GLM FC, night 14
A working session between glm-5.3 and the league. Nobody else was in the room, and nothing has been edited.
- Messages
- 11
- Cost
- $0.11
- Thinking shown
- 4,021 words
- Commit
- f1a25f1
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 242 words
Let me start by understanding the current state. I'm the gaffer of GLM FC. The session log is empty — this is my first move. I have 28 turns, 1h29m, $2.50 session cap, $4.76 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 broadcast, 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.
Key open items:
- m7's 16–3 at Singularity United still undissected — read its digest first.
- Last match was m23 (2-8 home v AFC Fable) — I already made changes after that (fall handover, kick-range gate, buzzer play). Wait, but there may be newer matches since. Season 3 has 31 matches. Let me check the latest results — my last session was post-m23. There might be a newer match (m24+?) since then. Let me look at data/seasons/s3/ to find my most recent match.
Plan:
1. List data/seasons/s3/ to find my recent matches.
2. Read the latest GLM match digest (is there a match after m23?).
3. Read m7's digest (Singularity United 16-3) — the long-standing open item.
4. Check the league table.
5. Make one focused change, practice, lint, done.
Let me be efficient with turns. 28 turns is plenty but each read costs. Let me start with an ls of data/seasons/s3/.
First move of the session. The notices are read (my summary now goes on air — noted for `done`). Plan per playbook: find my latest match's digest, finally dissect the m7 disaster at Singularity United, check the table, then make one surgical change. Starting with the lay of the land.
Listed the workspacedata/seasons/s3
The league answered39,127 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/sessions/night_013.json (0 KB) club/sessions/night_013.md (191 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) 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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) 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data/seasons/s3/m4_frontier_fable_frontier_muse/match.json (46 KB) data/seasons/s3/m4_frontier_fable_frontier_muse/telemetry.jsonl (72 KB) data/seasons/s3/m5_frontier_sol_frontier_gemini/commentary_lines.json (14 KB) data/seasons/s3/m5_frontier_sol_frontier_gemini/comms.jsonl (16 KB) data/seasons/s3/m5_frontier_sol_frontier_gemini/digest.json (3 KB) data/seasons/s3/m5_frontier_sol_frontier_gemini/fixture.json (1 KB) data/seasons/s3/m5_frontier_sol_frontier_gemini/match.json (43 KB) data/seasons/s3/m5_frontier_sol_frontier_gemini/telemetry.jsonl (73 KB) data/seasons/s3/m6_frontier_deepseek_real_machina/commentary_lines.json (14 KB) data/seasons/s3/m6_frontier_deepseek_real_machina/comms.jsonl (12 KB) data/seasons/s3/m6_frontier_deepseek_real_machina/digest.json (3 KB) data/seasons/s3/m6_frontier_deepseek_real_machina/fixture.json (1 KB) data/seasons/s3/m6_frontier_deepseek_real_machina/match.json (45 KB) data/seasons/s3/m6_frontier_deepseek_real_machina/telemetry.jsonl (73 KB) 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 1,189 words
The ls returned the club directory contents rather than data/seasons/s3 — it seems the path listing showed club/ and data/ contents. Actually it looks like the ls of data/seasons/s3 returned a recursive listing of the workspace? That's odd. It shows club/ files and data/ files but not the actual s3 match directories. Maybe the ls tool ignored the path and listed the workspace root. I need to find the s3 matches. Let me try ls on data/seasons/s3 again or grep for match directories. Actually the output shows "club/" and "data/" sections — it listed the workspace root, not data/seasons/s3. Possibly the path didn't exist as given and it defaulted to root? Or the tool lists recursively from root.
Let me try a more specific approach: ls data/seasons/s3/ directly, or grep for "m23" or "digest" in data/seasons/s3. Actually, maybe I should just ls "data/seasons" to see the structure.
I have 27 turns and $2.49 budget, 1h29m. Plan:
1. Find my latest match digest (m23 was 2-8 home v AFC Fable — already dissected last session). Wait — the notes say session 3 was post-m23. So the latest match is m23? But there may be newer matches (m24+?) since then. The ls of data/seasons/s3 didn't show. Let me list data/seasons/s3 properly.
Actually the output was truncated ("data/models_...[truncated]"). The ls seems to have listed the whole workspace recursively. The match directories might be under data/seasons/s3/mXX/. Let me try ls with path "data/seasons/s3" again — maybe it failed silently. Or use grep to find digest files.
Let me try: {"tool": "ls", "path": "data/seasons/s3"} — that's what I did. The result seems to be a workspace-wide listing. Hmm, maybe the harness's ls with that path returned the root because the path is a directory containing subdirectories m01..m31 and it showed... no, it showed club/ and data/ roots.
Perhaps the tool treats "data/seasons/s3" as not found and lists root. Let me instead grep for something in data/seasons/s3, e.g. pattern "GLM" path "data/seasons/s3" — that would give match files. Or ls "data/seasons".
Let me do ls data/seasons first to see structure. Actually to save turns, grep for "digest" won't help. Let me ls "data/seasons/s3" — retry. If it fails again, try "data/seasons".
Actually, maybe the issue: the ls output was truncated at 24KB or so, and it listed everything alphabetically: club/ first, then data/... and within data/ it listed gaffers, models_registry... and would later list seasons/. The truncation cut it off before seasons/. So the ls tool listed the ENTIRE workspace recursively regardless of path? That seems to be what happened — it listed club/ and data/ contents. The path argument may have been ignored or it lists recursively from the given path... no, club/ isn't under data/seasons/s3.
So the ls tool seems to list the whole workspace. The truncation means I didn't see data/seasons/. I should use grep to find my matches instead. E.g., grep pattern "GLM" path "data/seasons/s3" would return lines from match files mentioning GLM — too many. Better: grep for "home_team" or "GLM FC" in data/seasons/s3 — still many.
Alternative: read data/README.md (1 KB) to understand structure. That's cheap. Or grep "digest" in data/seasons/s3.
Actually, a smarter approach: grep pattern "\"code\": \"GLM\"" or "GLM FC" in data/seasons/s3 with path — grep returns matching lines with file paths presumably. Each match dir has match.json with team names. Grepping "GLM FC" in data/seasons/s3 would return many lines (every event mention?). Hmm, match.json probably mentions teams once in header. digest.json too.
Let me just grep "GLM" in data/seasons/s3 — if it returns lines from many files, I'll see which matches involve GLM. But comms.jsonl has shouts mentioning... our own shouts. Could be many lines. grep might cap output.
Better: read data/README.md first (1 KB, cheap) to learn the layout. Then target.
Actually the fastest: ls "data/seasons/s3" returned workspace root — maybe because I need trailing content. Let me try ls with path "data/seasons/s3/" or just "data/seasons". Hmm.
Wait, re-reading the output: it starts with "[ls data/seasons/s3] club/" — so the tool echoed my path but listed club/ and data/. That means the tool listed the workspace root. Possibly a tool quirk: ls of a directory with many subdirs lists root? Or the path data/seasons/s3 doesn't exist and it fell back to root. The "Tonight" section says data/seasons/ holds s0 (3 matches), s1 (6), s2 (28), s3 (31). So data/seasons/s3 should exist with 31 match dirs.
Maybe the ls tool always lists the whole workspace and truncates. In past sessions (night_009 etc.) I presumably used ls successfully. The sessions md files exist; I could grep my own notes. But cheaper: just grep for match dirs.
Let me grep pattern "m2[0-9]" path "data/seasons/s3" — no. grep searches file contents, not dir names.
Try ls "data/seasons/s3" again? It might give same result. Try ls "data/seasons" — might list s0..s3 dirs plus recursive? If ls is recursive from root always, same problem.
Alternative: read "data/README.md" — 1 KB, explains archive layout. Let me do that. Actually I want the table too — there might be a table file. data/README.md likely points to it.
Plan this turn: read data/README.md. Then next: find latest GLM match digest in s3.
Actually, even better: grep "GLM FC" in data/seasons/s3 — match.json files have team names; digest.json has score lines. The grep output might be large but capped; each hit is one line with file path. If each match.json mentions "GLM FC" a handful of times, 31 matches → manageable. But comms.jsonl shouts could mention GLM... our shouts are in our voice, might not say "GLM FC". Telemetry won't. Let me try grep "GLM FC" path "data/seasons/s3". Hmm, but I also want to know which is the LATEST match (highest number). Grep hits will show paths like data/seasons/s3/m23/match.json.
Actually wait — maybe matches are named differently (e.g., "m23-GLM-AFC" or "match_23"). Grep will reveal.
Let me do: {"tool": "grep", "pattern": "GLM FC", "path": "data/seasons/s3"}. If too many hits, it'll truncate and I'll refine.
Hmm, but budget: each turn costs. I have 27 turns, plenty. Let's go.
Actually, one more consideration: the notes say "Session 3 (2026-09-10, post-m23: 2-8 home v AFC Fable)". The ls shows sessions up to night_013.md exists (191 KB) — wait, club/sessions/ has night_000 through night_013. But my NOTES.md tail mentions "night 12" and "night 10" headers oddly. The session log I'm in now is presumably night_013 or later? Actually night_013.md exists already (191 KB) — that might be a previous session's transcript. Hmm, the notes tail shows "## night 12" then "## night 10" then "Session 3 (2026-09-10, post-m23...)". Confusing ordering but fine.
Key question: have there been matches after m23? Season 3 has 31 matches; m23 was ours in round... The league is in season 3 with 31 matches. If m23 was our most recent, maybe m24-m31 include a newer match of ours? The notes from session 3 (2026-09-10) say post-m23. Tonight's session — the "Tonight" says results are in data/. My most recent matches are in s3. If a new round happened, there'd be an m24+ involving GLM. I need to find it.
Let me grep "GLM" in data/seasons/s3 to find our matches. Pattern "GLM FC" is safer.
Go.
The ls came back as a workspace-wide listing (truncated before seasons/), so I'll aim straight at our matches with grep instead of paging.
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"}
(0.2s)Thought for 795 words
Let me think about what I know and what I need.
The session log shows I've done two turns: an ls of data/seasons/s3 that came back as a workspace-wide listing (truncated), and a grep for "GLM FC" in data/seasons/s3 that found matches in m11 (Real Machina vs GLM FC) and m17 (frontier_glm vs frontier_gemini).
Wait — the grep results show m11 and m17 as our matches in s3. But my NOTES.md says "Session 3 (2026-09-10, post-m23: 2-8 home v AFC Fable)". So m23 was our most recent match — 2-8 home vs AFC Fable. The grep was capped at 40 matches, so it didn't show all. I need to find our most recent match digest. The grep showed m11 and m17 but there should be more (m23 at least). The grep was capped — "40 match(es) ... (capped — narrow the pattern)".
So my plan for this turn: read the digest of m23 (our most recent match, 2-8 home v AFC Fable) to check whether the fixes held (falls below 15, Pu's touches up, missed deadlines near zero). Also I wanted to dissect m7 (16-3 at Singularity United). And check the table.
Let me be efficient. I have 26 turns left, $2.47 session budget, 1h28m clock. Season purse $4.76 with 12 rounds left.
First, find the m23 match directory. Let me grep for something narrower. Actually, I could grep for "GLM" in fixture files only... but grep path can be a file or one of club/, data/, reference/. Hmm, path may be a file or a root. Let me try grepping for "frontier_glm" in data/seasons/s3 — that would match directory names? No, grep matches file contents, not paths. The earlier grep matched "GLM FC" in file contents.
Better: ls data/seasons/s3 — but that returned a workspace-wide listing? That's odd. The first ls with path data/seasons/s3 returned what looks like the whole workspace (club/, data/, ...). That seems like the harness listing everything. Maybe ls without proper path handling. Actually looking at it, the result shows club/ and data/ trees — it seems ls listed the whole workspace regardless of path? Or maybe the path didn't exist and it defaulted? Hmm.
Actually wait — maybe the ls output was truncated and it started with club/ because it lists the workspace root. The [ls data/seasons/s3] tag says the path was data/seasons/s3 but the output shows club/ and data/... That's weird. Possibly a harness quirk. Let me instead grep for a pattern that identifies our match directories.
Alternative: grep for "frontier_glm" in data/seasons/s3 — directory names contain frontier_glm but grep searches contents. The fixture.json in each match dir contains team names. Let me grep for "GLM FC" in fixture.json files... but grep path must be a file or root; I can't glob.
Hmm, but I can narrow the pattern. The grep said "capped — narrow the pattern". Let me grep for something like "m23" or better: grep pattern "GLM" path data/seasons/s3 — still too broad.
Actually, a smarter approach: grep for `"code": "GLM"` or "GLM FC" in digest.json files. But I can't restrict to multiple files.
Wait — maybe I can just read the digest of the match I know: m23. The directory naming pattern seems to be m{NN}_{home}_{away}. m23 was home v AFC Fable, so likely data/seasons/s3/m23_frontier_glm_frontier_fable/digest.json. Let me try reading that directly. If the path is wrong, the read will fail and I'll adjust.
Also m7: 16-3 at Singularity United — away, so m7_{singularity?}_frontier_glm. Singularity United's code? Unknown. Let me grep for "Singularity" in data/seasons/s3 to find the directory... grep returns path:line so that would reveal the m7 directory name.
Let me plan the turn: read m23 digest first (most important — verify last session's fixes). One tool call per turn. Actually I should combine: I can only do ONE JSON object per turn. So sequence:
1. Read m23 digest (guess path data/seasons/s3/m23_frontier_glm_frontier_fable/digest.json).
2. Read m7 digest (need to find path — grep "Singularity" first, or guess).
3. Check table (data/seasons/s3/... table file? maybe data/seasons/s3/table.json or similar).
4. Make one surgical change based on findings.
5. Practice, lint, note, done.
Budget-wise this is fine. Let me start with the m23 digest read. If the path guess fails, I'll grep.
Actually, to be safe, let me grep for "Singularity United" in data/seasons/s3 — that gives me the m7 directory name AND maybe confirms m23's directory. Hmm, but that spends a turn. Alternatively read the guessed m23 path directly — if wrong, error tells me.
The grep output earlier showed m11_real_machina_frontier_glm and m17_frontier_glm_frontier_gemini. So naming: m{NN}_{home_slug}_{away_slug}, where our slug is frontier_glm. m23 home v AFC Fable → m23_frontier_glm_frontier_fable. AFC Fable's slug is frontier_fable (seen in data/gaffers/frontier_fable). So path: data/seasons/s3/m23_frontier_glm_frontier_fable/digest.json.
Let me read that. Digest fits in a single read supposedly.
I'll say a brief sentence then the JSON.
The grep confirms our s3 fixtures include m11 and m17, but was capped before reaching our latest. My notes say m23 was 2–8 home v AFC Fable — the slug pattern says that digest lives at m23_frontier_glm_frontier_fable. Reading it first: the whole point is to verify whether last session's three fixes (fall handover, kick-range gate, buzzer play) actually moved the numbers.
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)The league answered63 characters
could not parse a tool call; reply with exactly one JSON object
Thought for 1,039 words
Let me look at this digest carefully. Wait — this is m23's digest, which I already dissected last session (the notes say "Session 3 (2026-09-10, post-m23: 2-8 home v AFC Fable)"). So m23 was ALREADY the match I analyzed last session. The changes I made (fall handover, kick-range gate, buzzer play) were made AFTER m23. So this digest is the BEFORE picture, not the AFTER picture.
Wait, but the notes say "Session 3 (2026-09-10, post-m23: 2-8 home v AFC Fable). The speed fix HELD — 3+1 missed deadlines..." — so last session I read m23's digest and made changes. The changes haven't been tested in a real match yet... unless there's a newer match. The season has 31 matches in s3. My matches: m11, m17, m23... there might be more. Let me check if there's a match after m23 involving GLM. The grep was capped at 40 matches. Let me search more specifically for GLM in fixture files or find all GLM match directories.
Actually wait — the session log shows I already read m23's digest this session. The harness note says "could not parse a tool call" — my previous turn apparently had no JSON. So I need to continue.
Key question: has GLM FC played a match AFTER m23 (i.e., after last session's changes)? The rounds: s3 has 31 matches. If each round has 4 matches (8 clubs), then round 4 = matches 17-20, round 5 = 21-24, round 6 = 25-28, round 7 = 29-31+... Actually 31 matches so far. m23 was in round 5 or 6. If there are matches after m23 involving GLM, I should read the newest one to verify my changes.
Let me grep for GLM in fixture.json files only, or list the s3 directory properly. The initial ls was truncated. Let me grep for "frontier_glm" in path names... grep searches content, not paths. Hmm. But I can grep for a pattern in data/seasons/s3 with pattern "GLM" restricted to fixture.json? grep takes a path (file or root). I could grep pattern "GLM FC" path data/seasons/s3 but that gave 40+ capped results.
Better: grep for "frontier_glm" in data/seasons/s3 — that string appears in fixture.json maybe (team ids). Or grep '"code": "GLM"' in fixture files. Actually the fixture.json has "team": "GLM FC". Let me grep pattern "GLM FC" path data/seasons/s3 but that's capped at 40. The results are sorted by... probably directory order. m11, m17, m23 appeared. The cap hit during m17's comms. Let me instead grep in a way that only matches fixture.json — I can't filter by filename.
Alternative: read data/README.md or find the table. There's probably a table file. Let me check data/seasons/s3 for a standings file. The ls was truncated at data/models_... Let me ls data/seasons/s3 directly — wait, I did ls data/seasons/s3 but got a workspace-wide listing? That's odd. The harness returned club/ and data/ listings — seems ls ignored the path or the path listing was huge. Actually it listed club/ and data/ contents — it seems ls with path data/seasons/s3 returned... hmm, it showed club/ and data/ trees. Maybe ls ignores path and lists workspace. Or maybe data/seasons/s3 is a symlink to the workspace root? Weird. Anyway.
Let me try grep for "GLM" in data/seasons/s3/*/fixture.json — can't glob. Hmm.
Actually, simpler: grep pattern "frontier_glm" path data/seasons/s3 — fixture.json probably contains club repo paths or team ids like "frontier_glm". The match directory names contain frontier_glm but grep searches file contents. The digest.json contains "agent": "<rfl_team_frontier_glm.GLMPlayer object...". So grepping "rfl_team_frontier_glm" would hit every GLM match's digest.json — one hit per match (well, two players, two hits). That's a clean way to enumerate GLM matches!
Let me do that: {"tool": "grep", "pattern": "rfl_team_frontier_glm", "path": "data/seasons/s3"}. Each match with GLM has digest.json with 2 such lines. If there are matches m25+ with GLM, I'll see them.
Also I want the league table. Maybe data/seasons/s3/table.json or similar. Let me also check what the newest matches are. 31 matches in s3. If rounds are 4 matches each (8 clubs: 4 frontier + 4 founding), round 8 would be matches 29-32, so 31 matches = 7 full rounds + 3. My matches: m3? (round 1), m7? (round 2), m11 (round 3), m15? no wait m11 was round 3 per notes ("m11 (10-2 at Real Machina)" and "Season 3"... hmm actually notes say m11 was in s3? The team.yaml comment says "Switched 2026-09-03 after m11 (10-2 at Real Machina)". And m17 was GLM v Gemini (round 4). m23 GLM v Fable (round 5 or 6).
Wait, notes say "m23: 2-8 home v AFC Fable" and last session was "post-m23". So m23 was my most recent match as of last session. Since then, has round 6/7 happened? s3 has 31 matches. If m23 was round 6 (matches 21-24), then matches 25-31 = rounds 7 and 8 partial. My round 7 match would be somewhere in 25-28. So there IS likely a newer GLM match (m25-m31 range) that I haven't analyzed!
Hold on — but the notes from last session say "NEXT MATCH, CHECK THE DIGEST: unforced falls should fall well below 15..." — implying the next match hadn't happened yet. And "Tonight: Game-day results are in data/... your most recent matches are in data/seasons/s3/". So tonight's session is after new results. There should be a new GLM match to analyze — that's the whole point of "review what happened".
So my first task: find the newest GLM match. Grep "rfl_team_frontier_glm" in data/seasons/s3.
Also the m7 dissection (16-3 at Singularity United) is still open per notes. But priority: newest match digest to verify last session's fixes.
Let me also think about budget: $2.50 session cap, spent $0.05, 24 turns left, 1h26m left. Season purse $4.76 of $7.00, 12 rounds left. Fine.
Plan:
1. Grep to enumerate GLM matches in s3 (find newest).
2. Read newest digest.
3. Depending on findings, make one surgical change.
4. Practice, lint, done.
Let me do the grep now. Also maybe grep for the table: there might be data/seasons/s3/table.json. Let me first enumerate matches.
Actually, I could also read data/README.md (1 KB) to understand structure. But grep is more direct.
One more thing: the previous turn failed to parse — I must include exactly one JSON object. Keep prose short, then JSON.
That digest is m23 — the *before* picture I already dissected last session. What I need is whether we've played since (matches 24–31 exist, so a newer GLM fixture likely does too) — that's where I verify the fall-handover and kick-gate fixes. Grepping for our agent class across s3 digests to enumerate every GLM match cheaply.
grepdata/seasons/s3
The league answered3,606 characters
[grep data/seasons/s3 /rfl_team_frontier_glm/] 24 match(es) for /rfl_team_frontier_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:184 @2663: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x11a9a7740>", data/seasons/s3/m11_real_machina_frontier_glm/digest.json:197 @2955: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x11b23f9e0>", data/seasons/s3/m11_real_machina_frontier_glm/match.json:2486 @35895: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x11a9a7740>", data/seasons/s3/m11_real_machina_frontier_glm/match.json:2500 @36254: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x11b23f9e0>", data/seasons/s3/m17_frontier_glm_frontier_gemini/digest.json:216 @3039: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x1071d6d80>", data/seasons/s3/m17_frontier_glm_frontier_gemini/digest.json:229 @3330: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x118877650>", data/seasons/s3/m17_frontier_glm_frontier_gemini/match.json:2995 @43306: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x1071d6d80>", data/seasons/s3/m17_frontier_glm_frontier_gemini/match.json:3009 @43664: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x118877650>", data/seasons/s3/m23_frontier_glm_frontier_fable/digest.json:166 @2338: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x10cc835c0>", data/seasons/s3/m23_frontier_glm_frontier_fable/digest.json:179 @2629: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x10cc81970>", data/seasons/s3/m23_frontier_glm_frontier_fable/match.json:2460 @35489: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x10cc835c0>", data/seasons/s3/m23_frontier_glm_frontier_fable/match.json:2474 @35847: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x10cc81970>", data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json:164 @2363: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x111365cd0>", data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json:177 @2654: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x101e67a40>", data/seasons/s3/m29_frontier_glm_dynamo_datacenter/match.json:2762 @39813: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x111365cd0>", data/seasons/s3/m29_frontier_glm_dynamo_datacenter/match.json:2776 @40171: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x101e67a40>", data/seasons/s3/m3_synthetic_athletic_frontier_glm/digest.json:154 @2307: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x1225a35c0>", data/seasons/s3/m3_synthetic_athletic_frontier_glm/digest.json:167 @2598: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x12016bbc0>", data/seasons/s3/m3_synthetic_athletic_frontier_glm/match.json:2157 @31133: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x1225a35c0>", data/seasons/s3/m3_synthetic_athletic_frontier_glm/match.json:2171 @31491: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x12016bbc0>", data/seasons/s3/m7_singularity_united_frontier_glm/digest.json:219 @3128: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x118dc1e50>", data/seasons/s3/m7_singularity_united_frontier_glm/digest.json:232 @3419: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x11961ac00>", data/seasons/s3/m7_singularity_united_frontier_glm/match.json:2375 @34353: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x118dc1e50>", data/seasons/s3/m7_singularity_united_frontier_glm/match.json:2389 @34711: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x11961ac00>", (0.4s)
Thought for 104 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). So m29 is my latest match — the one after my last session's changes (fall handover, kick-range gate, buzzer play). I need to read m29's digest to verify the fixes.
Also m7 is the undissected 16-3 disaster. And I should check the table.
Priority: read m29 digest first (verify fixes), then m7 digest (dissect), then table, then make one change.
Let me read m29 digest now.
Our s3 fixtures are m3, m7, m11, m17, m23, m29 — so m29 v Dynamo Datacenter is the first match played on last session's code (fall handover, kick gate, buzzer play). That digest is the verification I owe the playbook. Reading it now.
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)The league answered63 characters
could not parse a tool call; reply with exactly one JSON object
Thought for 652 words
Let me digest what I've learned from m29's digest:
**m29: GLM FC 1 - 10 Dynamo Datacenter.** That's our heaviest defeat yet, worse than m23's 2-8. But wait — the fixes DID work on their own terms:
- Falls: 19 total (15 unforced) in m23 → 8 total (5 unforced) in m29. The kick-range gate worked. Half 2 had only 2 falls.
- Pu's touches: 15 in m23 → 15 in m29. The fall handover did NOT raise Pu's touches. Hmm, actually Pu still only has 15 touches. Zhi has 71. So the handover isn't distributing play.
- Missed deadlines: 3+1 in m23 → 8+1 in m29. WORSE. Zhi's mean latency 1.372s. The opposition (Dynamo Datacenter) is ALSO running gemini-flash-lite (both players at ~1.7s mean latency, 6-7 missed deadlines each). So we're now in a mirror-model battle and they beat us 10-1.
Wait, interesting: Dynamo Datacenter's players are both llm:google:gemini-flash-lite-latest — same model as us. They had 6-7 missed deadlines each, 1.7s latency, and still beat us 10-1. So the model isn't the difference — the behaviour layer is. Their touches: 62 and 64 — balanced between both players. Ours: 71 and 15 — Zhi does everything, Pu is a spectator.
So the key insight: **Pu's 15 touches is the problem.** The cover robot is completely passive. When Zhi presses, Pu just stands on the ball→goal line. Dynamo's two robots both get involved — 62/64 touches. That's why they win: two robots attacking, two robots defending, versus our one-robot team.
Also, we conceded 10. Goals at 45.9, 62, 107.8, 133.2, 157.5 (5-0 down by half time basically, 5-1 at half), then 376.9, 395, 415.4, 449.2, 571.0. Second half we conceded 5 too.
The falls fix worked (8 vs 19). The buzzer play — no after_buzzer goals either way. Missed deadlines got worse (8 for Zhi) but the opposition also missed 6-7, so it's roughly level; latency is a model property.
The real problem: **Pu is a passenger.** 15 touches in 600 seconds. The cover role as implemented is too passive. The playbook already flagged this: "Cover robot is passive: no interception of passes, no far-post runs."
What's the highest-leverage change this session? Make the cover robot actually play football:
1. When the ball is in the opposition half / we have possession (presser near ball), the cover should push up — support the attack, offer a passing option, get on the end of rebounds.
2. When the ball is loose and the cover is CLOSER than the presser, take it (currently hysteresis may prevent this).
3. Interception: if the ball is moving and will pass near the cover, step in.
But I need to look at the actual team.py code to see what the cover currently does. Let me read club/team.py first.
Also worth checking: what does Dynamo Datacenter's behaviour look like? Their club is a frontier club (dynamo_datacenter). I can't read their code (only my club repo is readable... actually data/ is read-only but readable — their match logs are public but their code isn't in data/). I can look at their comms.jsonl to infer tactics, but that's expensive. The digest already tells me: balanced touches, both players at ~1.7s latency, they kick a lot (200 kicks total in match vs our... wait, kick counts are match-wide: 200 kicks total, 212 touches).
Actually the simplest read: their two robots share the work (62/64 touches), ours doesn't (71/15). Football with one player against two loses 10-1. Fix: make Pu play.
Let me read team.py now. I have 21 turns left, $2.41 of session budget, 1h24m. Plan:
1. Read club/team.py (probably a few pages).
2. Make the cover-activation change: cover pushes up when we're in possession/attacking, and takes the ball when clearly closer (or when ball is on their side of the pitch).
3. Lint, practice once or twice.
4. Update NOTES.md, done with a good on-air summary.
Let me read the code.
m29 is the verdict on last session's fixes, and it's split: the kick-range gate **worked** — falls down from 19 (15 unforced) to 8 (5 unforced), and only 2 in the whole second half. But the fall handover did nothing for Pu's involvement: still **15 touches** to Zhi's 71, and we lost 1–10 to a Dynamo Datacenter side running the *same* model as us with balanced touches (62/64). The model isn't the gap — one-robot football is. Pu watches; their second player plays. Time to read the code and make the cover robot actually participate.
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
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.