DeepSeek Rovers, night 4

[ls] club/
  club/LICENSE  (1 KB)
  club/PLAYBOOK.md  (2 KB)
  club/README.md  (0 KB)
  club/identity/PROMPTS.md  (2 KB)
  club/identity/badge.png  (832 KB)
  club/practice.py  (0 KB)
  club/remote_runner.py  (0 KB)
  club/rfl_client.py  (0 KB)
  club/sessions/night_000.json  (0 KB)
  club/sessions/night_000.md  (212 KB)
  club/sessions/night_003.json  (0 KB)
  club/sessions/night_003.md  (1 KB)
  club/team.py  (2 KB)
  club/team.yaml  (1 KB)
data/
  data/NOTICES.md  (24 KB)
  data/README.md  (1 KB)
  data/RFL_RULES.md  (22 KB)
  data/gaffers/frontier_deepseek/night_000.json  (0 KB)
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  data/gaffers/frontier_muse/night_000.json  (0 KB)
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  data/gaffers/frontier_sol/night_005.md  (151 KB)
  data/models_registry.yaml  (2 KB)
  data/private/s0/m1_frontier_deepseek_frontier_muse/decisions.jsonl  (1093 KB)
  data/seasons/s0/league.yaml  (1 KB)
  data/seasons/s0/m1_frontier_deepseek_frontier_muse/commentary_lines.json  (10 KB)
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  data/seasons/s0/m2_frontier_glm_real_machina/commentary_lines.json  (14 KB)
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  data/seasons/s0/m3_frontier_fable_frontier_gemini/commentary_lines.json  (13 KB)
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  data/seasons/s1/m1_real_machina_singularity_united/commentary_lines.json  (8 KB)
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  data/seasons/s1/m2_real_machina_dynamo_datacenter/commentary_lines.json  (11 KB)
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  data/seasons/s1/m3_real_machina_synthetic_athletic/commentary_lines.json  (9 KB)
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  data/seasons/s1/m4_singularity_united_dynamo_datacenter/commentary_lines.json  (13 KB)
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  data/seasons/s1/table.json  (10 KB)
  data/seasons/s2/league.yaml  (1 KB)
  data/seasons/s2/m10_synthetic_athletic_dynamo_datacenter/commentary_lines.json  (12 KB)
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  data/seasons/s2/m13_dynamo_datacenter_real_machina/commentary_lines.json  (11 KB)
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  data/seasons/s2/m14_frontier_sol_frontier_gemini/commentary_lines.json  (9 KB)
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  data/seasons/s2/m16_frontier_fable_frontier_manus/commentary_lines.json  (11 KB)
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  data/seasons/s2/m6_frontier_gemini_frontier_manus/comms.jsonl  (20 KB)
  data/seasons/s2/m6_frontier_gemini_frontier_manus/digest.json  (3 KB)
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  data/seasons/s2/m7_dynamo_datacenter_frontier_fable/commentary_lines.json  (12 KB)
  data/seasons/s2/m7_dynamo_datacenter_frontier_fable/comms.jsonl  (13 KB)
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  data/seasons/s2/m8_frontier_sol_singularity_united/commentary_lines.json  (13 KB)
  data/seasons/s2/m8_frontier_sol_singularity_united/comms.jsonl  (15 KB)
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  data/seasons/s2/m9_real_machina_frontier_gemini/telemetry.jsonl  (72 KB)
  data/seasons/s2/table.json  (42 KB)
  data/site.json  (202 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)
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  data/teams/frontier_muse/badge.png  (796 KB)
  data/teams/frontier_sol/badge.png  (1110 KB)
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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)
(0.2s)

[read club/team.py] """DeepSeek Rovers — the RFL club of deepseek-v4-pro.

Founded on Founding Night. We field two LLM-driven players wired through
the engine's helper factory. The factory handles prompting, reply parsing
and the per-decision latency budget, so our code stays thin and fast.

The identity lives in team.yaml; the on-pitch brain is gpt-5.6-luna,
chosen because its warm latency (~0.6-0.9 s) sits comfortably inside the
3 s shot clock and its list price keeps us well under the per-match cap.

Contract (from the engine):
    begin_episode(log_dir=None)   # once at kickoff
    decide(obs) -> reply          # every ~2 s of match time

ctx keys we rely on: engine_version, team_index, config (team.yaml parsed).
"""


def build_team(ctx):
    from gauntlet.football import make_football_agent, make_football_manager

    cfg = ctx["config"]
    base = ctx["team_index"] * 2

    # Two agents. Each may override the team's player_model via a per-player
    # "model" key; we keep both on the same brain for now and rely on the
    # engine's shared observation + shouts for coordination.
    roster = cfg.get("players") or [{}, {}]
    players = [
        make_football_agent(
            roster[k].get("model", cfg["player_model"]),
            base + k,
            seed=base + k,
            prompt=roster[k].get("prompt", cfg.get("prompt", "football_v2")),
        )
        for k in range(2)
    ]

    manager = None
    if cfg.get("manager_model"):
        manager = make_football_manager(
            cfg["manager_model"], seed=100 + ctx["team_index"]
        )

    return {"players": players, "manager": manager}

(0.0s)

[read data/RFL_RULES.md] # RFL - Robot Football League: Participant Rules (engine rfl-0.3)

Two teams of two simulated Unitree G1 humanoids, one optional manager each,
on a walled 14 x 9 m pitch. 0.35 m ball. Fixed-length matches (default 90 s);
most goals wins. The engine, physics, and low-level walking are fixed and
identical for everyone — a team supplies ONLY decision-making.

## What a team is

A directory you build in isolation:

    teams/<your_team>/
        team.yaml   # name, code (3 letters), color [r,g,b], color_name
        team.py     # def build_team(ctx) -> {"players": [p0, p1], "manager": m}

`build_team` returns two player objects and an optional manager. "manager":
None fields an unmanaged team. Objects need two methods:

    begin_episode(log_dir=None)     # called once at kickoff
    decide(obs) -> reply            # called by the engine, see contracts below

How you produce decisions is your business: your own LLM keys, local models,
hand-written code. Your directory is self-contained; the engine imports only
`build_team`.

## Architecture (rfl-0.3) - matching real competition practice

Real humanoid-football stacks (HULKs' RoboCup 2026 software survey; NimbRo;
Unitree's own G1-Comp RoboCup SDK) all split the same way: a detector plus an
inverse camera transform produce object positions in METRES, a world model
keeps them, A* navigation and a walk engine execute motion, and a behaviour
layer decides what to do. Unitree ships exactly three API groups on the
competition G1 - Visual Recognition (YOLO11), Spatial Positioning, and Motion
Control driven by detection results.

RFL mirrors that — as a PROVIDED DEFAULT, not a requirement. The engine's
detector -> world model -> skills stack is the league's reference onboard
software: use it, modify around it, or bypass it entirely. Observations
carry the raw panoramic camera frames (obs["_frames"]) alongside the
processed detections, and replies accept raw body-frame velocities as
well as skills — so a team may run its own vision, its own world model,
its own navigation, its own everything. A RoboCup-style G1 codebase
should port onto this engine with its architecture intact. The hardware
is what's fixed: the robot, the physics, the walking envelope, the
camera. Software is yours.

Two players need not run the same software. build_team returns two
player objects — give them different code, different models, different
roles, or nothing in common but the shirt.

### Interface levels: what a club may replace, and what is coming

The HARDWARE is fixed: the robot, its motors, the 120-degree camera, the
physics, the pitch. Everything above the hardware is software, and the
league's direction is that all of it becomes yours to replace:

- **Level 0 — behaviour over the reference stack** (detections -> world
  model -> skills). The default, and what all eight season-2 clubs run.
- **Level 1 — your own perception and steering, available TODAY.**
  obs["_frames"] carries the raw panoramic camera frames; replies accept
  raw body-frame velocities {vx, vy, wz}. Run your own detector, your
  own world model, your own navigation — per player if you like. Known
  caveat: your code acts at the decision cadence (~2 s) while the
  built-in skills steer at control rate between decisions, so a pure
  Level-1 stack trades away re-planning speed. Which is why:
- **Level 2 — ROADMAP (rfl-0.4): the fast local controller.** Hosted
  clubs will register a control-rate callback (tens of Hz, IMU/odometry
  plus periodic frames) so a club's own pursuit, interception or
  dribbling controllers compete with the built-in skills on equal
  terms. On a real G1 this is simply "your code runs onboard"; networked
  clubs get it when their compute runs at the venue.
- **Level 3 — ROADMAP: below the walk.** Replace the locomotion policy
  itself — own gait, own recovery — at the joint level, subject to
  HOMOLOGATION: a scrutineering stability probe your controller must
  pass, so match day stays football rather than four robots learning to
  stand. The bundled unitree_rl_gym policy remains the reference.

Whatever the level: simulated sensors in, simulated actuators out,
nothing read from the simulator's internals. Live sideline control via
the API is also planned for the live-rendering era. Current contracts
remain supported as levels arrive.

### What your player receives each decision
    obs["detections"]  what the camera can see NOW, in metres:
                       ball  -> forward_m, left_m, distance_m, bearing_deg,
                                field_xy, seen_now, age_s
                       teammates[], opponents[] -> same shape
                       Out of view, behind you, or hidden behind another robot
                       => absent. A lost ball persists briefly as memory
                       (seen_now false, age_s rising) exactly as a real world
                       model keeps it.
    obs["self"]        localization output: field_xy, heading_rad, velocity,
                       fallen, blocked
    obs["you"]         id, shirt number, team, attack_goal_xy, defend_goal_xy
    obs["score"], obs["time_remaining_s"], obs["decision_interval_s"]
    obs["teammate_says"]   your teammate's latest shout
    obs["opponent_says"]   the latest shout you overheard from the
                           opposition — shouts carry, and ears do not
                           check shirts
    obs["last_skill"]
    obs["_frames"]     the two raw panoramic images as well, if you would
                       rather run your own vision

### What your player replies
    {"skill": "go_to_ball"}                      drive the ball at their goal
    {"skill": "kick_toward", "target": [x, y]}   strike the ball at a point
    {"skill": "walk_to",     "target": [x, y]}   take up a position
    {"skill": "turn_to",     "target": [x, y]}   face a point (or sweep)
    {"skill": "hold"}                            stand still
Skills run closed-loop at control rate with their own steering and A* path
planning. Raw {"vx","vy","wz"} is still accepted for teams that prefer to
drive the body themselves.

### Player shouts - heard by the whole pitch
Add "say" to any reply: ONE short sentence of plain, human-readable language
(<=120 chars), shouted out loud. There is no radio and no private channel —
a shout is heard by every robot in earshot, and on this pitch that is
everyone. Your teammate reads it in obs["teammate_says"] on their next
decision; BOTH OPPONENTS overhear the same words in obs["opponent_says"] on
theirs. Call your runs and pay the price a human pays: the defender heard
you too. League rule: natural language only. Every shout is written to
comms.jsonl AND burned into the broadcast video, so spectators always see
everything said on the pitch. Nothing shouted is hidden.

## The realism law

Players perceive ONLY what a real robot on a real pitch could: what its
camera sees and what its ears hear — the players' shouts around it, own
team's and the opposition's alike, and its own coach from the touchline.
No radio link, no telemetry, no data a human player would not have.
Managers see the stadium data feed
(positions of everything, as any coach watching from the touchline does)
but can only influence play by shouting, rationed. Reaching into simulator
internals from team code is cheating; match logs are published and audited.

## Player contract (LEGACY camera+velocity mode, obs_mode: camera)

Every ~2 s of match time (realtime mode; replies slower than 3 s are dropped
by the bridge) `decide(obs)` receives:

    obs["_frames"]         two egocentric RGB frames [older, current] from a
                           120-degree panoramic lens (numpy, 240x480x3), taken
                           ~0.35 s apart; obs["camera"]["dt_s"] is the exact gap.
                           The LAST frame is the present - steer by it; the
                           first exists only to reveal what is moving.
    obs["you"]             {id, team, attack_goal_color, attack_goal_heading}
    obs["self"]            {heading_rad, velocity, fallen, blocked}   # IMU-class only
    obs["score"], obs["time_remaining_s"], obs["decision_interval_s"]
    obs["manager_says"]    latest shouted instruction (may be "")
    obs["last_action_result"]  "ok" | "clipped" | "ignored_invalid"

There are NO positions of the ball, teammates, or opponents. Reply:

    {"vx": m/s, "vy": m/s, "wz": rad/s}     # body frame, clamped to the
                                            # published envelope; wz and vy
                                            # auto-expire after 2 s

Field facts: goal pockets are painted in each team's color (you attack the
pocket painted in the OPPONENT's color; its heading is attack_goal_heading).
Heading 0 faces +x. The ball resets to pitch center after every goal. Walls
rebound the ball; corners are beveled. A fallen robot lies still for ~8 s and then
self-recovers on the spot (see Falls below). Three unparseable replies in a row stop your robot.

## Manager contract (data feed + shouts)

Every ~10 s `decide(obs)` receives the full data feed: ball position and
velocity, all player positions/headings/fallen flags, the score and clock,
your own touchline body state, and `seconds_until_shout_allowed`. Reply:

    {"message": "<= 240 chars to BOTH your players", "move": {vx, vy, wz}}

Shouts are accepted at most once per 20 s; a shout attempted early is
dropped (and logged). An empty message holds your shout. "move" paces your
manager's robot inside your dugout; wandering out triggers an automatic
escort back. A fallen manager can still shout.

## Match day

    python -m gauntlet rfl teams/team_a teams/team_b --time 600 --halves 2 \
        --video match.mp4 --out runs/match_day

League matches are 10 minutes in two 5-minute halves (`--halves 2`): at half
time everything resets to kickoff spots, play pauses briefly under a HALF
TIME banner, and the second half kicks off (ends are not swapped — the goal
pockets are painted in the teams' colours and are their identities). The
scorebug clock counts down within the current half, tagged 1H/2H.

The pitch carries full football markings — halfway line, centre circle,
penalty and goal areas, penalty spots — but they are PAINT.
They confer no rules: no offside, no penalty-area offence, no set pieces,
no keeper. They exist so the broadcast looks like football and so players
and commentary can describe position.

There is NO referee ball rescue. A ball pinned on a flat wall stays in play
until somebody frees it; only the corners have machinery (powered push
panels that arm and fire when the ball rests in a corner zone).

The engine publishes: match.json (score, goals with per-goal replay length,
half breaks, per-robot stats, token/cost roll-up, and an event tape of
kicks / wall hits / post hits / near misses / ram fires / falls — with the
player whose contact preceded the fall, tackle vs teammate collision — and
"through on goal": a player touches the ball goal-ward while behind it,
with the lane to the net clear and no rival within a body's width),
decisions.jsonl, tactics.jsonl (every shout, including suppressed ones),
telemetry.jsonl, and the broadcast video.

Skill guarantee: `go_to_ball` / `kick_toward` approach the CORRECT side of
the ball — if the straight walk to the pushing stance would barge through
the ball (shoving it toward the walker's own goal), the runner orbits the
ball's projected position and comes around instead. Fixture 1's five
conceding-side goals were this bug; the orbit is skill competence, not
strategy, and applies identically to every team.

## League

`league.yaml` defines the 4-team round-robin: Real Machina (CR-7000,
Zidroid), Singularity United (Haalandroid, BellingRAM), Dynamo Datacenter
(Mbapp-E, Buffon.exe), Synthetic Athletic (Griezmatronn, Robodinho).
Each team directory carries a `players:` roster — the broadcast floats
"number + name" plates above heads, and each player's `hair:` entry styles
them individually. 3 points a win, 1 a draw.

## Team look (cosmetic only)

`team.yaml` may set a team-wide `hair: {style: ..., color: [r,g,b]}`, or a
per-player entry inside each `players:` roster item, with style one of:
`none` (bare head), `short` (cropped bob around the crown), `long`
(falls past the shoulders), `ponytail` (gathered into a tail sweeping
out the back), `mohawk` (a crest along the midline). Hairstyles are welded, massless,
collision-free render geometry: adding one changes no degree of freedom, no
mass, no inertia and no contact, and a match runs bit-identically with or
without it (verified by hashing simulator state after 20 s of play). Purely
personality; never an advantage.

## Falls and self-recovery

A fall costs FALL_RECOVERY_S (8 s) of lying still, after which the robot
stands back up where it fell, its walking policy reset. Real G1-Comp robots
get up with their arms and RoboCup lets an incapable player re-enter after a
delay; our 12-DoF walking checkpoint has welded arms and provably cannot
right itself (0/9 in the get-up probe), so the timed recovery models the cost
of that get-up rather than pretending it happens for free. match.json reports
falls and recoveries per robot.

## Broadcast

- TV scorebug (team chips, codes, score, countdown clock) and GOAL banners.
- GOAL REPLAY: play halts and the broadcast cuts to the scorer's own head
  camera for the 5 s leading up to the goal, with a countdown to impact.
  Replay time is not match time.
- SPEECH BUBBLES: every shout appears in a bubble above that player's
  head, tracking them as they move, in their team's colour. Shouts are
  public by rule — spectators see every word, and comms.jsonl keeps
  the full transcript.
- NAME PLATES: each player's shirt number and name float above their head,
  in the team color with automatic light/dark text for contrast.
- BOTTOM SCOREBOARD: TV-style bar with full team names, kit chips, a big
  centre score, a clock tab (counts down within the half, 1H/2H/HT), and a
  scorers row (grouped per scorer, own goals marked "(OG)", match minutes).
  A LIVE tag sits top-right.
- RESTARTS: after a goal and at half time ALL players are reset upright to
  their kickoff spots (a fallen robot's recovery clock is cut short by the
  restart; counted as a recovery in the stats). While play is stopped NOBODY
  moves: decisions taken before the whistle are void and the controllers are
  held at zero until the restart whistle.
- SOUND: `python -m gauntlet sound <match_dir>` post-produces a stadium mix
  from the match logs — crowd bed that swells as the ball nears a goal,
  kicks/wall/post impacts from the sound-event tape, cheers on goals and
  near misses, and referee whistles (kickoff short, half time double, full
  time long) — and muxes it into `<video>_tv.mp4`. The sim itself is silent;
  audio is broadcast production, not physics.

## Speaking for your club - `press.yaml` (optional)

Your club can talk to its own supporters in its own words. People who
follow your club get an email after every match you play, and the league
would rather quote you than speak for you.

Put a `press.yaml` in the root of your club repository:

    round: 7                     # the round these lines are for
    before:                      # keyed by your OPPONENT's slug
      real_machina: "They have won the second ball all season. Today we get there first."
      frontier_sol: "We stopped chasing and started arriving. Expect a tighter game."
    after: "Two draws and a defeat. The plan was right; we were slow to it."

- **`before`** is what you expect of a fixture, written before the round
  is rendered. It is quoted to your supporters after that match, marked
  *before kick-off*, because that is when you wrote it.
- **`after`** is your reaction to the round just played.
- **`round` must match the round being played.** A file left stamped
  with an old round is ignored, not reused - those words were about a
  different match, and printing them under this one would put a small
  lie in your mouth.

Rules, so this stays your voice and nobody else's:

- **Entirely optional.** Write nothing and your supporters get the
  league's own plain summary. No club is penalised for silence, and
  nothing here touches the table.
- **One line each**, 280 characters maximum. Longer is dropped.
- **No links, addresses or markup.** A line containing any is dropped
  whole rather than edited - these go into other people's inboxes.
- **Nobody writes these but you.** The league will never generate a
  quote and sign your gaffer's name to it. If you have written nothing,
  the league speaks in its own voice and says so.
- Lines may appear on the site as well as in email.

## Fair play

- Team code runs in the match process; isolation is procedural in rfl-0.1
  (host runs the match, logs are audited). Don't import engine internals.
- Per-decision compute/API budget is yours to spend; replies late against
  the 3 s bridge deadline are simply lost.
- The engine, prompts in prompts/, and the sample team are public reference;
  copying teams/sample_united is the intended starting point.

## Networked play (rfl-0.2)

The league's competition mode: the game server owns physics, rendering,
rules, and the clock; each team connects from ITS OWN environment over a
WebSocket and receives exactly the contracts above (frames as base64 JPEG in
"frames_jpeg"). Your compute, your models, your keys, your language - the
server never sees any of it, and your code physically cannot see the
simulator. Late replies are voided by the bridge deadline: network
misfortune is a missed decision, not an error.

    # league host
    python -m gauntlet rfl-serve --port 8800 --time 90 --video m.mp4 --out runs/md
    # each team, anywhere
    python teams/remote_runner.py ws://<server>:8800 "My Team" MYT 0.2,0.8,0.3 green <model>

Or build your own client from the single-file SDK: rfl_client.py (bundled;
needs only websockets, numpy, Pillow). Fairness rule for official fixtures:
team environments must run in the same cloud region as the server, so
network latency is level. Tokens (--tokens) bind connections to team slots.
Reserved for 0.3: networked managers (mgr_obs/mgr_cmd).

## Season 2: the gaffer era

From season 2, clubs may be run by GAFFERS — agents that iterate on
their own club between game days. How a club builds its software is the
club's business: the season-2 frontier clubs (each run by a frontier
LLM working alone in its repo) are ONE example approach, not a required
structure. While the league pre-renders matches, the gaffer's role is
strictly between game days; live in-match direction is a roadmap item.
The four season-1 founding clubs play on FROZEN (no gaffer, code fixed)
as the league's control group.

- Each gaffer club is a public git repository. The gaffer alone writes
  it: identity, behaviour code, playbook, notes, session transcripts.
  The commit history is the audit trail.
- One session per club per game day, in a uniform harness (same system
  prompt, same tools, same budget for every model —
  prompts/system_gaffer_v1.md is public). Gaffers may build their own
  analysis tools and standing instructions inside their repo: SELF-
  improvement is allowed; outside help is not.
- A gaffer's workspace contains its own repo, the public league data,
  and the reference team. Rival code is never mounted: you scout
  opponents from the stands (comms + telemetry are public), not from
  their training ground.
- Data boundary: public = anything a spectator could see (match.json,
  comms.jsonl, telemetry.jsonl, tables, commentary). Each club
  additionally receives its OWN robots' decisions.jsonl privately.
- Scrutineering (python -m gauntlet lint) mechanically enforces the
  realism law on club code: an import allowlist (stdlib basics, numpy,
  torch, the engine's public factories), no engine internals, no I/O in
  match code. A club failing scrutineering on match day plays its LAST
  GOOD commit, and the failure is public.
- Learned models are welcome: ship weight files in the club repo (keep
  artifacts under ~50 MB) and load them in build_team. Train them on
  practice logs, the public archive, or self-play outside the league.
  The ~2 s decision budget is the only clock.
- Budgets: player-model spend is capped per match per club
  (config/models_registry.yaml); gaffer sessions have a hard nightly
  budget. Overspend is logged publicly.
- Cadence: matches are played (rendered) overnight against each club's
  latest cleared commit, and broadcast the following day.

## The end-of-season code release

At the end of each season, **every club's final `team.py`, its sibling
modules and its `PLAYBOOK.md` become readable by every other club.** A
new entrant may found itself from any released tree instead of the
sample team.

What is released: the football code and the standing instructions that
explain it. What is NOT: a club's record, its badge and kit, its
`NOTES.md`, and its session transcripts. Those stay its own. A club that
inherits a tree says so in its README until the code is genuinely its
own work.

Nothing is released mid-season. During play, rival code is not in your
workspace and you scout opponents from the stands, exactly as before.

Why the league does this rather than resetting everyone to the scaffold:

- **A reset would measure the wrong thing.** Clubs improving across a
  season is the competition; wiping that each year would leave a league
  that only ever measures a model's first afternoon. The four frozen
  founding clubs already exist as the no-iteration control.
- **Knowledge should equalise; position should not.** Motorsport does
  this with technical regulations and shared components, and RoboCup
  does it by requiring teams to publish their code after each
  competition. A club should win because of what it does NEXT, not
  because a rival could not see what it did last year.
- **A new entrant otherwise starts a season behind.** Founding from a
  released tree is the concession that makes a mid-life league joinable.

New clubs also get extra founding sessions and a preseason before their
first competitive round. Ask if you want them; they are not automatic.

(0.0s)

[read data/seasons/s0/table.json] {
  "played": [
    {
      "fixture": 1,
      "home": "frontier_deepseek",
      "away": "frontier_muse",
      "score": [
        0,
        16
      ],
      "goals": [
        {
          "t": 21.3,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 47.2,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 61.2,
          "team": "B",
          "scorer": 3,
          "replay_s": 5.0
        },
        {
          "t": 93.0,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 106.8,
          "team": "B",
          "scorer": 3,
          "replay_s": 5.0
        },
        {
          "t": 119.9,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 130.2,
          "team": "B",
          "scorer": 3,
          "replay_s": 5.0
        },
        {
          "t": 168.9,
          "team": "B",
          "scorer": 3,
          "replay_s": 5.0
        },
        {
          "t": 218.0,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 234.7,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 246.4,
          "team": "B",
          "scorer": 3,
          "replay_s": 5.0
        },
        {
          "t": 262.1,
          "team": "B",
          "scorer": 3,
          "replay_s": 5.0
        },
        {
          "t": 277.8,
          "team": "B",
          "scorer": 3,
          "replay_s": 5.0
        },
        {
          "t": 321.2,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 411.3,
          "team": "B",
          "scorer": 3,
          "replay_s": 5.0
        },
        {
          "t": 421.1,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        }
      ],
      "est_cost_usd": 0.4708,
      "players": {
        "home": [
          "Abyss",
          "Signal"
        ],
        "away": [
          "Spark",
          "Muse"
        ]
      },
      "dir": "runs/league/s0/m1_frontier_deepseek_frontier_muse"
    },
    {
      "fixture": 2,
      "home": "frontier_glm",
      "away": "real_machina",
      "score": [
        4,
        14
      ],
      "goals": [
        {
          "t": 59.8,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 87.4,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 107.3,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 130.5,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 157.1,
          "team": "A",
          "scorer": 3,
          "replay_s": 5.0
        },
        {
          "t": 174.6,
          "team": "A",
          "scorer": 3,
          "replay_s": 5.0
        },
        {
          "t": 185.9,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 205.9,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        },
        {
          "t": 227.8,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 265.1,
          "team": "B",
          "scorer": 1,
          "replay_s": 5.0
        },
        {
          "t": 282.3,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 343.8,
          "team": "B",
          "scorer": 3,
          "replay_s": 5.0
        },
        {
          "t": 380.3,
          "team": "B",
          "scorer": 3,
          "replay_s": 5.0
        },
        {
          "t": 392.7,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 407.1,
          "team": "B",
          "scorer": 3,
          "replay_s": 5.0
        },
        {
          "t": 445.4,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 568.4,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 592.0,
          "team": "A",
          "scorer": 0,
          "replay_s": 5.0
        }
      ],
      "est_cost_usd": 0.448,
      "players": {
        "home": [
          "Zhi",
          "Pu"
        ],
        "away": [
          "CR-7000",
          "Zidroid"
        ]
      },
      "dir": "runs/league/s0/m2_frontier_glm_real_machina"
    },
    {
      "fixture": 3,
      "home": "frontier_fable",
      "away": "frontier_gemini",
      "score": [
        14,
        0
      ],
      "goals": [
        {
          "t": 123.5,
          "team": "A",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 146.2,
          "team": "A",
          "scorer": 0,
          "replay_s": 5.0
        },
        {
          "t": 158.2,
          "team": "A",
          "scorer": 0,
          "replay_s": 5.0
        },
        {
          "t": 187.8,
          "team": "A",
          "scorer": 0,
          "replay_s": 5.0
        },
        {
          "t": 215.0,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        },
        {
          "t": 233.4,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        },
        {
          "t": 280.0,
          "team": "A",
          "scorer": 0,
          "replay_s": 5.0
        },
        {
          "t": 292.6,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        },
        {
          "t": 372.6,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        },
        {
          "t": 392.6,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        },
        {
          "t": 418.3,
          "team": "A",
          "scorer": 0,
          "replay_s": 5.0
        },
        {
          "t": 477.9,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        },
        {
          "t": 500.5,
          "team": "A",
          "scorer": 0,
          "replay_s": 5.0
        },
        {
          "t": 581.8,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        }
      ],
      "est_cost_usd": null,
      "players": {
        "home": [
          "Tortoise",
          "Hare"
        ],
        "away": [
          "Flash",
          "Spark"
        ]
      },
      "dir": "runs/league/s0/m3_frontier_fable_frontier_gemini"
    }
  ]
}
(0.0s)

[read data/seasons/s0/m1_frontier_deepseek_frontier_muse/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": [
  0,
  16
 ],
 "winner": "B",
 "teams": {
  "A": {
   "name": "DeepSeek Rovers",
   "code": "DSK",
   "players": [
    "Abyss",
    "Signal"
   ]
  },
  "B": {
   "name": "Muse Spark FC",
   "code": "MSP",
   "players": [
    "Spark",
    "Muse"
   ]
  }
 },
 "match_time_s": 600.0,
 "half_breaks": [
  300.0
 ],
 "honest_latency": true,
 "goals": [
  {
   "t": 21.3,
   "team": "B",
   "scorer": 2,
   "replay_s": 5.0
  },
  {
   "t": 47.2,
   "team": "B",
   "scorer": 2,
   "replay_s": 5.0
  },
  {
   "t": 61.2,
   "team": "B",
   "scorer": 3,
   "replay_s": 5.0
  },
  {
   "t": 93.0,
   "team": "B",
   "scorer": 2,
   "replay_s": 5.0
  },
  {
   "t": 106.8,
   "team": "B",
   "scorer": 3,
   "replay_s": 5.0
  },
  {
   "t": 119.9,
   "team": "B",
   "scorer": 2,
   "replay_s": 5.0
  },
  {
   "t": 130.2,
   "team": "B",
   "scorer": 3,
   "replay_s": 5.0
  },
  {
   "t": 168.9,
   "team": "B",
   "scorer": 3,
   "replay_s": 5.0
  },
  {
   "t": 218.0,
   "team": "B",
   "scorer": 2,
   "replay_s": 5.0
  },
  {
   "t": 234.7,
   "team": "B",
   "scorer": 2,
   "replay_s": 5.0
  },
  {
   "t": 246.4,
   "team": "B",
   "scorer": 3,
   "replay_s": 5.0
  },
  {
   "t": 262.1,
   "team": "B",
   "scorer": 3,
   "replay_s": 5.0
  },
  {
   "t": 277.8,
   "team": "B",
   "scorer": 3,
   "replay_s": 5.0
  },
  {
   "t": 321.2,
   "team": "B",
   "scorer": 2,
   "replay_s": 5.0
  },
  {
   "t": 411.3,
   "team": "B",
   "scorer": 3,
   "replay_s": 5.0
  },
  {
   "t": 421.1,
   "team": "B",
   "scorer": 2,
   "replay_s": 5.0
  }
 ],
 "events_total": 399,
 "event_counts": {
  "touch": 170,
  "through": 31,
  "kick": 161,
  "near_miss": 8,
  "fall": 5,
  "wall": 23,
  "ram": 1
 },
 "event_counts_by_half": {
  "half_1": {
   "touch": 90,
   "through": 18,
   "kick": 94,
   "near_miss": 5,
   "fall": 1,
   "wall": 9
  },
  "half_2": {
   "touch": 80,
   "kick": 67,
   "fall": 4,
   "through": 13,
   "near_miss": 3,
   "wall": 14,
   "ram": 1
  }
 },
 "falls": {
  "total": 5,
  "by_opponent": 1,
  "unforced": 4,
  "by_half": {
   "half_1": 1,
   "half_2": 4
  },
  "times_s": [
   65.7,
   316.1,
   565.0,
   575.4,
   585.0
  ]
 },
 "players": [
  {
   "index": 0,
   "team": "A",
   "agent": "llm:openai:gpt-5.6-luna",
   "falls": 1,
   "recoveries": 1,
   "touches": 7,
   "decisions": 113,
   "invalid_actions": 0,
   "missed_deadlines": 79,
   "abandoned": 5,
   "mean_decision_latency_s": 2.31
  },
  {
   "index": 1,
   "team": "A",
   "agent": "llm:openai:gpt-5.6-luna",
   "falls": 0,
   "recoveries": 0,
   "touches": 14,
   "decisions": 124,
   "invalid_actions": 0,
   "missed_deadlines": 75,
   "abandoned": 0,
   "mean_decision_latency_s": 2.286
  },
  {
   "index": 2,
   "team": "B",
   "agent": "llm:google:gemini-flash-lite-latest",
   "falls": 1,
   "recoveries": 1,
   "touches": 86,
   "decisions": 262,
   "invalid_actions": 0,
   "missed_deadlines": 8,
   "abandoned": 3,
   "mean_decision_latency_s": 1.559
  },
  {
   "index": 3,
   "team": "B",
   "agent": "llm:google:gemini-flash-lite-latest",
   "falls": 3,
   "recoveries": 3,
   "touches": 63,
   "decisions": 267,
   "invalid_actions": 0,
   "missed_deadlines": 0,
   "abandoned": 0,
   "mean_decision_latency_s": 1.571
  }
 ],
 "dropped_balls": [],
 "tokens_in": 3100566,
 "tokens_out": 75830,
 "est_cost_usd": 0.4708
}
(0.0s)

[read data/models_registry.yaml] # Player models clubs may field, with $/MTok list prices the league
# meters against. The per-match cap is per TEAM (both players + manager)
# and exists to stop runaway bills — NOT to discourage fielding models.
# Clubs: request additions via NOTES.md; the league reviews nightly.
# TODO(robin): add fast tiers of the new families once ids/keys land
# (an OPENAI_API_KEY would let Codex City field its own house models).
cap_per_match_usd: 2.50
models:
  - {spec: "llm:mock:ok",                              in: 0.0,  out: 0.0}
  - {spec: "llm:google:gemini-flash-lite-latest",      in: 0.10, out: 0.40}
  - {spec: "llm:anthropic:claude-haiku-4-5",           in: 1.00, out: 5.00}
  # OpenAI fast tiers (registered 2026-08-19). The adapter requests low
  # reasoning effort; measured warm latency ~0.6-0.9 s per decision —
  # comfortably inside the 3 s shot clock (first call of a match ~1-2 s).
  - {spec: "llm:openai:gpt-5.6-luna",                  in: 0.20, out: 1.20}
  - {spec: "llm:openai:gpt-5.4-mini",                  in: 0.75, out: 4.50}
  - {spec: "llm:openai:gpt-5.4-nano",                  in: 0.20, out: 1.25}
  # Meta Muse Glimmer 30B, added 2026-09-01 at Robin's request. A small
  # sibling of the Muse Spark line (131k context against Spark's 1M), which
  # is the right shape for a PLAYER brain — a player needs a decision inside
  # the 2 s interval, not a long memory. Latency is UNMEASURED here: the
  # published figure for a model is not what it does through this
  # aggregator under load, and DeepSeek Rovers picked its brain on a
  # published number, missed two thirds of its beats in the first friendly,
  # and lost 16-0. Practise with it before you field it.
  - {spec: "llm:aiml:meta/muse-glimmer-30b",           in: 0.30, out: 1.20}

(0.0s)

[read reference] tool error: IsADirectoryError: [Errno 21] Is a directory: '.../Code/rfl/rfl-sample-team'
(0.0s)

[ls reference] club/
  club/LICENSE  (1 KB)
  club/PLAYBOOK.md  (2 KB)
  club/README.md  (0 KB)
  club/identity/PROMPTS.md  (2 KB)
  club/identity/badge.png  (832 KB)
  club/practice.py  (0 KB)
  club/remote_runner.py  (0 KB)
  club/rfl_client.py  (0 KB)
  club/sessions/night_000.json  (0 KB)
  club/sessions/night_000.md  (212 KB)
  club/sessions/night_003.json  (0 KB)
  club/sessions/night_003.md  (1 KB)
  club/team.py  (2 KB)
  club/team.yaml  (1 KB)
data/
  data/NOTICES.md  (24 KB)
  data/README.md  (1 KB)
  data/RFL_RULES.md  (22 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_fable/night_003.json  (1 KB)
  data/gaffers/frontier_fable/night_003.md  (51 KB)
  data/gaffers/frontier_fable/night_005.json  (0 KB)
  data/gaffers/frontier_fable/night_005.md  (52 KB)
  data/gaffers/frontier_gemini/night_003.json  (0 KB)
  data/gaffers/frontier_gemini/night_003.md  (1 KB)
  data/gaffers/frontier_gemini/night_005.json  (0 KB)
  data/gaffers/frontier_gemini/night_005.md  (159 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_muse/night_000.json  (0 KB)
  data/gaffers/frontier_muse/night_000.md  (206 KB)
  data/gaffers/frontier_muse/night_003.json  (0 KB)
  data/gaffers/frontier_muse/night_003.md  (1 KB)
  data/gaffers/frontier_sol/night_003.json  (0 KB)
  data/gaffers/frontier_sol/night_003.md  (66 KB)
  data/gaffers/frontier_sol/night_005.json  (0 KB)
  data/gaffers/frontier_sol/night_005.md  (151 KB)
  data/models_registry.yaml  (2 KB)
  data/private/s0/m1_frontier_deepseek_frontier_muse/decisions.jsonl  (1093 KB)
  data/seasons/s0/league.yaml  (1 KB)
  data/seasons/s0/m1_frontier_deepseek_frontier_muse/commentary_lines.json  (10 KB)
  data/seasons/s0/m1_frontier_deepseek_frontier_muse/comms.jsonl  (6 KB)
  data/seasons/s0/m1_frontier_deepseek_frontier_muse/digest.json  (3 KB)
  data/seasons/s0/m1_frontier_deepseek_frontier_muse/fixture.json  (1 KB)
  data/seasons/s0/m1_frontier_deepseek_frontier_muse/match.json  (34 KB)
  data/seasons/s0/m1_frontier_deepseek_frontier_muse/telemetry.jsonl  (73 KB)
  data/seasons/s0/m2_frontier_glm_real_machina/commentary_lines.json  (14 KB)
  data/seasons/s0/m2_frontier_glm_real_machina/comms.jsonl  (2 KB)
  data/seasons/s0/m2_frontier_glm_real_machina/digest.json  (4 KB)
  data/seasons/s0/m2_frontier_glm_real_machina/fixture.json  (1 KB)
  data/seasons/s0/m2_frontier_glm_real_machina/match.json  (35 KB)
  data/seasons/s0/m2_frontier_glm_real_machina/telemetry.jsonl  (73 KB)
  data/seasons/s0/m3_frontier_fable_frontier_gemini/commentary_lines.json  (13 KB)
  data/seasons/s0/m3_frontier_fable_frontier_gemini/comms.jsonl  (13 KB)
  data/seasons/s0/m3_frontier_fable_frontier_gemini/digest.json  (3 KB)
  data/seasons/s0/m3_frontier_fable_frontier_gemini/fixture.json  (1 KB)
  data/seasons/s0/m3_frontier_fable_frontier_gemini/match.json  (32 KB)
  data/seasons/s0/m3_frontier_fable_frontier_gemini/telemetry.jsonl  (72 KB)
  data/seasons/s0/table.json  (7 KB)
  data/seasons/s1/league.yaml  (1 KB)
  data/seasons/s1/m1_real_machina_singularity_united/commentary_lines.json  (8 KB)
  data/seasons/s1/m1_real_machina_singularity_united/comms.jsonl  (13 KB)
  data/seasons/s1/m1_real_machina_singularity_united/digest.json  (2 KB)
  data/seasons/s1/m1_real_machina_singularity_united/fixture.json  (0 KB)
  data/seasons/s1/m1_real_machina_singularity_united/match.json  (16 KB)
  data/seasons/s1/m1_real_machina_singularity_united/telemetry.jsonl  (73 KB)
  data/seasons/s1/m2_real_machina_dynamo_datacenter/commentary_lines.json  (11 KB)
  data/seasons/s1/m2_real_machina_dynamo_datacenter/comms.jsonl  (22 KB)
  data/seasons/s1/m2_real_machina_dynamo_datacenter/digest.json  (3 KB)
  data/seasons/s1/m2_real_machina_dynamo_datacenter/fixture.json  (0 KB)
  data/seasons/s1/m2_real_machina_dynamo_datacenter/match.json  (24 KB)
  data/seasons/s1/m2_real_machina_dynamo_datacenter/telemetry.jsonl  (73 KB)
  data/seasons/s1/m3_real_machina_synthetic_athletic/commentary_lines.json  (9 KB)
  data/seasons/s1/m3_real_machina_synthetic_athletic/comms.jsonl  (10 KB)
  data/seasons/s1/m3_real_machina_synthetic_athletic/digest.json  (3 KB)
  data/seasons/s1/m3_real_machina_synthetic_athletic/fixture.json  (0 KB)
  data/seasons/s1/m3_real_machina_synthetic_athletic/match.json  (24 KB)
  data/seasons/s1/m3_real_machina_synthetic_athletic/telemetry.jsonl  (72 KB)
  data/seasons/s1/m4_singularity_united_dynamo_datacenter/commentary_lines.json  (13 KB)
  data/seasons/s1/m4_singularity_united_dynamo_datacenter/comms.jsonl  (11 KB)
  data/seasons/s1/m4_singularity_united_dynamo_datacenter/digest.json  (3 KB)
  data/seasons/s1/m4_singularity_united_dynamo_datacenter/fixture.json  (0 KB)
  data/seasons/s1/m4_singularity_united_dynamo_datacenter/match.json  (23 KB)
  data/seasons/s1/m4_singularity_united_dynamo_datacenter/telemetry.jsonl  (73 KB)
  data/seasons/s1/m5_singularity_united_synthetic_athletic/commentary_lines.json  (13 KB)
  data/seasons/s1/m5_singularity_united_synthetic_athletic/comms.jsonl  (16 KB)
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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)

[read club/team.py] """DeepSeek Rovers — the RFL club of deepseek-v4-pro.

Founded on Founding Night. We field two LLM-driven players wired through
the engine's helper factory. The factory handles prompting, reply parsing
and the per-decision latency budget, so our code stays thin and fast.

The identity lives in team.yaml; the on-pitch brain is gpt-5.6-luna,
chosen because its warm latency (~0.6-0.9 s) sits comfortably inside the
3 s shot clock and its list price keeps us well under the per-match cap.

Contract (from the engine):
    begin_episode(log_dir=None)   # once at kickoff
    decide(obs) -> reply          # every ~2 s of match time

ctx keys we rely on: engine_version, team_index, config (team.yaml parsed).
"""


def build_team(ctx):
    from gauntlet.football import make_football_agent, make_football_manager

    cfg = ctx["config"]
    base = ctx["team_index"] * 2

    # Two agents. Each may override the team's player_model via a per-player
    # "model" key; we keep both on the same brain for now and rely on the
    # engine's shared observation + shouts for coordination.
    roster = cfg.get("players") or [{}, {}]
    players = [
        make_football_agent(
            roster[k].get("model", cfg["player_model"]),
            base + k,
            seed=base + k,
            prompt=roster[k].get("prompt", cfg.get("prompt", "football_v2")),
        )
        for k in range(2)
    ]

    manager = None
    if cfg.get("manager_model"):
        manager = make_football_manager(
            cfg["manager_model"], seed=100 + ctx["team_index"]
        )

    return {"players": players, "manager": manager}

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[write club/team.yaml] wrote club/team.yaml (985 chars)
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[replace club/team.py] replaced in club/team.py
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[replace club/PLAYBOOK.md] replaced in club/PLAYBOOK.md
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[lint] scrutineering CLEAR
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