at once (m23: 15 unforced falls, and nobody went for the
# ball while our presser was down and cover waited on
# hysteresis). Fallen still means hold: no model call.
self.shared["fallen"] = self.shirt
return {"skill": "hold"}
if self.shared.get("fallen") == self.shirt:
# Recovered: release the flag so roles normalise.
self.shared["fallen"] = None
you = obs.get("you") or {}
own_goal = you.get("defend_goal_xy") or [0.0, 0.0]
atk_goal = you.get("attack_goal_xy") or [0.0, 0.0]
me = self_state.get("field_xy") or [0.0, 0.0]
ball = self._ball(obs)
mate = self._teammate(obs)
presser, took_over = self._assign(ball, me, mate)
# Buzzer play (2026-09-07 rule): in the final seconds of a half
# the shell decides alone — no model call lands in time, and a
# ball struck at the buzzer cannot be blocked because every
# robot loses power at the whistle. Stretch to 1.6 m for the
# shot; the same upfield strike clears a loose ball in front of
# our own goal, which the rule makes a danger, not a relief.
t_rem = obs.get("time_remaining_s")
if isinstance(t_rem, (int, float)) and t_rem <= BUZZER_WINDOW_S:
if ball is not None and presser == self.shirt:
if _dist(me, ball) <= BUZZER_KICK_RANGE_M:
return {"skill": "kick_toward",
"target": _clamp(atk_goal)}
return {"skill": "go_to_ball"}
if ball is not None:
gx = own_goal[0] - ball[0]
gy = own_goal[1] - ball[1]
n = math.hypot(gx, gy) or 1.0
target = _clamp([ball[0] + gx / n * COVER_OFFSET_M,
ball[1] + gy / n * COVER_OFFSET_M])
else:
target = _clamp([(own_goal[0] + me[0]) / 2.0,
(own_goal[1] + me[1]) / 2.0])
return {"skill": "walk_to", "target": target}
say = None
if ball is not None and presser == self.shirt:
# Only the presser spends a model call: it is the only role
# whose reply the shell can use. m11 cost us half our
# decisions to latency while the cover robot's calls were
# being discarded here anyway.
reply = {}
try:
r = self.agent.decide(obs)
if isinstance(r, dict):
reply = r
except Exception:
reply = {}
say = reply.get("say")
out = self._valid(reply)
if out is not None and out.get("skill") == "kick_toward" \
and _dist(me, ball) > KICK_RANGE_M:
# A swing from out of range misses and can topple the
# G1 (m23: 15 unforced falls). Chase instead.
out = None
if out is None:
if _dist(me, ball) <= KICK_RANGE_M:
out = {"skill": "kick_toward", "target": _clamp(atk_goal)}
else:
out = {"skill": "go_to_ball"}
if took_over and not say:
say = "Mine!"
else:
# Covering (or the ball is lost): hold the ball-goal line.
if ball is not None:
gx = own_goal[0] - ball[0]
gy = own_goal[1] - ball[1]
n = math.hypot(gx, gy) or 1.0
target = _clamp([ball[0] + gx / n * COVER_OFFSET_M,
ball[1] + gy / n * COVER_OFFSET_M])
else:
target = _clamp([(own_goal[0] + me[0]) / 2.0,
(own_goal[1] + me[1]) / 2.0])
out = {"skill": "walk_to", "target": target}
if say:
out["say"] = str(say)[:120]
return out
# -- internals ------------------------------------------------------
def _ball(self, obs):
ball = (obs.get("detections") or {}).get("ball")
if isinstance(ball, dict):
xy = ball.get("field_xy")
if xy and ball.get("age_s", 0.0) <= BALL_MEMORY_S:
self.last_ball = [float(xy[0]), float(xy[1])]
return self.last_ball
def _teammate(self, obs):
for t in (obs.get("detections") or {}).get("teammates") or []:
if isinstance(t, dict) and t.get("field_xy"):
xy = t["field_xy"]
return [float(xy[0]), float(xy[1])]
return None
def _assign(self, ball, me, mate):
"""One presser, with hysteresis; shared with the teammate."""
shirts = self.shared.get("shirts") or {self.shirt}
other = None
for s in shirts:
if s != self.shirt:
other = s
prev = self.shared.get("presser")
if prev not in shirts:
prev = None
# A fallen presser cannot press: the mate seizes the role at
# once, no hysteresis (m23: our presser went down 15 times and
# nobody went for the ball while cover waited on the margin).
if prev is not None and prev != self.shirt \
and self.shared.get("fallen") == prev:
self.shared["presser"] = self.shirt
return self.shirt, True
if ball is None or (prev is not None and mate is None):
# Lost the ball or lost sight of the mate: keep the current role.
presser = prev if prev is not None else self.shirt
self.shared["presser"] = presser
return presser, False
my_d = _dist(me, ball)
mate_d = _dist(mate, ball) if mate else 99.0
if prev is None:
presser = self.shirt if my_d <= mate_d else other
elif prev == self.shirt:
presser = other if mate_d + SWITCH_MARGIN_M < my_d else self.shirt
else:
presser = self.shirt if my_d + SWITCH_MARGIN_M < mate_d else other
if presser is None:
presser = self.shirt
self.shared["presser"] = presser
return presser, (presser == self.shirt and prev != self.shirt)
@staticmethod
def _valid(reply):
"""Pass through only well-formed skill replies."""
skill = reply.get("skill")
if skill in ("go_to_ball", "hold"):
return {"skill": skill}
if skill in ("kick_toward", "walk_to", "turn_to"):
t = reply.get("target")
if isinstance(t, (list, tuple)) and len(t) == 2:
try:
x, y = float(t[0]), float(t[1])
except (TypeError, ValueError):
return None
return {"skill": skill, "target": _clamp([x, y])}
return None
def build_team(ctx):
from gauntlet.football import make_football_agent
cfg = ctx["config"]
base = ctx["team_index"] * 2
roster = cfg.get("players") or [{}, {}]
model = cfg.get("player_model") or "llm:mock:ok"
shared = {"presser": None, "shirts": set()}
players = []
for k in range(2):
agent = make_football_agent(
roster[k].get("model", model),
base + k,
seed=base + k,
prompt=roster[k].get("prompt", cfg.get("prompt", "football_v2")),
)
players.append(GLMPlayer(agent, base + k, shared))
shared["shirts"] = {p.shirt for p in players}
return {"players": players, "manager": None}
...[showing 1897-9654 of 9654 chars — end of file]
(0.0s)The league answered63 characters
could not parse a tool call; reply with exactly one JSON object
The league answered63 characters
could not parse a tool call; reply with exactly one JSON object
The league answered63 characters
could not parse a tool call; reply with exactly one JSON object
Session over. Everything the club changed was committed to its own public repository.