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# Tooling & analysis — guide (loop tier)

Reference notes, design rationale, and negative results (25).

#### 1. Coworld backend wall times, replay-render network needs, and the three identifier namespaces
`loop`

Three Coworld execution backends differ in wall time for the same CvC variant: local play (amd64) ~6:25 at ~26 ticks/sec, league dispatch (in-cluster k8s service-to-service) ~9 min, hosted-game (public-internet proxy) ~14 min at ~12 t/s -- use local for iteration and hosted only to validate the network path. Distinguish three identifier namespaces when mapping a game into local tooling: 'cow_...' is the Coworld id (for 'coworld download' and disk layout), 'league_...' is the league id (memberships/submissions/results), 'game-...' is the project's internal games-row id (library sync); resolve the internal game id from the synced games table by matching on coworld id, name, or league id rather than hardcoding. Stable IDs for guided_bot/Among Them: Among Them Daily league = league_494db37d-d046-4cba-a99a-536b1439262f, its Daily division = div_334593c6-da90-4651-98c7-606573ea1474, the Among Them game = game_8a1c0e5c-512b-4b01-86d2-8a152b4b5aa0. coworld replay/replay-open require INTERNET even for a local file because the local container's /clients/replay returns HTTP 307 to the externally-hosted Mettascope viewer on metta-ai.github.io (airgapped you can curl raw /replay-data but cannot render), and the container never exits cleanly (game_process.wait has no timeout: Ctrl-C, docker stop=143, docker rm -f=137 all non-zero), so wrap with '|| true'. Prefer local materialized manifests over fetching stale remote league replay IDs (which cause 404 replay boot failures).
  <sub>sources: archive/cogames_playground/docs/coworld_cli.md, coworld-source-repos.crewrift-upload/optimizers/.cursor/skills/downloa, players/users/james/personal_cogs/among_them/guided_bot/coworld/INSPEC, players_checkouts/players/users/james/personal_cogs/among_them/guided_</sub>

#### 2. Live Coworld spectator viewer is broken upstream; watch replays instead
`loop` · **negative result**

The live Coworld spectator viewer (/proxy/clients/global and /proxy/global) is broken upstream: it sends one hello frame then closes the TCP connection with code 1006 (the /player path is unaffected so containers still play). Prove it is upstream (not browser/auth) by probing the wss URL directly from Python websockets with no browser/auth and reading the close code; the viewer never auto-reconnects (onclose just sets status='Disconnected') and it builds its WS URL from location.pathname, so loading /client/global.html connects to ws://host/client/global.html instead of ws://host/global. A hosted pod is reachable only in a narrow window (start ~5-15s, episode ~46s with stragglers to ~82s, then shutdown -> 'not ready'); before ready returns 503, after shutdown 404 -- both look like 'viewer broken'. The session API has no live-state endpoint (/state,/info,/replay,/stats,/episode all 404; current_step/max_steps/started_at all None while running), so detect completion by watching for all player containers to exit. Driving a live run-episode is a poor way to WATCH: the sim runs as fast as bots respond (a 10k-tick game wraps in minutes) then the container tears down. To actually watch: save the replay then launch the game image with the replay loaded for true 24fps rewatchable playback (no auto-loop; reload to rewatch). For interactive watching, start the match open-ended and poll /proxy/clients/global until HTTP 200 before handing a human the link.
  <sub>sources: claude-code:0919285d-1374-4827-b596-6e7107d95526, claude-code:1c3aa024-ff20-4a9a-af03-7c68b7ca5068, claude-code:3c13aeb0-5617-452d-a8d7-aff376e3d4ff, claude-code:cd2e0b52-b541-4e03-9fd3-798db4591603 (+2)</sub>

#### 3. Read cogs-vs-clips deaths from episode infos, not position trajectories; account for the MapGen border offset
`loop` · **negative result**

The coworld/cogs-vs-clips replay stores location as SPARSE delta-compressed [tick,[x,y]] (only on change) and inventory as sparse [tick,[[item_id,amount],...]] with fixed item IDs (0=oxygen,1=carbon,2=germanium,3=silicon,4=hp,5=heart,6=energy,7=aligner,8=scrambler,9=miner,10=scout,11=solar). CRITICAL: death/respawn does NOT show as a teleport-back-to-hub or a tick gap, so death-detection keying on hub-jumps or gaps fails silently -- read deaths from the episode infos metrics (and note show-reported per-agent deaths can differ from the infos['agent']['death'] 8-agent average). When converting replay object coordinates to internal spatial-memory coordinates, account for the map border offset: a MapGen border_width of 5 makes the full rendered grid 98x98 around an 88x88 inner grid, so replay [x,y] convert to memory cells as (row=y-5, col=x-5).
  <sub>sources: opencode:ses_1fc6f16cfffe03SKAcP78Ua0IK, opencode:ses_1ff2ef8c8ffej7zC4A18jgSDFv, archive/cogames_playground/bulbacog/designs/MEMORY.md</sub>

#### 4. Hosted/Observatory player logs are a capped stderr TAIL; prefer the artifact zip
`loop` · **negative result**

Hosted/Observatory policy-log endpoints return only a capped TAIL of each container stderr (limits seen: ~4500 lines / ~90s of game time per Coworld agent; ~9,999 for Observatory; ~10,000 for some hosted endpoints), keeping the END, so the START of the game (tick 0, provider resolution, LLM init failures, role assignment, first meeting/vote) may be missing -- do not assume tick 0 is present and a truncated log cannot PROVE a feature never started. A chatty per-tick player fills the window with idle noise and pushes decisive early events off the front. crewborg defaults to jsonl@artifact, uploading its trace as a per-slot zip (policy_artifact_{N}.zip with telemetry.jsonl + manifest.json, ~96k lines/episode of real per-tick decision trace where decision_snapshot.data carries phase/role/mode/intent) that is NOT subject to the log cap, while policy_agent_{N}.log may contain only 'game over' -- fetch and unzip the artifact when both exist. Heavy per-tick belief dumps (decision_snapshot, full suspicion vector) are off by default, so to get them re-run the agent locally. A player log is policy-specific with no shared schema: crewborg writes per-tick JSONL (tunable via CREWBORG_TRACE*), while Nim players (notsus, suspectra) write plain-text human-readable stderr. Mitigations: corroborate startup events against the full-match wire summary, and design the player default trace volume to fit comfortably under the cap.
  <sub>sources: personal_labs/crewrift_lab/crewrift/crewborg/docs/trace-logs.md, personal_labs/crewrift_lab/docs/crewrift-replays.md, personal_labs/crewrift_lab/lessons_archive/TENTATIVE_LESSONS-20260613-, claude-code:d48f9347-7884-4ea2-8659-230051d86410 (+2)</sub>

#### 5. Observability is built, not given: instrument the decision loop BEFORE adding the feature, with separate JSONL streams
`loop`

Before you can fix bad behavior you must usually build the instrument first, so instrument an LLM/agent layer BEFORE adding capability rather than 'wired but under-instrumented'. Treat observability as a first-class phase: emit separate per-frame JSONL streams keyed by concern -- decisions (chosen mode/intent plus decision SOURCE scripted vs reflex vs LLM directive, params, branch ID), mode transitions, reflex firings, game events, LLM/guidance events, periodic full-belief snapshots, categorized verbose types (belief_diff, cooldown_change, minimap_sighting, grid_change, action_memory_mutation), plus a manifest recording start/end tick and outcome. Emit structured JSONL at every well-defined transition (perception, belief_diff, snapshot_submitted, strategy_evaluated, llm_response, mode_entered/exited/stalled/completed, reflex_fired, action_intent, act_command, fallback_activated, validation_rejected, task/report started/completed/abandoned WITH a reason) plus metrics (mode duration, directive age, fallback rate, decide latency, step latency) and typed errors (http|empty_response|parse|validation|stale|context_overflow). Beware blind spots: a transition that emits NO event (e.g. a confirm-timeout abandonment) is invisible offline. Ship the harness WITH the policy, not bolted on later: a live anomaly detector flagging failure classes in real time (stuck agents, death spirals, wasted resources, position oscillation, acting-before-gearing-up), a ring-buffer 'black box' per agent, and an on-demand LLM postmortem investigator, with a headless eval driver (cogames run --format json) as the backbone. Separate streams plus early/mid/late sampling and per-mode time distribution make 'why did the bot do X on frame N' tractable offline; make per-tick tracing opt-in via env/URI params (?trace=1&trace_level=2&trace_agent=0) so normal runs pay no overhead.
  <sub>sources: claude-code:7db8d397-c6dc-47b6-9b95-8f90675dd1b8, opencode:ses_1fb1787afffemhwtF09UMzPc5V, opencode:ses_20422c08ffferRLvWnNE5DeB4w, opencode:ses_21b02c437ffeor4lV5Nb4Cec7y (+11)</sub>

#### 6. Reserve stdout for the wire protocol; route traces to a separate sink, prefer artifact upload; route container logging at entrypoint with force=True
`loop`

Keep stdout reserved for the coworld protocol/wire channel and route all traces/metrics through a separate sink (the SDK TraceOutputs) so logging never corrupts the wire. Prefer uploading the trace as an artifact (e.g. crewborg's jsonl@artifact to COWORLD_PLAYER_ARTIFACT_UPLOAD_URL at exit) to sidestep the hosted per-line log cap, falling back to jsonl@stderr with a warning when no URL is present (a pre-connect crash would otherwise fail the episode). Player containers that configure no Python logging emit zero output, blinding hosted debugging: at the top of entrypoint main() call logging.basicConfig(stream=..., force=True) -- force=True overrides logging already configured by imported libs -- and point the handler at the stream the runner actually scrapes (confirm stdout vs stderr). Make config idempotent/overridable by CLI flag AND env var; guard against double-init. Make the tracer FAIL-OPEN (lazy-open, never crash the live agent), let a trace event's type discriminator WIN over caller payload keys, and verify the trace file EXISTS before relying on it (an OOM exit 137 can kill before any write).
  <sub>sources: personal_labs/crewrift_lab/crewrift/crewborg/AGENTS.md, personal_labs/crewrift_lab/crewrift/crewborg/design.md, players_checkouts/players/archive/players/among_them/coborg/DESIGN.md, claude-code:3c13aeb0-5617-452d-a8d7-aff376e3d4ff (+2)</sub>

#### 7. Align traces to the authoritative server tick and instrument loop drift
`loop`

Align every trace event and metric to the engine's AUTHORITATIVE tick, not the local received-message counter, because the local counter silently lags when the agent falls behind and would misalign forensics/replay tick numbers (crewrift streams its true tick as an invisible 'tick <N>' marker sprite id 5016; drive the runtime from it). Instrument the inner real-time loop with latency/drift metrics tagged to that server tick: step wall-time vs the per-tick budget, gap between frame arrivals, and tick-drift (server_tick - local_tick relative to initial offset; 0 = keeping up, growth = falling behind). Break the per-step budget into named sub-counters for the expensive operations (for a screen-reading bot, localization is costliest: track centering/lock and A* time separately, centerMicros/astarMicros).
  <sub>sources: personal_labs/crewrift_lab/crewrift/crewborg/design.md, bitworld/among_them/players/how_to_make_a_bot.md</sub>

#### 8. Validate every derived number, grep filter, and trace metric against raw output and the real schema
`loop` · **negative result** · ⚠ _session-derived, unverified_

Treat every derived number, filter, and metric as suspect until checked against raw output and the actual schema. Watch event-counts vs tick-counts: a mode's 'count' from an event log can be ~3x inflated vs actual ticks (a 3-tick hunt logged as ~9 events) -- verify durations in TICKS. Do NOT fabricate quantitative numbers: one session invented a '135 second' window and '6.5 fps' throughput and built a 'kicked for falling behind' theory on them; real connect/close timestamps showed ~18.7 fps vs the game's ~24 fps and the theory collapsed. An EpisodeRequest's created_at is QUEUE/scheduling time, not game start -- the real start is the first WebSocket connect of a fully-active opponent slot. A naive metric can read zero even when the feature works: target_switches=0 across all agents looked like a failed fix but the decisions trace simply lacked tc_target while goal_x/goal_y deltas DID show switches; another trace logged waypoint IDs (current_wp: 40) the code looked up by array INDEX, yielding nonsense navigation; an event named like an outcome can mislead (a kill_attempted event fired on target ACQUISITION not the action press, and logged distances 30-68px all above the 20px strike range proved the bot never got in range) -- when a script reports a flat zero or odd values, dump the real schema, confirm the field exists and whether identifiers are IDs or indices, and verify in code what each event means. Before reporting a failure, confirm grep matches for 'error' are real Python stack traces not named telemetry fields (e.g. distance_error in an occupancy model); confirm a grep filter is not hiding the lines you need (a grep -v on '^INFO' silently dropped the player's own connect/disconnect logs and faked a 'no logs' belief); when logs are stored as Python bytes literals, decode/normalize before grepping. Beware stale renders: an old image still captioned 'frame 150/274' can masquerade as fresh -- confirm the output file actually rewrote.
  <sub>sources: claude-code:25488cd9-61d5-4231-bd87-e2744b6db043, claude-code:5f9e8287-da92-4dff-bf39-2511bcaf0700, claude-code:2ed48615-a956-43ec-a15a-b92eb2c8f879, claude-code:e5e23b1b-d4da-478d-b010-bb374c477eec (+6)</sub>

#### 9. Verify trace-capture correctness: per-agent keying, change-sampling, single-game windows, stop at game-over
`loop` · **negative result**

Per-tick trace capture has subtle correctness traps -- verify before trusting any agent-level finding. (1) Record each agent's action DURING the step loop keyed per-agent, not after: a bug reading action_name from the last loop iteration's outer-scope variable made all 8 agents show the same action; check that different agents show varied actions at the same tick, and bump the trace schema version when fixing such a bug. (2) Sample a field every time it CHANGES, not only at moments you care about: a logger sampling active_task_progress_pct only at task-set changes (when progress had reset to null) produced a false 'progress null all game'; logging on every change revealed it ramps 1->98 during a task. (3) Confirm your sampling window covers exactly ONE match: a tail can splice dead/imposter slots from a prior match (a 'crewmates only complete 1-3 of 8 tasks' finding was bad windowing; clean single-game data showed all 8), and STOP counting at the first game-over event because post-game reset behavior inflates counts (a '53/48 tasks' figure was a trace-analysis error from including post-game activity).
  <sub>sources: codex:019de5a5-fadf-70b3-8773-2072cabae94c, codex:claude-code:ff9f693c-b88a-419d-be7a-efc46750dc23, codex:019e1593-c00d-7a32-9dfa-1cf6b16b01a4, personal_cogs/persephone/agents/eurydice/IMPLEMENTATION_PLAN.md (+1)</sub>

#### 10. Split attempt-rate from conversion; suspect one shared timing cause; diagnose move-failure and the production bottleneck before blaming deaths or the partner
`loop`

When diagnosing a 'low output' agent, split ATTEMPT RATE from CONVERSION before assuming a skill/aim problem, and check whether the agent even survived long enough to act. crewborg's imposter under-killed (1.7 vs 2.0) but its trace showed kill_attempted == kills (~100% conversion); it just attempted too rarely (1-3/game vs a ~4-5 cooldown ceiling) because it sat in pretend mode 54-74% of ticks and hunt only 0.1-2.9% -- the fix space was 'attempt more', not 'aim better'. Always pull the per-tick mode-time distribution and an attempts-vs-successes split. One latency/init stall can masquerade as several unrelated bugs: crewborg's ~14s first-tick nav/substrate build under the 250m CPU cap backlogged ~330 frames and was the shared root cause of both 'slow to leave spawn' and a rare early-meeting vote-timeout (a meeting in that catch-up window ran the vote-cursor on a stale cursor and never converged) -- when two odd symptoms cluster in the same early-game window, suspect one shared timing cause first. Move-failure rate is a major performance differentiator and a better diagnostic than death count: the worst tournament match (score 2.02) had 50% move-failure (agents stuck half the time via max-steps-without-motion) while productive matches were under 2%; a 47-point match had 32.5 deaths but only 1.5% move failure -- high deaths with low move-failure is not the bottleneck. In cogs-vs-clips a low score is usually bound by heart production, not mining: check hearts withdrawn vs elements deposited (only 5 hearts in 10k ticks despite ~115/98/95/104 elements, which allowed floor(95/7)=13) before blaming alignment. In a 4+4 split, subtract the partner's contribution (junctions gained 6 minus our aligned 1 = partner aligned 5) before blaming the partner -- a competent-but-weak partner cannot explain a catastrophic 1.64 score, so suspect a systemic failure.
  <sub>sources: personal_labs/crewrift_lab/lessons_archive/TENTATIVE_LESSONS-20260612-, opencode:ses_1fbf248dcffelT9O4xUYaLujGu, opencode:ses_1ffba372effeoVxzZ3X2OpUAcj</sub>

#### 11. Name the failing layer, separate ops from behavior, and ground every finding in evidence anchors
`loop`

Name the failing layer first -- perception, belief, strategy, or execution -- because the layer determines where the fix goes, and keep operations failures (cannot connect/build/crash) strictly separate from behavior failures (connects fine but plays badly); never let a crash pollute behavioral analysis. Ground truth beats inference: verify against the game's authoritative source/logs before building any conclusion, and explicitly separate OBSERVATION (direct evidence) from HYPOTHESIS (inferred bias). Anchor every behavioral finding from a Coworld episode in four things -- the real decoded tick (flag if estimated), the player (slot index and/or name), the event key (e.g. action.kill, vote.cast, lifecycle.game_end), and the source artifact (results.json/replay/policy log/timeline) -- never invent ticks/players/outcomes, identify the single most impactful error and decisive tick rather than cataloguing every mistake, and express improvements as concrete testable IF X THEN Y rules (not 'played poorly') since only specific rules can be implemented and validated.
  <sub>sources: player_labs/best_practices.md, coworld-source-repos.crewrift-upload/optimizers/instructions/mvp/debug, coworld-source-repos.crewrift-upload/optimizers/instructions/mvp/explo</sub>

#### 12. Separate detection/navigation/completion-condition bugs and anchor replay debugging on a frame+bot
`loop` · ⚠ _session-derived, unverified_

When reading game-policy replays, confirm the symptom you fixed is actually fixed and name the remaining DISTINCT problem rather than conflating bug classes -- detection vs navigation vs completion-condition: after a perception fix a bot correctly pruned tasks but then navigated 1000+ frames toward its real task without arriving (a pathing failure, not perception). An action emitted but never producing its effect (a task-interaction looped 28x without completing while a peer completed it once) is a broken completion-condition localized to position/instance/tier, not global action logic -- compare peers to localize. Make this tractable by anchoring on a specific frame plus a specific bot and cross-referencing decisions.jsonl/perception.jsonl at that frame to recover position, camera, goal, radar dots, and visible task icons.
  <sub>sources: opencode:ses_1fecece4dffe779FmeajgdwhMD, opencode:ses_20422c08ffferRLvWnNE5DeB4w, opencode:ses_1fa63b520ffeQP7WMCZiDebllG</sub>

#### 13. Re-check what a replay-derived metric actually measures before treating it as a problem
`loop`

Re-check what a replay-derived metric actually MEASURES before treating it as a problem. A reported '19-107 role switches per match vs 0-1 for top policies' turned out to be inventory oscillations from death-and-respawn, not role-switching instability (role-switching was already prevented); the real bottleneck was the death rate and the apparent role-instability was just a symptom of it.
  <sub>sources: archive/cogames_playground/alpha_cog/experiments/notebook/hypotheses/H</sub>

#### 14. Tournament/league replay analysis is the dominant signal source the outer-loop self-play A/B cannot replace; replay-watching alone is weak
`loop`

Live tournament replay analysis finds signal local self-play A/B structurally cannot: self-play runs your policy against itself or a fixed passive NPC on fixed seeds, while live matches add opponent diversity, agent-count splits (2+6/4+4/6+2 with a partner), and hidden map seeds, exposing interaction effects and novel-geometry failures. Self-play can underestimate a tournament-specific mechanism by up to ~20x (passive NPC vs aggressive real opponents), so when a tournament-targeted mechanism shows a marginal-but-positive self-play signal, ship it rather than chasing a larger self-play signal that cannot exist. Build a tool that downloads every match involving your policy and caches full replays plus structured per-match metadata (game stats, per-agent metrics, partner identity, split config), aggregating by version/split/partner/partner-strength, so the bottleneck becomes hypothesis quality. Run the review on a schedule: after every submission once 10+ competition matches complete (~2-6h post-submit), before a new experiment session, and immediately on a leaderboard drop. Triage: fetch ~20 replays, sort by worst score, look first for split asymmetry and opponent-class vulnerabilities, then watch the worst in MettaScope for stuck agents/lost territory/pre-gear wandering, and only after a failure reproduces locally (same seed, self-play) spend cost on mechanism diagnosis. Diagnose the underlying MECHANISM (the why), not the cascade (the how). But replay-WATCHING alone is a weak strategy signal: watching a top competitor's replays revealed no legibly distinct tactic behind its high placement, so pair replay-watching with statistical distribution analysis rather than inferring strategy from a few replays.
  <sub>sources: alpha_cog/AGENTS.md, archive/cogames_playground/alpha_cog/docs/league-replay-tool-design.md, archive/cogames_playground/alpha_cog/docs/paper/paper.md, opencode:ses_2095d36efffemO4jJhy7HRYYxD (+1)</sub>

#### 15. Tournament replays expose only your agents' positions and often no map seed; plan analysis around that
`loop` · **negative result**

Local reproduction of tournament failures is often blocked because the API does not expose the map seed, so you cannot recreate the exact tournament map or derive a character seed -- plan analysis around diagnosing mechanisms (oscillation traps, pre-gear wandering) and heuristically matching them to known local failure seeds, not local re-simulation. Tournament replays also contain only positions and actions for YOUR agents (not per-tick beliefs/skills/args), so they cannot support programmatic per-tick decision analysis or opponent reverse-engineering -- use replays for triage and visual inspection, rely on local trace reproduction for mechanism diagnosis, and focus on YOUR response to opponent behavior rather than modeling opponent internals. If a failure reproduces in self-play it is a new character seed; if it fails only against the opponent it is interaction-dependent and needs a different hypothesis class.
  <sub>sources: opencode:ses_206bfd18dffe1hUfJBOccuaLDC, archive/cogames_playground/alpha_cog/docs/league-replay-tool-design.md, alpha_cog/AGENTS.md</sub>

#### 16. Use competitor replay analysis to find the largest concrete capability gaps, then test the inferred fix
`loop`

Replay review of stronger competitors localizes the biggest concrete levers, which you then test. For alpha-cog vs top CvC policies it found a ~4x mining-throughput gap (we deposited after every 10-resource bump, 23% cargo utilization, vs top filling to 37-40 at 83-91%), a 48x scrambler gap (no dedicated scramblers), 3.3x slower territory expansion, and late gear-up (slowest agent at tick 497 vs tick 25). Raising the cargo deposit threshold to 30-40 was the single biggest lever, shipped as a +2.59-mean economy improvement; prioritize it first since it cascades into hearts/alignment/score.
  <sub>sources: archive/cogames_playground/alpha_cog/docs/cogs-v-clips-strategy.md, archive/cogames_playground/alpha_cog/experiments/notebook/hypotheses/H, archive/cogames_playground/alpha_cog/experiments/notebook/hypotheses/H, archive/cogames_playground/alpha_cog/experiments/submissions.json</sub>

#### 17. Spot-check a new simulator driver's scores against the established harness before trusting it
`loop` · **negative result**

When writing a new simulator driver (e.g. a tracer that bypasses the normal play CLI), spot-check its scores against the established harness on a known seed BEFORE trusting any output. A first version baked max_steps into the mission at build time, which changed the per-tick reward weight and inflated scores by (full_horizon/steps)x. Fix: build the mission with the default max_steps and mutate it only AFTER environment construction.
  <sub>sources: alpha_cog/AGENTS.md</sub>

#### 18. Build a decoupled, build-chain-free debug viewer that runs the live code path or replays the exact logged trace -- never a drifting reimplementation
`loop` · **negative result** · tool: `debug_server.py`

For a screen-reading/perception bot, build a rich debug viewer FIRST (before complex behavior) as a build-chain-free app decoupled from the agent: either a static browser app (plain HTML/CSS/JS + Canvas loading a JSONL log from disk, no server/npm) or a separate localhost server the agent broadcasts on (e.g. WebSocket 9090, HTTP 9091) -- NOT an in-process GUI (an in-process mainloop fights the agent's asyncio loop, a slow blit starves the bot, a viewer crash kills the bot, and you cannot attach mid-run). Make it zero-cost when unwatched: publish() is sync and never-awaiting into a per-client 1-slot latest-only queue (drop-old, never block), skip encoding with zero clients, give each client its own drain task, drop a client silently on error; for the static-app path emit a compact per-tick viewer_frame plus one-time viewer_map/viewer_occupancy_grid bootstrap records from on_step_complete, gated behind an opt-in env flag (CREWBORG_TRACE=viewer/debug) since the grid is huge at 24 Hz. Feed it from the player's EXISTING trace stream (a thin trace-to-view-model layer beats reconstructing beliefs from sparse domain events); log everything needed to reconstruct a perception update (a per-update perception.jsonl) rather than re-importing another codebase's perception module, which drifts. Draw BOTH internal state (intent, role, camera lock/score, goal, velocity, timings, voting parse) AND visual evidence overlaid on the map (viewport, task rects, lines to expected icons, radar lines, A* path, crewmate/body boxes) -- most serious bugs were only obvious after the right box was drawn. A debug scrubber MUST run the exact live code path or it lies: one running only perception+motion never called decide(), leaving goal/path at defaults so the operator concluded the fix did nothing; replaying captured frames through a fresh core CAN measure decision stability (goal-run count 23->5) but the camera path is fixed by capture, so verify only decision stability not navigation, and follow with a live run. Full-pipeline-per-frame is ~2x slower (~10ms vs ~5ms); mitigate with frame caching or a --frame N flag, never by dropping the real code path; do NOT wholesale-adopt a full episode driver (it starts its own server, loads policies from URIs not a live object, has no per-tick render hook) -- compose its internal helpers. Make the wire format forward-compatible (one JSON message per tick: tick, wall_time, fps, button mask, unpacked frame, a free-form extras bag, an ignorable overlays list; render unknown extras as text and ignore unknown overlay types). Index multi-source overlays consistently (perception/decisions are 1-based tick t, the frame buffer is 0-based -> key by t-1) and bail out of drawing rather than crash on missing data. 'URLs to watch' means HTTP/file URLs, never ws:// (a human can't watch a WebSocket); if a viewer shows a jumbled mess suspect a stale-cached page before blaming the socket. Smoke-test the broadcaster/draw path with a tiny HEADLESS websocket/Node client (pygame fails headless; an early run failed only because the fake canvas lacked measureText) -- `node --check` for syntax, never the Python compiler; explicitly test disconnect-not-disturbing-agent, reconnect, multiple viewers in lock-step, no-port-when-disabled; for a curses TUI register curses.endwin() via atexit so a crash restores the terminal.
  <sub>sources: codex:019e8f04-ae06-7c71-b3fe-f7791c8c8379, claude-code:ee5aaed4-70d2-4597-b012-9d348f4f1e2b, opencode:ses_1ffe75d89ffeHvW4p2ARluHh51, opencode:ses_21fc35ba9ffeK5TywAEvVvlBBl (+7)</sub>

#### 19. Standardize and de-duplicate run-harness scripts into one shared library with uniform CLI flags
`loop` · ⚠ _session-derived, unverified_

Run/utility scripts for the same game accrete inconsistent conventions (duration as --duration vs positional; player count as --num-players/--num-agents/positional; host/name hardcoded vs flagged) -- standardize on one set of named argparse flag groups shared across all scripts, avoid positional-arg shell wrappers, and treat scripts needing PYTHONPATH/envvar exports as a usability smell (prefer CLI flags that set them internally). The same low-level logic (action/button-mask conversion, the WebSocket recv/step/send loop, server-start + filler-spawn, per-tick metrics, JSONL writing, frame-capture-to-.npy) gets copy-pasted everywhere; extract it into one shared library and make each script a thin composition (this collapsed three action-mask/agent-loop/frame-capture implementations and four server-startup implementations into single functions). Decompose into single-purpose composable scripts (server.py that prints host:port and blocks, a generic connect.py, a passive capture.py, an all-Python play_match.py, a play_debug.py with overlay). Don't compute duration as `deadline = monotonic() + duration` -- `--duration 0` then expires immediately; special-case 0/negative as the run-forever sentinel. cogames subprocess output uses rich console writes that buffer differently than plain prints, so a wrapper's header can appear AFTER subprocess output -- capture with capture_output=True and print it after your header.
  <sub>sources: opencode:ses_21b46167dffePyHuN6mEYBKwfS, opencode:ses_21b899dadffeBiSiDW3XWFqEdz, opencode:ses_21a1839daffe5QnSh33ozoq3rB</sub>

#### 20. A long-stuck 'running' episode means inspect pods/clusters, not keep polling
`loop` · ⚠ _session-derived, unverified_

Interrupting a smoke episode leaves a permanently 'running' row in the optimizer DB/UI; mark it failed in the DB before rerunning. More broadly, a long-stuck 'running' job is a signal to inspect, with distinct mechanisms: (1) an episode WEDGES instead of failing when a player container can't start -- the coordinator waits indefinitely for minPlayers while the API still reports 'running'; (2) a reconciler that only runs when the event queue is empty (processed==0) never fires on a continuously-busy cluster, so when the DB says 'running' but the cluster has no matching pods, suspect the reconciler; (3) eval/tournament jobs may run on a DIFFERENT cluster than the control-plane (look for EVAL_CLUSTER_NAME or a cross-account role ARN in the event-processor env), so 'missing' pods on the obvious cluster mean wrong cluster, and the eval cluster may need a cross-account role with an external ID.
  <sub>sources: codex:019e94d1-e329-7590-b6b2-bfac0eef9950, codex:019ea9af-7020-7550-b427-d3478fed2057</sub>

#### 21. A killed long-running runner is not a crash, the local runner is not real-time, and a working sibling localizes the bug
`loop` · ⚠ _session-derived, unverified_

A shell/tool timeout killing a long-running game process usually does not mean a crash -- verify liveness first. play_match.py-style runners run to completion and exit (NOT spectatable live servers, so you can't 'watch' them mid-run); a server/bot launch can exceed the tool's wait timeout (e.g. 30s) while still running -- check the PID with ps; local LLM-bot runs against a live server 'time out' at the harness limit because the game runs indefinitely, and 'frames dropped' at join is normal catch-up. The local runner also is not real-time: it processes ticks as fast as the server allows (~80 ticks/s for bitworld_runner vs nominal 24 fps), so wall-clock timeouts and fps-derived 'game time' mislead -- reason in TICKS and instrument the actual tick rate, and check whether a persistent/spectator server mode exists before promising a live watch. When an elaborate code path is broken, compare it against the simplest working sibling to find the divergence: a simpler sibling bot made the LLM call synchronously inline (blocking the frame loop 1-3s, acceptable) and worked, isolating the bug to the more elaborate async dispatcher.
  <sub>sources: opencode:ses_1ff325156ffei2UiDygaVOZo5O, opencode:ses_200be1a72ffevDDc1PULNNG9UF, opencode:ses_21a0d30b6ffewSKOVSUyO4lZkU, opencode:ses_21f67d3f5ffeBF92agcd0cbOV0</sub>

#### 22. Resolve game rules against the installed package source you execute, not docs, READMEs, or example repos
`loop`

When investigating game mechanics (cogames/cogsguard/mettagrid, or any CLI + game package + framework), resolve rules against the INSTALLED source you actually run (.venv/lib/python3.12/site-packages/cogsguard, mettagrid), not PyPI READMEs or web pages. Conflict priority high-to-low: installed game-engine source > local framework source/docs > runtime CLI introspection > public GitHub repos > web/PyPI snippets. Stale docs actively mislead: old cogsguard releases describe a heart-production survival game (assemblers/chargers/chests) while the installed version is territory-control scored by connected held junctions. Example/Coglet/Cogamer fork repos are good for architecture but NOT authoritative for exact rules even when they claim the same scoring/length/agent-count. Name the three layers distinctly -- cogames (CLI), cogsguard (game/missions), mettagrid (framework providing the policy import surface). When a design doc and code disagree on map geometry, validate against the upstream game source, not the player's bootstrap constants. Treat run/launch docs as suspect until validated command-by-command against the launcher/bot code. Authoritative repos: github.com/Metta-AI/cogames and cogame-cogsguard; local Metta source at ~/coding/metta/packages/cogames.
  <sub>sources: opencode:ses_223934d40ffeDEyNrTrCRbhzkC, opencode:ses_223c540d9ffe8nF6kcU1RXf0wp, opencode:ses_22400da29ffdOpcL8PRxwU9b7G, alpha_cog/AGENTS.md (+5)</sub>

#### 23. Confirm a probe/trace tool can reproduce the configuration you are diagnosing, and prefer reproducible probes over docs
`loop`

The in-process trace tool `experiments/trace.py` cannot reproduce split-team behavior -- it runs ONE policy class for all 8 agents via direct simulator access with no --split/--partner flags. Splits are only available through `cogames play` with `-p class=...,proportion=N -p starter:M`, so diagnose a split-specific failure by comparing `cogames play` game-stat summaries (gear gained/lost = pickups/deaths, hearts produced/consumed) across configs rather than expecting a per-tick trace. Confirm load-bearing environment assumptions with small reproducible probe scripts rather than prose docs. The runnable Bulbacog policy is HandcarPolicy/MultiAgentPolicy in tools/railcar/policy.py with a curses TUI (tui.py) rendering observation/memory/full-map for human-in-the-loop debugging (obs tokens decoded in obs_parser.py; symbols/keybinds in symbols.py/keybinds.py) -- use this 'Handcar' harness to drive the player live.
  <sub>sources: opencode:ses_1fb28db30ffe6RjE6dq3HoVbzs, archive/cogames_playground/alpha_cog/docs/cogames-verification-report., opencode:ses_1f6fd3cbeffesP62asFzIp32PQ</sub>

#### 24. Ground every stat in an LLM-narrative report with comparative context and avoid fake precision
`loop` · ⚠ _session-derived, unverified_ · _see related: S030 (other tier)_

Ground every stat an LLM-narrative report cites with comparative context (rank, leader value, margins) or it is meaningless -- '577 tasks' means nothing without 'near top, leader did 590'; feed the LLM every stat the reporter computes, not a subset. Cut absurd significant figures ('54.46%' reads as fake precision); round in narrative text. Don't capitalize long lowercase policy names -- distinguish them with per-name colors in rendered HTML and reserve gold/silver/bronze styling for the top-three podium. Conventions for coworld reporter/extractor code: prefer a self-contained HTML report (inline CSS/JS, no external imports) so the artifact renders standalone in Observatory without asset hosting; when converting a reporter from .md to .html, ADD the new HTML and parquet artifacts while KEEPING the existing stats.json artifact (download-only) rather than dropping it; and generalize over slot count rather than hard-coding two players, even for a currently-2-player game.
  <sub>sources: codex:019eb326-beb5-7b03-890e-732da6aa6746, claude-code:2e0ca80b-d747-4358-a5dc-650397742760</sub>

#### 25. In a variant-composed engine, verify a mechanic empirically -- a named field or helper is not evidence it is active
`loop`

To answer 'does attribute X affect gameplay in mission Y' in a variant-composed engine like cogsguard, do NOT trust schema docs, a TECHNICAL_MANUAL capability statement, docstrings, field descriptions, or suggestively-named helpers (they pattern-match LLMs into wrong models). machina_1 is assembled from ~22 variant files with no surface summary. Checklist: (1) list required variants (grep dependencies()); (2) find the runtime handler that actually READS the attribute (e.g. VibeFilter/actorVibe(...)); (3) confirm the installing variant is in deps; (4) grep for assignments to empty-default schema fields; (5) verify active actions with `uv run cogames describe <mission>`; (6) grep for callers of suggestive dead helpers. A Pydantic field with an empty/disabled default factory is opt-in and looks identical at the call site -- treat it OFF until a variant populates it. Dead code with a confident name (exported-but-uncalled require_role_names_from_vibes) is worse than no API. Name overlap (vibe names vs gear-item names match 5/7) is a hypothesis, not coupling -- open the file where coupling would be ENFORCED and look for the actual read. Cheapest checks first: `uv run cogames describe machina_1` shows only noop/move/change_vibe (no attack), and a probe script confirmed move_energy_cost==4, talk disabled, 21 tags, 74 obs features, and that change_vibe_* never adds gear.
  <sub>sources: opencode:ses_223c540d9ffe8nF6kcU1RXf0wp, archive/cogames_playground/alpha_cog/docs/postmortem-vibes-vs-gear.md, alpha_cog/docs/upstream-issue-report.md, archive/cogames_playground/alpha_cog/docs/research-sources.md (+1)</sub>
