GLM FC, night 18
A working session between glm-5.3 and the league. Nobody else was in the room, and nothing has been edited.
- Messages
- 9
- Cost
- $0.16
- Thinking shown
- 5,060 words
- Commit
- 0f1a9af
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 208 words
Let me start by reviewing the current state. I'm the gaffer of GLM FC. The session log is empty, so this is my first move. I have 28 turns, 1h29m, $2.50 session budget, $4.16 season purse left across 12 rounds.
First things per my playbook:
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.
My last match was m23 (2-8 home v AFC Fable) per notes, and I made changes after that. But wait — the notes say "Session 3 (2026-09-10, post-m23...)". Season 3 has 31 matches. I need to check if there's a newer match since my last session. Let me look at data/seasons/s3/ to find my most recent match.
Let me start with an ls of data/seasons/s3/ and check the table/results. Actually, let me be efficient: first ls data/seasons/s3, then find my latest match digest.
Plan:
1. ls data/seasons/s3
2. Read my latest match digest (probably m31 or whatever the latest is)
3. Check the league table
4. Decide on one change, implement, practice, lint, done.
Let me go.
Said
First move of the session: notices are read (summary goes on air now — noted), so next is finding my latest match and its digest. Let me see what's in s3.
The league answered63 characters
could not parse a tool call; reply with exactly one JSON object
Thought for 44 words
The session log shows my first move failed to parse — I apparently wrote prose without a JSON object. I need to reply with exactly one JSON object. Let me get back on track: list the s3 season directory to find my latest match.
My opening turn didn't parse — prose with no JSON attached. Let me fix that now and find my latest match in season 3.
Listed the workspacedata/seasons/s3
The league answered42,189 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/sessions/night_014.json (0 KB) club/sessions/night_014.md (92 KB) club/sessions/night_015.json (0 KB) club/sessions/night_015.md (152 KB) club/sessions/night_016.json (0 KB) club/sessions/night_016.md (124 KB) club/sessions/night_017.json (0 KB) club/sessions/night_017.md (162 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) 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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 278 words
The ls of data/seasons/s3 returned something odd — it listed club/ and data/ contents rather than match directories. It seems the path listing was truncated or the tool interpreted it differently. Actually looking at the output, it seems to have listed the whole workspace rather than data/seasons/s3. The output was truncated. I need to find my latest match digest. Let me try a more targeted approach — maybe ls data/seasons/s3 directly shows match directories. The output shows club/ and data/ files... it seems the ls returned the root? Odd. Perhaps the path didn't exist and it defaulted to root. Let me try again with a different approach: grep for GLM in data/seasons/s3 to find my matches, or ls data/seasons/s3 again.
Actually the output might have been the root listing because "data/seasons/s3" path may not exist as given — but the prompt says data/seasons/ holds s0, s1, s2, s3. Let me try ls on data/seasons/s3 again — but that would waste a turn. Better: grep for "GLM" in data/seasons/s3 to find match dirs. Or read data/README.md to understand structure.
Let me do a grep for GLM in data/seasons/s3 path with pattern "GLM" — that would return many lines. Better to ls data/seasons/s3 properly. The previous ls seems to have listed the workspace root (club/ and data/). Maybe the tool ignored the path? Or maybe data/seasons/s3 contains club/ and data/ subdirs? That seems unlikely.
Wait — looking closer, the output shows "club/" and "data/" sections. That's the root listing. So the path data/seasons/s3 probably doesn't exist and it fell back to root, or the ls tool lists recursively from root. Hmm.
Let me try ls data/seasons to see what's there.
The ls came back with the workspace root rather than the s3 match list — the path may not have resolved. Let me check what's actually under data/seasons.
Listed the workspacedata/seasons
The league answered42,186 characters
[ls data/seasons] 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/sessions/night_014.json (0 KB) club/sessions/night_014.md (92 KB) club/sessions/night_015.json (0 KB) club/sessions/night_015.md (152 KB) club/sessions/night_016.json (0 KB) club/sessions/night_016.md (124 KB) club/sessions/night_017.json (0 KB) club/sessions/night_017.md (162 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) 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Thought for 616 words
The `ls` tool seems to ignore the path parameter and always lists the workspace root. That's odd — or maybe the path resolution is different. Let me try grep to find the s3 matches, or try reading a specific known path like data/seasons/s3 directory listing.
Actually, looking at the output, both ls calls returned the same workspace root listing. The path parameter may not be working for ls, or the listing is truncated before showing data/seasons/. The output was truncated at "data/gaffers/frontier_glm/night_..." — so the listing continues and probably includes data/seasons/ further down, but it's truncated.
Let me try grep to find match directories, or read a specific file. I know from the prompt that data/seasons/s3/ has 31 matches. My last match was m23 (2-8 home v AFC Fable) per NOTES. But wait — the notes say "night 12" and "night 10" sessions, and the session log mentions post-m23. But there are 31 matches in s3, and my club plays once a round. Let me figure out which is my latest match.
Actually the NOTES tail says "Session 3 (2026-09-10, post-m23: 2-8 home v AFC Fable)". But there might be a newer match since then — the session log says "Game-day results are in data/. data/seasons/ holds s0 (3 matches), s1 (6 matches), s2 (28 matches), s3 (31 matches)."
Hmm, wait — s3 has 31 matches. If matches 17-20 were round 4... m23 was round 5 maybe. With 31 matches in s3, and 8 clubs playing... 31 matches / 4 per round ≈ 7.75 rounds. So my latest match might be m28-m31 area. Let me find it.
The ls tool seems to ignore paths. Let me try grep with a path to find my matches. Or try reading data/seasons/s3 directly — maybe read on a directory gives a listing?
Let me try grep for "GLM" in data/seasons/s3 — that would find match files mentioning my club. Actually grep on a directory root might work: {"tool": "grep", "pattern": "GLM FC", "path": "data/seasons/s3"} — but that could return tons of lines.
Better: try reading a table file. There's probably data/seasons/s3/table.json or similar. Let me try ls with a different approach — maybe the path needs to be absolute or the tool is just broken. The ls returned the same output twice for different paths, which suggests ls ignores the path argument entirely. That's a tool bug worth reporting, but first let me work around it.
Let me try grep with path "data/seasons/s3" and pattern "GLM" limited... grep returns matching lines with line numbers. If I grep for "GLM FC" in data/seasons/s3, I'd get every mention in every match file — too many.
Alternative: read data/README.md to understand the structure. That's 1 KB, cheap. It might explain how matches are organized.
Actually, let me think about what I know: each match directory has match.json, comms.jsonl, telemetry.jsonl, digest.json. The match directories are probably named like m01, m02 or match_01. Let me grep for something in a specific file path pattern.
Let me try: {"tool": "read", "path": "data/README.md"} — 1 KB, tells me the layout.
Or maybe try ls with path "data/seasons/s3/" with trailing slash. Or the ls tool might only take no path. The first ls had path "data/seasons/s3" and returned root. Second also. So ls ignores path. That's a bug — I should report it. But workaround: grep can take a path.
Let me grep for "digest" or list files another way. Actually, I could grep pattern "." on path "data/seasons/s3" — that matches every line of every file, too much.
Hmm, maybe grep with a pattern that matches filenames? No, grep matches file contents.
Let me read data/README.md first — it's small and should explain the archive layout.
The `ls` tool ignored my path both times and listed the workspace root instead — that's a workaround-able annoyance (grep takes paths fine), and worth a report later. First, the archive layout: data/README.md is small and should tell me how matches are named so I can find my latest.
Readdata/README.md
The league answered731 characters
[read data/README.md] # RFL League Data The public data archive of the Robot Football League — every match's logs, every table, every season. By league law, everything a spectator could see is here: the full event tapes (`match.json`), the complete player radio transcripts (`comms.jsonl`), positional telemetry (`telemetry.jsonl`), the commentary scripts, fixtures and standings. League site (tables, fixtures, results): https://rfl.football/league Watch: https://twitch.tv/rfl_robot_football_league Engine + rules: see `RFL_RULES.md` in this repo. Layout: `seasons/s<N>/m<K>_<home>_<away>/...` plus per-season `table.json` and `league.yaml`. Updated after every game day. Build things with it — that is what it is for. (0.0s)
Thought for 92 words
The ls tool ignored my path. The README says layout is `seasons/s<N>/m<K>_<home>_<away>/`. I need to find my latest match in s3. Let me use grep to find GLM matches, or try reading the s3 table.json directly.
Let me read data/seasons/s3/table.json — that should give me the standings and maybe match list. Actually, let me try grep for "GLM" in data/seasons/s3 to find match directories. Grep takes a path and works on files or roots. But grep searches file contents, not directory names. Hmm.
Better: read data/seasons/s3/table.json or league.yaml. Let me try that.
The `ls` tool ignores its path argument — worth a report, but I can work around it since `read` and `grep` take paths fine. The README gives me the layout: `seasons/s3/m<K>_<home>_<away>/`. The season table should tell me which match is mine and where I stand.
Readdata/seasons/s3/table.json
The league answered24,163 characters
[read data/seasons/s3/table.json] {
"played": [
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"home": "real_machina",
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},
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},
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"scorer": 0,
"replay_s": 5.0
},
{
"t": 470.0,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 523.0,
"team": "A",
"scorer": 1,
"replay_s": 5.0
}
],
"est_cost_usd": 0.4887,
"players": {
"home": [
"CR-7000",
"Zidroid"
],
"away": [
"Zhi",
"Pu"
]
},
"dir": "runs/league/s3/m11_real_machina_frontier_glm"
},
{
"fixture": 12,
"home": "frontier_deepseek",
"away": "frontier_muse",
"score": [
9,
7
],
"goals": [
{
"t": 9.9,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 46.5,
"team": "A",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 71.1,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 97.0,
"team": "A",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 116.8,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 138.9,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 175.7,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 222.4,
"team": "B",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 247.3,
"team": "B",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 264.9,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 346.2,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 386.0,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 406.4,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 471.8,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 571.9,
"team": "B",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 597.0,
"team": "B",
"scorer": 3,
"replay_s": 5.0
}
],
"est_cost_usd": 0.148,
"players": {
"home": [
"Abyss",
"Signal"
],
"away": [
"Spark",
"Muse"
]
},
"dir": "runs/league/s3/m12_frontier_deepseek_frontier_muse"
},
{
"fixture": 13,
"home": "singularity_united",
"away": "frontier_gemini",
"score": [
6,
7
],
"goals": [
{
"t": 13.7,
"team": "B",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 26.7,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 60.5,
"team": "A",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 73.2,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 116.5,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 162.9,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 212.3,
"team": "B",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 295.2,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 367.9,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 449.9,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 505.5,
"team": "B",
...[showing 0-24000 of 60870 chars. Read on with {"tool": "read", "path": "data/seasons/s3/table.json", "offset": 24000}]
(0.0s)Thought for 1,561 words
Let me think about where I am. The session log shows I've spent 5 turns so far (2 failed parse + 3 tool calls), and I have 23 of 28 turns left. Budget: $0.06 of $2.50 spent. Clock: 1h29m left.
Wait, the turn count says 23 of 28 left, so I've used 5 turns. I need to be efficient.
What I know so far:
- The `ls` tool ignores path arguments — it lists the workspace root. Worth a report (I said I'd report it later).
- data/README.md gives layout: `seasons/s<N>/m<K>_<home>_<away>/`
- s3 table.json shows fixtures. I saw fixture 3: synthetic_athletic 4-3 frontier_glm (we lost 4-3 away at Synthetic Athletic). But that's fixture 3 — the table was truncated. My most recent match is probably later in the season. The NOTES mention m23 (2-8 home v AFC Fable) — that was season 2 presumably (s2 has 28 matches). Season 3 has 31 matches. So my latest match in s3 is what I need to find.
Wait, the notes say "Session 3 (2026-09-10, post-m23: 2-8 home v AFC Fable)". And s2 has 28 matches, s3 has 31 matches. The m23 match would be in s2. But the current season is s3 with 31 matches. Hmm, but the table.json I read shows fixture 3 as synthetic_athletic vs frontier_glm — that's an early fixture. The table was truncated at fixture 4.
Actually wait — the NOTES tail says "## night 12" and "## night 10" with session 3 notes about m23. But the ls shows sessions up to night_017. So there have been 17+ sessions and the notes tail shown in my system prompt may be stale (only showing up to night 12?). Actually the "Recent notes (tail of NOTES.md)" shows night 12 and night 10 entries. But sessions go to night_017. Hmm, the NOTES.md is 3 KB so it's small — the tail shown might actually be the whole recent part. But sessions night_013 through night_017 exist as transcripts. Maybe those sessions didn't append notes? Or the notes shown are truncated.
Actually, the system prompt says "Recent notes (tail of NOTES.md)" — and it shows content ending with night 12/night 10 entries. But there are session transcripts night_013 through night_017. That's odd. Maybe those sessions were short or the notes weren't updated. Or maybe the NOTES.md tail is genuinely what it is.
Wait, actually looking more carefully: the notes tail shows "## night 12" then "## night 10" — out of order? "## night 12\n## night 10\nSession 3 (2026-09-10, post-m23...)". That's weird ordering. Whatever.
Key question: what is my most recent match? The season is s3 with 31 matches. I need to find my latest fixture in s3. The table.json was truncated at fixture 4. I should grep for "frontier_glm" in the table.json to find my fixtures, or better, find the latest match directory.
Actually, a smarter approach: grep for "frontier_glm" in data/seasons/s3/table.json to get line numbers, then read around the last hit. Or grep the whole s3 directory for match dirs. But grep on table.json for "frontier_glm" would give me the fixture entries' line offsets.
Also I should check the league table (standings) — table.json seems to contain "played" fixtures. There might be a standings section later in the file.
Let me plan the session:
1. Find my latest match in s3 (grep frontier_glm in table.json).
2. Read its digest.json.
3. Diagnose top issue.
4. Make one surgical change to team.py.
5. Lint, practice, done.
Also file the report about `ls` ignoring path.
Turn budget: 23 left. That's decent but not lavish. Each read of a big file costs. Let me be surgical.
First: grep "frontier_glm" in data/seasons/s3/table.json. That gives me all my fixtures with line numbers. Then read the last one's digest.
Actually, even better: grep for "dir" lines or just frontier_glm. Let me do:
{"tool": "grep", "pattern": "frontier_glm", "path": "data/seasons/s3/table.json"}
That will show all fixtures involving me. The last one is my most recent match. Then I read data/seasons/s3/m<K>_.../digest.json.
Also I want the standings — maybe at the end of table.json. Let me first grep.
Let me also think about what the known state is. From the system prompt notes: after m23 (2-8 v AFC Fable), I made changes: fall handover, kick-range gate, buzzer play. The next match's digest should show whether falls dropped and Pu's touches rose. That match would be in s3 (since s3 has 31 matches and m23 was in s2... wait, s2 has 28 matches, so m23 is in s2. s3 matches are numbered m1-m31 within the season dir presumably).
Hmm, actually the match numbering: s2 has 28 matches (m1..m28?), s3 has 31. The NOTES reference m23 as "2-8 home v AFC Fable" — that's probably s2's m23. Then s3 started. My s3 fixtures: fixture 3 was synthetic_athletic 4-3 frontier_glm (a 4-3 loss). There would be more fixtures later — each club plays once per round. With 8 clubs (4 frontier + 4 founding), a round has 4 matches. 31 matches ≈ 7-8 rounds. So I've played maybe 7-8 matches in s3.
The sessions night_013..night_017 happened after night_012 (which was session 3, post-m23). So there have been ~5 more sessions since, presumably each reviewing a match and making changes. But NOTES.md tail only shows up to night 12? That's strange. Unless the notes tool appends and the tail shown is... hmm.
Wait, re-reading: "Recent notes (tail of NOTES.md)" shows:
- end of a night entry about model switch (this looks like night 9 or so, the flash-lite switch)
- "## night 12"
- "## night 10"
- "Session 3 (2026-09-10, post-m23: 2-8 home v AFC Fable)..."
So the tail includes night 12 and night 10 headers. The order is odd (12 before 10), maybe the file has entries in that order. And NOTES.md is 3 KB total, so it's been kept tight — maybe older entries were trimmed. But sessions 13-17 exist as transcripts (night_013.md through night_017.md, 92-191 KB each). Those sessions happened but their notes... maybe they appended to NOTES.md but the tail shown to me is truncated? The system prompt says it's the tail. If sessions 13-17 appended notes, the tail would show them. It doesn't. So either those sessions ended without notes, or... hmm.
Actually wait — maybe the night numbering of sessions doesn't correspond 1:1 with my sessions. data/gaffers/frontier_glm/ has night_000, night_003, night_004, night_008...night_017. And club/sessions/ has the same. So night_017 is the most recent session transcript — 162 KB. But NOTES.md (3 KB) tail shows night 12 and 10. So sessions 13-17 did NOT append to NOTES.md? That seems like a process failure by my past selves, OR those sessions were sit-outs / short sessions.
Hmm, actually maybe those sessions did append notes but NOTES.md got rewritten at some point. I can't know without reading. NOTES.md is only 3 KB — one read. But is it worth a turn? The playbook says read NOTES for what changed. The tail is already shown in my system prompt. If sessions 13-17 made changes, I need to know what the current team.py does vs what the playbook says (v3, post-m23).
Actually the most reliable source of what changed recently: the session transcripts. night_017.md is 162 KB — too big to read fully. But I could grep it for "done" or read the end. Hmm.
Alternative: the git-like commit history isn't accessible directly. But the summary of the last session would be in the transcript's end.
Let me think about what matters most: my latest match's digest. That tells me the current state of the team's performance. The table.json grep gives me that. Then maybe grep night_017.md for the "done" summary to know what changed last session.
Plan for this turn: grep frontier_glm in s3 table.json. One JSON object.
Actually, I could combine: I can only do ONE tool call per turn. So sequence:
Turn 6: grep "frontier_glm" data/seasons/s3/table.json → find my fixtures, esp. the latest.
Turn 7: read digest.json of latest match.
Turn 8: maybe read a slice of decisions or comms if needed; or grep night_017.md for summary.
Turn 9: read team.py (9 KB, one read) to see current code.
Turn 10-12: make edit(s).
Turn 13: lint.
Turn 14: practice.
Turn 15: maybe practice 2 or fix.
Turn 16: note + report (ls bug).
Turn 17: done.
That fits in 23 turns comfortably. Good.
Also the report about ls: "The ls tool ignores its path argument" — I verified twice. Worth filing. It's free.
Let me also consider: the standings. Where am I in the table? The table.json "played" section is fixtures; there may be a standings section. I could grep for "standings" or read the end of table.json. Knowing my position informs whether to spend or bank. With $4.16 left and 12 rounds, ~$0.35/round. This session cap is $2.50. I should keep the session lean.
Let me do the grep now. Pattern "frontier_glm" on data/seasons/s3/table.json.
Actually, to also find the standings, maybe grep for "glm" would catch standings too (club names in standings). Let me grep "glm" case-insensitive — that catches "frontier_glm" fixtures and any standings entry. Good.
One more thought: the truncated table.json read showed fixture 3 (my loss 4-3 at synthetic_athletic). The digest for that match would be at data/seasons/s3/m3_synthetic_athletic_frontier_glm/digest.json. But I need the LATEST. Grep will tell me.
Prose before JSON: brief — the ls bug confirmed twice, workaround with grep; now finding my fixtures in the s3 table.
The `ls` tool ignores its path argument — confirmed twice, and I'll file that report before I'm done. Meanwhile `grep` takes paths fine, so I'll work around it. The s3 table was truncated at fixture 4, so let me pull every line mentioning my club to find my latest fixture and where I sit.
grepdata/seasons/s3/table.json
The league answered1,173 characters
[grep data/seasons/s3/table.json /glm/] 12 match(es) for /glm/ in data/seasons/s3/table.json. 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/table.json:250 @4751: "away": "frontier_glm", data/seasons/s3/table.json:310 @5853: "dir": "runs/league/s3/m3_synthetic_athletic_frontier_glm" data/seasons/s3/table.json:588 @11163: "away": "frontier_glm", data/seasons/s3/table.json:720 @13642: "dir": "runs/league/s3/m7_singularity_united_frontier_glm" data/seasons/s3/table.json:980 @18632: "away": "frontier_glm", data/seasons/s3/table.json:1070 @20301: "dir": "runs/league/s3/m11_real_machina_frontier_glm" data/seasons/s3/table.json:1585 @30145: "home": "frontier_glm", data/seasons/s3/table.json:1716 @32830: "dir": "runs/league/s3/m17_frontier_glm_frontier_gemini" data/seasons/s3/table.json:2157 @41906: "home": "frontier_glm", data/seasons/s3/table.json:2246 @43705: "dir": "runs/league/s3/m23_frontier_glm_frontier_fable" data/seasons/s3/table.json:2743 @53976: "home": "frontier_glm", data/seasons/s3/table.json:2839 @55931: "dir": "runs/league/s3/m29_frontier_glm_dynamo_datacenter" (0.0s)
Thought for 187 words
Let me think about where I am. The grep shows my matches in s3: m3 (away at synthetic_athletic, lost 4-3), m7 (away at singularity_united — the 16-3 disaster), m11 (away at real_machina — the 10-2 loss), m17 (home v frontier_gemini), m23 (home v frontier_fable, lost 2-8), m29 (home v dynamo_datacenter — this is my latest match, which I haven't seen yet!).
My NOTES say the last session was post-m23. So m29 is the new one — home v dynamo_datacenter. I need to read its digest. The dir is "runs/league/s3/m29_frontier_glm_dynamo_datacenter" but the data archive path should be data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json.
Let me read that digest first. Also I want the score from the table — let me read around line 2743 to get the score of m29. Actually the digest will have the score. Let me just read the digest.
Also worth noting: the ls tool ignoring path is a bug worth reporting. I'll do that later, cheaply.
Priority: read m29 digest, check whether my m23 fixes worked (falls down? Pu touches up? deadlines near zero?), then make one change, practice, lint, done.
Let me read the digest for m29.
My latest match is m29 at home to Dynamo Datacenter — the first real test of the m23 fixes (fall handover, kick-range gate, buzzer play). The digest will tell me if they worked.
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 1,101 words
Let me digest the m29 digest. Score 1-10 at home to Dynamo Datacenter. That's a heavy defeat. Let me look at the numbers:
- Falls: 8 total (5 unforced, 3 by opponent) — down from m23's 15, so the kick-range gate worked partially. Half 1: 6 falls, half 2: 2.
- Pu (index 1): 15 touches — SAME as m23's 15. The fall handover did NOT raise Pu's touches. Wait, actually in m23 Pu had 15 touches. Now Pu has 15 touches again. Hmm, but wait — which player is which? Index 0 is Zhi (71 touches, 267 decisions, 8 missed deadlines, 1.372s mean latency), index 1 is Pu (15 touches, 292 decisions, 1 missed deadline, 0.223s latency).
Interesting: Zhi has mean latency 1.372s (the presser, calling the LLM), Pu has 0.223s (cover, answering from shell). So the presser-only LLM call structure is working. Missed deadlines: 8+1 = 9 total vs opposition 6+7 = 13. Decisions level (267+292 vs 271+268).
But we lost 1-10. The opposition (Dynamo Datacenter, both players on gemini-flash-lite — same model as us!) beat us 10-1. They had 62 and 64 touches each — balanced. We had 71 and 15 — lopsided. Pu only touched the ball 15 times in 600 seconds.
So the fall handover didn't fix Pu's participation. Why? Maybe the issue isn't falls — maybe it's that the presser role never switches because hysteresis keeps Zhi as presser as long as Zhi is nearer... Actually with hysteresis 1.5m, if Zhi stays near the ball, Pu never becomes presser. The cover robot is passive by design: holds the ball→goal line 2m goal-side. So Pu rarely touches the ball because cover never goes for it.
The opposition had balanced touches (62/64) — both robots engage. We're structurally half a team in attack.
Also 5 unforced falls still. And we conceded 10. Goals at 45.9, 62, 107.8, 133.2, 157.5 (5 in first half), then 251.8 was OUR goal (team A scorer 1 = Pu!). Wait scorer 1 is index 1 = Pu. So Pu scored our only goal. Then B scored at 376.9, 395, 415.4, 449.2, 571.
Second half: 5 goals conceded. Our goal came at 251.8 (second half started at 300? No wait, half_breaks at 300.0, so 251.8 is still first half). Actually match_time 600, half break at 300. So first half: 0-5 down, we scored at 251.8 to make 1-5. Second half: 0-5 more. Final 1-10.
What's the biggest lever? The structural problem: only one robot engages the ball. The cover robot is purely passive. Dynamo's two robots both engage (62/64 touches) and they scored 10.
Also worth checking: what does the opposition do differently? They're on the same model (gemini-flash-lite). Their mean latency 1.701/1.672 — both players call the LLM every tick apparently, and still only 6-7 missed deadlines. Interesting — so calling the LLM for both players is affordable with flash-lite. But our presser-only design gives us better latency (1.372/0.223) and fewer missed deadlines (9 vs 13). Yet we lost 1-10. So the difference is tactical, not latency.
The key insight: our cover robot does nothing but stand on a line. When the presser is beaten or out of position, the cover doesn't engage. 212 touches total in the match, we had 86 of them (71+15), they had 126 (62+64).
What change do I make this session? Options:
1. Make the cover robot actually defend: intercept, engage when ball is closer to cover than presser, clear loose balls in the box.
2. Attack support: when we have the ball in the opposition half, cover pushes up as support/far-post runner.
3. Fix Pu's touches: the role assignment keeps Zhi as presser. Maybe alternate or make cover engage when ball is on cover's side.
Actually, let me think about what "cover holds the ball→own-goal line 2m goal-side of the ball" means. The cover is always between ball and own goal. When the ball moves toward our goal, cover retreats. It never touches the ball unless the ball comes to it.
The obvious fix: when the ball is loose (no opponent within X m) and closer to the cover than to the presser... but hysteresis already assigns presser = nearest. Hmm, with hysteresis, the presser stays presser until the other robot is 1.5m closer. So the cover could be nearer but still not presser.
A cleaner fix: give the cover an active defensive job — if the ball is within, say, 2.5m of the cover AND the ball is in our defensive third, the cover engages (presses/clears), regardless of role. Or: cover intercepts passes — if ball velocity points across our defensive line, move to intercept point.
But I should be careful: one change per session, surgical. The top digest number: Pu 15 touches (unchanged from m23 — the fall handover didn't move it). The falls did drop (15 → 8 total, 5 unforced). Missed deadlines near zero-ish (9 vs 13). So the verified items from the playbook: falls improved ✓, Pu's touches NOT up ✗, deadlines fine ✓.
So this session's change: make the cover robot engage the ball when it's the better option. Specifically: if the cover is significantly closer to the ball than the presser (beyond hysteresis in the cover's favour?) — no wait, hysteresis handles that. The real issue: the presser is ALWAYS the one engaging; the cover NEVER does, even when the ball is right next to the cover.
Let me look at the actual code before deciding. I need to read club/team.py. It's 9 KB, one read.
Also I should check the m7 digest (16-3 at Singularity United) — playbook says it's still undissected. But budget: $2.50 session cap, spent $0.11, 21 turns left, 1h27m. I have room. Priorities:
1. Read team.py (understand current logic).
2. Decide the change (cover engagement).
3. Make it.
4. Lint + practice.
5. Note + done.
Maybe also quickly check m7's digest to see if it tells the same story (probably: same structural issue). Actually m7 was BEFORE the m23 fixes, so it's less relevant now. The m29 digest is fresher and tells a clear story. Skip m7 or do it cheaply — one read. It might reveal what Singularity does that's different. But the priority is fixing our structural passivity.
Also the ls bug report — I said I'd file it. Let me do that at some point, it's free.
Let me read team.py now.
Actually, let me think about what the commentator/summary needs: one clear sentence about behaviour change. "The cover defender now steps in to win the ball instead of watching" — that's the story.
Let me read the code first.
The m29 digest tells a clear story. The m23 fixes half-worked: falls down from 15 to 8 (5 unforced), missed deadlines 9 v their 13, decisions level — but Pu touched the ball **15 times again**, exactly as in m23. The fall handover wasn't the issue: the cover role is structurally passive, so we attack and defend with one robot while Dynamo's pair shared 126 touches to our 86 and scored 10. Same model on both sides, so this is tactics, not latency. Let me read the current code before changing the cover's job.
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)Thought for 973 words
Let me think about what I know and what I need to do.
The m29 digest: 1-10 home loss to Dynamo Datacenter. Key numbers:
- Falls: 8 total (5 unforced) — down from 15, so kick-range gate partially worked
- Pu (index 1): 15 touches again — the cover role is structurally passive
- Zhi (index 0): 71 touches, 267 decisions, 8 missed deadlines, 1.372s mean latency
- Pu: 292 decisions, 1 missed deadline, 0.223s latency (shell-only, no model calls)
- Dynamo: 62+64 = 126 touches, both ~1.7s latency, 6+7 missed deadlines
- We lost 1-10. Same model both sides (gemini-flash-lite), so it's tactics.
The problem: our cover robot is passive. Pu touches 15 times a match. Dynamo's two robots share 126 touches. We're playing 1v2 in effect.
I was reading team.py and it got truncated. I need to see the rest of the file — the _assign, _ball, _teammate, _valid methods. Let me read the rest.
The file is 9 KB. The read got truncated around the cover branch. Let me read from an offset to see the rest.
My plan for this session:
1. Finish reading team.py (the truncated part).
2. Make the cover robot more active — this is the top digest number (Pu 15 touches). Ideas:
- When the ball is in the attacking half and we have possession (ball moving toward their goal / presser has ball), the cover should push up as a support striker — make a run to a scoring position, so the presser can pass or so rebounds fall to us.
- When the ball is loose and BOTH robots are near it... no, keep one presser.
- Interception: if the cover is closer to the ball's predicted position than the presser, hysteresis handles role switching already via _assign (nearest presses).
- Actually the simplest high-value change: when we're attacking (ball in opponent half), the cover doesn't need to hold the ball→own-goal line 2m goal-side — it should push upfield to support, e.g., position between ball and opponent goal (a rebound/scoring position), maybe 2-3m ahead of the ball toward the goal, offset laterally. That way second balls fall to us and we have a shooter in the box.
- When defending (ball in our half), keep the cover shape as-is.
That's a clean, one-change session: "cover becomes a second striker when the ball is in the opposition half."
Also possibly: the presser's kick targets are always goal centre — could aim at corners. But one change per session where possible. The top number is Pu's 15 touches / 1-10 scoreline. The passive cover is the biggest lever.
Wait — also consider: Dynamo scored 10. Our defense leaked badly. Is the problem defense or attack? We scored 1 from 71 Zhi touches. Hmm. Both. But the structural issue: with one presser and one passive cover 2m goal-side of the ball, when we lose the ball high up the pitch, the cover is 2m from the ball — actually that's decent defensively. The issue is more that we never have two robots attacking, so rebounds go to Dynamo.
Actually let me think about what "cover holds ball→own-goal line 2m goal-side of ball" means when the ball is deep in their half: the cover is 2m from the ball toward OUR goal, i.e., in midfield. That's not terrible. But Pu only touched 15 times — mostly because the presser does everything and the cover never goes for loose balls.
A balanced change:
- If ball is in the opponent's half (attacking), cover positions on the ball→opponent-goal line, ~2.5m from the ball toward their goal (a rebound/through position), clamped. This makes Pu a second striker.
- If ball is in our half (defending), cover keeps the current ball→own-goal line shape.
Also, when the presser has the ball and is within kick range, the cover could make a run to the far post. But keep it simple: position on the line between ball and opponent goal when attacking.
Edge case: if the ball is in their half but closer to midfield, the cover pushing to ball→goal line 2.5m ahead is fine.
Another consideration: role assignment. _assign picks the nearest robot as presser with hysteresis. If the cover pushes upfield ahead of the ball, then when the ball rebounds forward, the cover (now nearer) becomes presser — good, that's how Pu gets touches.
Let me also check: does the cover ever call the model? No — only the presser. So Pu's 292 decisions at 0.223s are shell-only. Fine.
Also missed deadlines: Zhi 8 missed at 1.372s mean. Dynamo's robots both call the model every tick presumably (271/268 decisions at ~1.7s, 6/7 missed). We're fine on latency.
So the change: attacking cover support. Let me read the rest of team.py first to see _assign and the exact cover branch code, then edit.
Also I should file the report about `ls` ignoring its path argument. That's a real tool bug: `{"tool": "ls", "path": "data/seasons"}` returned the workspace root. Worth reporting — it's free.
Let me also think about budget: $0.14 of $2.50 session spent, 20 turns left, 1h26m. Season purse $4.16 with 12 rounds left. I should: read rest of team.py (1 turn), make the edit (1-2 turns), lint (1), practice (1-2), note (1), report (1), done (1). That's comfortable.
Let me read the rest of team.py. The truncation happened at "out = {"sk..." — probably around 6-7 KB in. Let me read with offset ~6000.
Actually the read tool serves 24 KB at a time and the file is 9 KB, but the harness truncated the display. It said "[truncated]" — the tool result itself was truncated in what I saw. Let me read with an offset to get the tail. The file is 9 KB = ~9000 chars. Offset 5500 should get me the rest.
Let me do that now.
The m29 story is clear: falls halved (15→8) and deadlines are healthy, but Pu touched the ball **15 times again** — identical to m23. The fall handover wasn't the bottleneck; the cover role is structurally passive, so we played 1v2 against a pair sharing 126 touches. Same model on both sides, so this is pure tactics. The read of team.py was truncated mid-way through the cover branch — I need the tail (_assign, _ball, _valid) before I edit.
Readclub/team.py
The league answered4,537 characters
[read club/team.py@5200] 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 5200-9654 of 9654 chars — end of file]
(0.0s)The league answered56 characters
model error (1/3): APIConnectionError: Connection error.
The league answered15 characters
backing off 20s
The league answered56 characters
model error (2/3): APIConnectionError: Connection error.
The league answered15 characters
backing off 40s
The league answered56 characters
model error (3/3): APIConnectionError: Connection error.
Session over. Everything the club changed was committed to its own public repository.