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# tools/ — executable substance

Lessons embodied by a script/program. The scripts themselves live in the source repos (see `sources`); this manifest records what each is for and the heuristic it encodes.

#### 1. Cap paid inference: default to a same-code-path mock provider and iterate against a generation cache
`loop` · tool: `--llm-mock:fixtures.jsonl`

Cap paid inference during development: default the iterate loop to a mock/deterministic provider and spend real tokens only on a deliberate, budgeted smoke test (e.g. a single Bedrock-backed bootstrap with --vlm-budget 1); for end-to-end path validation that just needs to prove the LLM action/chat/vote path works, use a cheaper/smaller model override — the trace still proves the path. Implement the LLM mock on the SAME code path as live (e.g. --llm-mock:fixtures.jsonl replaying JSONL with strict FIFO and kind-mismatch detection, auto-erroring when fixtures run out so the agent degrades to its rule-based fallback): this keeps the parity/regression harness self-contained, exercises the real code minus the network call, costs no credits, and lets the acceptance suite include matrices like {LLM-no-mock, mock-basic, mock-errored} to test success AND error handling. Build a generation cache so a classifier iterates offline without hammering a shared GPU worker: calling the live worker per experiment is slow and queue-bound (queue depth hit 42; long requests timed out), so a build_cache step generates reference and test answers once and a classify_offline bake-off reads the cache, letting featurizer iteration run in seconds with zero worker calls.
  <sub>sources: claude-code:b952ba45-d32f-4ce0-a227-c691de8c588f, codex:019e13e1-e883-7fe0-93b4-54adeac75285, codex:019e14a5-e574-7770-953b-17019f2f5e74, bitworld/among_them/players/mod_talks/LLM_SPRINTS.md (+1)</sub>

#### 2. Build a gating multi-seed multi-candidate A/B harness with structured output that shares baseline runs
`loop` · tool: `ab.py`

Run each (policy, seed) pair as a subprocess parsing the score from stdout, accept one baseline plus MANY candidates per invocation (a repeatable --candidate mode sharing the baseline across {baseline,cand1,cand2,...} in one pass), use a bounded THREAD pool (workers only block on subprocesses, no Python CPU; ~3x faster, 22s vs 65s, capped because multi-second per-env init does not parallelize), and exit non-zero when any candidate's mean is strictly worse so it gates automatically. Sequential A/Bs against the substrate baseline and against the full policy each saturate the worker pool on their own baseline and cannot be parallelized as separate invocations, so the shared-baseline multi-candidate mode roughly halves wall-clock per winning cycle and enables cheap parameter sweeps without editing config constants between runs. Emit STRUCTURED machine-readable output (scores, exact commands, git SHA, dirty-tree state, env) with paired-bootstrap CIs, sign-test, trimmed means, and top-N wins/losses; human-only tables force manual transcription and block meta-analysis.
  <sub>sources: alpha_cog/AGENTS.md, alpha_cog/CODE-SUMMARY-key-scripts.md, archive/cogames_playground/alpha_cog/experiments/notebook/hypotheses/H, alpha_cog/audits/research-audit-2026-05-04.md (+1)</sub>

#### 3. Build a reusable in-process trace/diff tool for recurring regression questions; read the score first
`loop` · tool: `ab.py`

For any per-seed regression that persists unchanged across two or more ships, capture and DIFF per-tick per-agent traces (baseline vs current) to find the earliest divergence tick BEFORE forming another hypothesis -- a trace diff is far cheaper than another speculative A/B and repeatedly reveals mechanisms one step removed from the flag (a ~2-hour trace-and-diff tool build plus 20 minutes of analysis resolved a regression open across 15+ speculative experiments). A saved cogames scrimmage replay (--save-replay-dir) contains only static objects and aggregate stats, NOT per-tick frames, so to get per-tick behavior build an in-process trace runner driving mettagrid.Simulator directly (not the 'cogames play' subprocess) so you can grab each AgentPolicy._infos per tick (chosen skill, args, working memory, failures) plus sidecar state the policy never sees (hub inventory, agent positions, deaths); keep the A/B scoring harness as a subprocess for isolation but use the in-process path for trace analysis. It is much slower than a scrimmage, so read the SCORE first -- the failure is often obvious from the regressed score before the trace finishes. Build a reusable general capture+diff tool (skill histograms per role, hub-inventory time series, per-agent distance, death timelines, earliest-divergence tick) once you have three or more pending 'why does seed X regress' questions. When hosted logs 404 or are useless, build a deterministic local replay inspector: resim the BitWorld input+hashes from public artifacts and print final per-player state to cleanly separate 'died early' vs 'lost as crew' vs 'wandered until timeout' (in a temp dir to keep the repo clean) -- but note the tension: a thin resim-and-print inspector is worthwhile while rolling your own full replay PARSER is a dead-end detour, and if the real problem is the player's own tracing is too noisy/untargetable, fix the tracing source instead. Desired-but-unbuilt: ab.py --trace-seed N to auto-capture a trace for one interesting seed alongside the aggregate A/B, and trace_compare_agents.py for per-agent-pair trajectory divergence between two traces on the same seed.
  <sub>sources: claude-code:425dc11e-1c8d-4d12-bf44-adf01b6fa796, codex:019e147d-3e35-7f00-a855-0d39cb511d70, codex:019de5a5-fadf-70b3-8773-2072cabae94c, codex:019e147f-2fcd-7e83-93ab-fab57ce504b4 (+6)</sub>

#### 4. Bake perception assets offline with a digest-pinned regenerator and check them into the repo
`crewrift` · tool: `bake_assets.nim`

Check baked perception inputs (palette, sprite atlas, map rasters, walk/wall masks, font) into the repo alongside a digest-pinned regenerator so they are reproducible and tamper-evident rather than regenerated ad hoc. Concretely, bake_assets.nim/bake_assets.sh produce palette.bin, sprites.bin, map_pixels.bin, walk_mask.bin, wall_mask.bin, font.bin, and map.json from an upstream bitworld checkout (BITWORLD_DIR), and bake_nav.py runs A* over walk_mask.bin to produce nav_paths.bin from nav_graph.json. For inspection, frame_to_text.py converts recorded frames.bin into LLM-readable text grids and frame_viewer.py views them -- both read recorded traces and are not a live capture pipeline.
  <sub>sources: players_checkouts/players/users/james/personal_cogs/AGENTS.md</sub>

#### 5. Serialize the one-time shared build/sync before fanning out parallel clients - parallel loaders corrupt the toolchain
`loop` · **negative result** · tool: `build_modulabot.py`

Run shared one-time setup serially in a single process BEFORE launching N parallel policy clients or bot subprocesses, then fan out. Parallel loaders race on building the shared native lib (e.g. libguidedbot.{so,dylib}) or on per-__init__ dependency extraction/sync when the pre-build step only guards 'build lib if missing' and not 'sync deps once'. The robust mechanism is a cross-process fcntl advisory lock (e.g. on ~/.nimby/.python_sync.lock) wrapping concurrent dependency syncs so parallel builds do not corrupt the shared toolchain dir; pin exact versions (Nim 2.2.4 / nimby 0.1.26, or .nim-version 2.2.6 / .nimby-version 0.1.26) and run `nimby sync -g <nimby.lock>`. Import POSIX-only modules like fcntl defensively (try/except ImportError) so the helper still loads on non-POSIX platforms.
  <sub>sources: bitworld/among_them/players/build_evidencebot_v2.py, bitworld/among_them/players/evidencebot_v3/build_evidencebot_v3.py, bitworld/among_them/players/mod_talks/build_modulabot.py, claude-code:0bd5f20d-10ad-41ef-8372-ae778cd59000 (+1)</sub>

#### 6. Intercept button-runners as an event-trigger en route, never as a standing positional bias
`crewrift` · tool: `button_runner_study.py`

A button-runner is just a high-value victim appearing predictably during the kill window. Trigger on the ROBUST signal (ticks_until_kill_ready + an observed approaching runner), not a fragile absolute tick like 'tick 500/900' (every meeting resets the clock); this reuses the window Search/Hunt already wake for, so no new scheduler is needed. Layer it as one pure helper (detect a runner over belief) plus small named bias hooks into Search positioning, victim selection, and Hunt strike-location -- each a preference, never a hard override of 'kill the visible isolated victim'. Empirically, runners travel SOLO (median 0 other crew within 48px) and 57.3% funnel through the Hydroponics-side mouth of the Bridge corridor (chokepoint ~x270-368), but ~42% are already at the bridge and not interceptable; bias search toward the corridor/Hydroponics, prefer cells ~120-320px off the button (peak ~220) on common approach paths, and score candidate cells as (pass-through fraction) x (distance band) x 1/(1+median_crew_near). Kill the runner en route then vent away, not at the button (the bridge is the most-trafficked room where a body is reported fast).
  <sub>sources: personal_labs/crewrift_lab/crewrift/crewborg/docs/designs/button-runne, personal_labs/crewrift_lab/lessons_archive/TENTATIVE_LESSONS-20260613-, personal_labs/crewrift_lab/suspicion_lab/tools/button_runner_study.py</sub>

#### 7. Unit-test the bundle's shape, not a real upload; skip engine-needing tests with a reason
`loop` · ⚠ _session-derived, unverified_ · tool: `cogames`

Unit-test the submission bundle's shape, not a real tournament upload: cover config JSON round-trip, packaging-CLI output layout, and the override engine at the action-index level, and deliberately do NOT run `cogames upload` (which needs Docker and tournament credentials). Skip tests needing an unavailable engine (e.g. mettagrid not installed) with a clear reason rather than failing, while engine-independent tests run unconditionally. When the submission runner gives no constructor seam for config (cogames calls SDKPolicy.__init__(policy_env_info, device) with no slot for instructions/config kwargs), ship a JSON config file alongside the policy module via a packaging CLI that cogames flattens into the bundle root, and have the CLI print (but not execute) the exact cogames upload command so the human stays in control of the actual submission.
  <sub>sources: claude-code:eb751ed4-c198-4b0d-819e-1a3002f4f341, claude-code:f196c6ef-01ee-461d-b240-e21e96b7c038</sub>

#### 8. Run the A/B harness as one subprocess per (policy, seed); render off, fail-fast, thread-parallel
`crewrift` · tool: `cogames play`

Run the A/B harness as one subprocess (cogames play) per (policy, seed) pair rather than importing the simulator in-process: this avoids cogames init-ordering concerns and prevents one crashed run from contaminating the rest. Use threads (not processes) since workers block on subprocess I/O; expect ~3x speedup bounded by a ~1.5s serial per-subprocess cogsguard init tail. Fail-fast on any worker exception (cancel remaining work and re-raise) — partial A/B results are worse than none. Force render off in batch runs (-r none) for ~25x speedup (a 6-seed run dropped from ~62s to ~2.5s) because the harness defaults render to gui even without a replay file.
  <sub>sources: opencode:ses_2204f83deffedZP7TPbuAnQIhs, alpha_cog/experiments/README.md</sub>

#### 9. Download episode artifacts via job_id raw routes; resolve IDs fresh, prefer the artifact zip over the tail-truncated stderr log
`loop` · **negative result** · tool: `coworld-episode-artifacts`

Download Coworld episode artifacts via the universal job_id handle, reading RAW API routes rather than the published `coworld` client (which ships behind the server): results/replay/error_info from /jobs/{job_id}/artifacts/{results,replay,error_info}, per-agent logs from /jobs/{job_id}/policy-logs[/{idx}], policy artifacts from /jobs/{job_id}/policy-artifact[/{idx}] (200 MB cap); check <base>/openapi.json when a route 4xxs. League/tournament and experience-request episodes are disjoint populations but BOTH expose job_id, so one code path serves both. Gotchas: Crewrift LEAGUE episodes are NOT in /v2/episode-requests ('coworld episodes -p <policy>' returns [] for a league player) -- go via /stats/policy-versions then /episodes. The policy-artifact listing returns FILENAMES like ['policy_artifact_0.zip'], not slot ints, so int(s) parsing silently drops everything and looks like 'no artifacts'. The hosted stderr policy log is capped (~9999/10k lines), keeps the TAIL (tick 1 is gone, ~final 6%), and has id-only path params with no offset/limit, so prefer the artifact zip for time-series especially the start. NEGATIVE: resolve live IDs (league/division/round/pool/policy-version) FRESH every time and never reuse cached ones (they rotate; a cached ID silently fetches the wrong/no episode) -- get current IDs from the relevant CLI or the Observatory UI, and re-resolve any route on a 4xx. The coworld CLI lives in ~/coding/metta/packages/coworld (treat the metta checkout READ-ONLY) and installs to ~/.local/bin/coworld; verify auth by listing leagues (`coworld list` hits v2/leagues; a 200 with a table means authenticated). The stable extractor output URI is COGAME_REPLAY_STATS_PARQUET_URI (dev CLI equivalent --output <path>, `coworld extract <episode-id>`).
  <sub>sources: personal_labs/crewrift_lab/docs/crewrift-replays.md, personal_labs/crewrift_lab/lessons_archive/TENTATIVE_LESSONS-20260612-, player_labs/.claude/skills/coworld-episode-artifacts/SKILL.md, claude-code:93aaeccd-fa2d-4581-8f93-3e77e556db21 (+2)</sub>

#### 10. Recover Crewrift events black-box by re-simulating the replay and diffing pre/post-step state, not by parsing bytes or patching sim.nim
`crewrift` · ⚠ _session-derived, unverified_ · tool: `crewrift replay event reporter (re-sim diff)`

The robust way to derive Crewrift game events from a replay is to re-simulate with the real Crewrift sim and snapshot-diff two consecutive states, reusing the sim's already-exported procs; do NOT parse bytes and do NOT modify sim.nim or anything outside the reporter directory. Use the built-in ReplayPlayer loop as the canonical consumer template (loadReplay(path) -> initReplayPlayer(data) -> looping=false, mismatchQuit=true -> while replay.playing: replay.stepReplay(sim); stepReplay applies replay joins/leaves/inputs/chats, steps the real sim, and validates hashes), not a hand-rolled driver. gameHash() (sim.nim:1551+) hashes essentially the entire SimServer/Player state every tick and playback validates against it, so after each stepReplay the re-simulated state is provably identical to the original with exact tick alignment. Capture the pre-step snapshot AFTER that tick's joins/leaves/inputs are applied, not before, or player-index diffs are misaligned on leave ticks. Resolve tick-semantics disputes against the actual sim.step order, not intuition: within a tick this tick's input is applied, then movement/vent/cooldown update, then fresh-A kills fire from the UPDATED state (the naive 'press A, tick ends, kill applies from last tick's position' model is wrong); the Playing-phase per-player loop (sim.nim:3927-3948) does NOT break on a mid-loop phase change, and applyInput movement (2875) precedes the attack check (2887), so every player's tick-T movement still lands and the post-step snapshot holds correct positions. Events cleanly recoverable by state-diff: death/body (postStep.bodies.len > preStep.bodies.len; new Body color/slotId), task completed (tasks[t].completed[p] flips), meeting started (phase Playing->Voting), vote cast (voteState.votes[i] changes), ejection (voteState.ejectedPlayer), roles (players[i].role any tick after Lobby), emergency button (players[i].buttonCallsUsed increments). More generally, detect lifecycle/terminal events by diffing state across ticks when the protocol sends no explicit messages (an origin coordinate reset = new active object; a sudden stack-height drop = clear/score), keeping cross-tick diffing in a dedicated telemetry object separate from decision logic. SimServer.gameEventLoggingEnabled only controls the sim's own prose event logs, not core replay semantics. src/crewrift/replays.nim is the VIEWER/playback + hash-checking module, NOT the writer; the write path is the live server (crewrift.nim passes a temp .bitreplay as saveReplayPath into runServerLoop; server.nim opens replayWriter and writes leaves/joins/input-mask changes/tick hashes, then uploads on shutdown).
  <sub>sources: claude-code:3dd8c20f-7122-4a7c-9400-e9b7f6799a28, claude-code:bc36021d-994c-48ec-a3bc-7ec637f7464f, codex:019e8aca-584f-7821-af97-25a1500c403d, codex:019e8ae0-d849-7d12-9a7c-2b5e09ba75cf</sub>

#### 11. Structure the lab notebook as typed, validated, append-only per-file entries; orient against it before any A/B and write the hypothesis file first
`loop` · tool: `experiments/notebook/generate_index.py`

Structure a scripted-policy optimization lab notebook as per-file append-only entries under experiments/notebook/ (experiments/E0XX.md, hypotheses/H0XX.md, tournaments/T0XX.md, submissions/S0XX.md), plus a generated index.json, a backlog.yaml, and a protocol.md — not one monolithic file; never edit old entries, create a NEW file per experiment, and regenerate the committed index in the SAME commit (`uv run python experiments/notebook/generate_index.py` produces index.json and backlog.yaml). backlog.yaml and index.json are GENERATED — never hand-edit; change a hypothesis priority via its markdown front-matter and regenerate. The index validator enforces a fixed verdict vocabulary — use the exact tokens {inconclusive, not-shippable, reject, ship, informational} (write 'reject' not 'rejected') and fixed status enums {open, shipped, rejected, closed, deferred}, with cross-field constraints (a ship-version implies a ship verdict; status:shipped requires shipped_in) so malformed entries fail loudly. Use one experiment (E-number) per run-batch: the same hypothesis question with different arms (a sweep of preload=2, preload=3, then gate variants, all testing 'does preloading work and under what conditions') is ONE E-number, while a different aspect of the same H-number gets a new E-number. Before any A/B, orient first: read index.json, then backlog.yaml and per-hypothesis status, then related E0XX.md files, and the by-flag/by-status/axis-saturation indexes — do not re-derive an existing hypothesis or re-run a rejected/saturated one without materially changing the variant. For any non-trivial new hypothesis, write hypotheses/H0XX.md BEFORE running so the idea survives a crash. Record each ship's provenance as a compact evidence string (seed count, horizon, W/T/L, mean delta vs the named prior, e.g. '50-seed 10k, 46W/0T/4L, +13.74 mean vs V10') and label high-confidence vs exploratory (a +0.18 mean, 27W/23L probe must be labeled). Require each canonical league ship in BOTH submissions.json and a notebook S<NNN> entry (internal version, league name/version, season, git SHA, flags) — absent from either it is not a verified league submission. Quarantine dirty-tree submissions as non-canonical: keep for audit but never compare or use to infer league-version mapping.
  <sub>sources: claude-code:e049dbf8-9702-41e5-8ac5-830cde8a27ad, archive/cogames_playground/alpha_cog/experiments/notebook/protocol.md, ses_1fc6f16cfffe03SKAcP78Ua0IK, ses_1ff558835ffe0KTtjYHxXaJXa3 (+11)</sub>

#### 12. Feature-flag every version difference behind a byte-identical flags-off substrate, freeze each ship as a snapshot, and trace regressions against the frozen config plus the git SHA
`loop` · **negative result** · tool: `experiments/regression_check.py`

Encapsulate every version-to-version behavioral difference as a feature flag on a single policy class, and guarantee all-flags-off is BYTE-IDENTICAL to the upstream starter policy on fixed seeds. Verify this substrate equivalence automatically (experiments/regression_check.py) as a HARD GATE at the start of every session and before every A/B, never after; it proves the only changed variable is the flag under test. If it fails (non-zero exit) the harness is contaminating the baseline — treat every A/B as invalid until fixed; new subsystems (a memory store) must stay empty and gated when flags are off; compile-time flags (Nim `when defined(...)`) work the same way. Re-run the cheap substrate check whenever a non-flag-gated shared path (perception, interpretation, pipeline wrapper, core navigation, decide.py, pursue_target.py) drifts, because otherwise every 'versus bare substrate' A/B is silently invalidated and the symptom is misattributed to the candidate flag; flag-gated code is safe to refactor as long as the flags-off path is untouched. CRITICAL caveat: the flags-OFF check never exercises flag-gated code, so a broken flags-ON ship build can score 0.00 while the substrate check passes a FALSE NEGATIVE — after modifying shared decision/pathfinding code additionally spot-check the actual flagged ship build by confirming known per-seed scores (seed 42=1.42, seed 1=1.72, seed 7=3.08). A V5 ship break from an uncommitted PursueTargetArgs field mismatch passed the flags-off check unnoticed; a new PolicyConfig field must also be wired into the _build_config() call that passes each ClassVar, or A/B candidates overriding the ClassVar silently run as baseline. Freeze each shipped policy version as a named, byte-stable historical snapshot class (e.g. `AlphaFullV<N>` in experiments/historical_policies.py) in the SAME commit that flips the flag in the shipping policy, enforce that every ship commit adds its snapshot entry, and never delete these classes — without the frozen snapshot, anti-regression tournaments cannot compare against the pre-ship configuration, drift becomes invisible, and a 'current vs latest ancestor' comparison silently reports a meaningless all-ties row; when triaging a new version's regressions, trace AGAINST the frozen class, never the live class (a moving target whose reward delta is non-reproducible). A flag-snapshot freezes only the enable_* flag set, not the shared base-class code (stage programs, skills, navigation, memory, obs parser) it re-runs against, so refactors retroactively move every snapshot together — the only true reproducible anchor for a submitted version is the git SHA recorded at submission (experiments/submissions.json); to reproduce a past version, check out that SHA in a worktree or replay the shipped bundle, not just the flags.
  <sub>sources: archive/cogames_playground/alpha_cog/experiments/notebook/tournaments/, alpha_cog/AGENTS.md, alpha_cog/audits/research-audit-2026-05-04.md, alpha_cog/experiments/README.md (+13)</sub>

#### 13. Vendor fitted weights as a JSON contract with the transform inside
`crewrift` · tool: `fit.py`

Vendor fitted model weights as a JSON artifact (feature -> coefficient, intercept, bin edges, model/version provenance) loaded at import, with hand-tuned constants as an env-gated fallback (CREWBORG_SUSPICION_WEIGHTS=0 forces fallback); runtime scoring stays a plain dot product with no ML library in the image. Ship the feature transform (bin edges and linear clip) INSIDE the weights JSON as an explicit CONTRACT (BIN_SPEC, LINEAR_CLIP) so the offline-trained transform and the live-inference scorer cannot silently diverge — the runtime scorer must reproduce it byte-for-byte. Tag the file with an explicit schema version (e.g. crewborg-suspicion-weights/v1) and embed reproducibility metadata: feature_set, trained date, dataset path, game/row counts, regularization C, cv_auc, cv_log_loss, intercept/logit prior, linear_clip, sample_unit_ticks, bin spec, full coefficient dict. Vendor ONLY fit metadata and coefficients — keep secrets, tokens, and credentials out of the artifact entirely.
  <sub>sources: personal_labs/crewrift_lab/suspicion_lab/tools/fit.py, personal_labs/crewrift_lab/crewborg/data/suspicion_weights.json</sub>

#### 14. Defer A* to the chosen target; rank candidates with cheap local geometry
`crewrift` · ⚠ _session-derived, unverified_ · tool: `frontier.py`

Per-tick target ranking that runs A* across many candidate boundary/repair cells is the dominant compute cost, not the territory math, so rank candidates with cheap local geometry and defer A* to the final action resolver on the single chosen target -- running A* across all candidates every tick is a performance trap. Centralize 'where is the frontier' geometry in one shared helper (frontier.py: cogs_frontier_junctions, frontier_repair_junctions, FRONTIER_CONTACT_RANGE = 24 Manhattan, quadrant-filtered) that BOTH the strategy/outer-loop layer and the patrol mode call, so they share one definition of frontier contact; otherwise the outer loop orders a transition (quadrant contested) the mode cannot fulfill because it computes a different frontier.
  <sub>sources: codex:019e147f-2fcd-7e83-93ab-fab57ce504b4, codex:019e1355-fd7e-77b2-8af2-3f93c0ae623a, codex:019e1357-835f-7442-bd46-7e911617621f</sub>

#### 15. Fix the generator, never the generated artifact; gate the intermediate contract
`loop` · **negative result** · ⚠ _session-derived, unverified_ · tool: `guide_contract.json generator pipeline`

When iterating on a multi-stage generation pipeline, fix the generator/prompts -- never hand-patch outputs. Keep 'log mistakes' and 'fix mistakes' as distinct modes by recording generation defects to a separate audit log (GENERATION_MISTAKES.md) while explicitly NOT editing generated docs/contract/code. Treat the intermediate contract as a quality gate: a bad guide_contract.json (a bogus action candidate 'b', a wrongly-inferred visual/mixed perception path) was consumed as-is by the next stage and carried into the final agent, and errors caught there are far cheaper than in emitted policy code. Validate the framework's required API symbols (ActionCommand, AgentRuntime) import successfully BEFORE generation -- turning a late 'module has no attribute X' into an early actionable error. Keep generator tests hermetic with a tiny local framework stub while emitted artifacts still point at the real package.
  <sub>sources: codex:019e1940-5a75-7f51-b057-63f894ddb061, codex:019e13cd-d482-7133-a38d-c683c32a3004</sub>

#### 16. Render world-space nav data onto the ego viewport with the per-frame camera offset
`loop` · ⚠ _session-derived, unverified_ · tool: `nav-trace-viewport-renderer`

To render world-space nav data (path, edges, goal) onto a small fixed viewport for debugging, apply the per-frame camera offset from the perception log (world_to_screen = (wx - cam_x, wy - cam_y)) plus a visibility clip to viewport bounds, and build a waypoint id -> (x,y) lookup once from the static waypoint graph so path/edge IDs in the trace resolve to drawable points.
  <sub>sources: codex:019e00c0-d889-7d72-9781-3e90e7c1c7b9</sub>

#### 17. Give an unattended nightly pipeline a --check mode - it silently skips a sleeping machine or an expired token
`generic` · tool: `nightly_refit.sh`

A cron-driven nightly pipeline silently skips a night if the machine is asleep at the scheduled time, and an expired softmax/league auth token aborts the run until the next night; provide a `--check` mode that verifies prerequisites (uv, docker daemon, softmax auth) before relying on the unattended cadence, and account for both gaps.
  <sub>sources: personal_labs/crewrift_lab/suspicion_lab/README.md, personal_labs/crewrift_lab/suspicion_lab/tools/nightly_refit.sh</sub>

#### 18. Run a multi-mode parity harness keyed on tick, keep the trace writer observation-only, and prove byte-identical action streams
`loop` · tool: `parity harness (--vs:self/--vs:ref)`

Maintain one harness with explicit modes (--vs:self, --vs:<reference-bot>, --mode:black|random|mixed, --replay, --trace-dir, plus a CI smoke script) and run it at multiple seeds whenever you add state, trace output, or instrumentation; separating self-consistency from reference-parity distinguishes a real logic bug from forced RNG divergence. Verify reproducibility empirically — replay against captured frames (6281/6281 identical) or hash per-tick action sequences across double runs — to catch hidden nondeterminism (uninitialized state, clock reads, dict/set order). Key every determinism assertion on tick, never wall_ms (replays re-time emission). Score-equality is only a smoke test (trajectories can diverge yet end equal); add a per-tick action hash for true equivalence. Give --max-steps-truncated runs an explicit signal (--allow-truncated or a round_truncated event) so a 'no unclosed meetings' rule does not false-positive. A trace writer must not change decisions: read agent state only AFTER the decision function returns, never mutate the bot, make no RNG calls, and confirm via parity that diagnostic fields (branchId, intent) are read by NO policy code, verifying a byte-identical action stream with and without tracing (500/500); on a parity-critical path keep instrumentation side-effect-only and preserve original field write-timing when migrating a field other code reads. Record WHY each branch fired via a fired(<branch_id>) call (the firing branch, not the action, is the tuning unit) plus a tuning_snapshot of knobs in effect per run, and keep three artifacts in sync — the doc (BRANCH_IDS.md), the validator whitelist (validate_trace.nim KnownBranchIds), and the fired IDs at call sites — auditing the whitelist in BOTH directions since a missing branch fails valid episodes while a deleted one lingers.
  <sub>sources: opencode:ses_21fdab7c0ffeyW9gOCR3FyrI7b, opencode:ses_2202cf626ffenXSjna0lj4fEs2, opencode:ses_21ffeb5ffffeA1Pk6sdyHDzwQW, opencode:ses_224322057ffexj7SYlcfVA0zgy (+12)</sub>

#### 19. Build cross-game LLM memory and iterate prompts offline against captured contexts with mechanical checks
`loop` · tool: `players/mod_talks/tools/llm_prompt_eval.py`

Give an LLM agent cross-game learning by dumping full per-game memory to JSON (game_id, result, role, kills, ejected, strategies_used, failures, episodic events, llm_call_log) and, before each new game, synthesizing recent dumps into a 'PRIOR GAME LEARNINGS' block injected into the system prompt (benefits compound after ~10+ games); optionally run a more capable analyst model (e.g. Claude Opus) offline over each dump to produce the structured learnings, separating the cheap real-time advisor from the expensive post-game analyst. Iterate prompts offline by capturing dispatched contexts during live/mock games (MODTALKS_LLM_CAPTURE=1; contexts land under <trace>/<bot>/<session>/round-NNNN/llm_contexts/) and replaying a few hundred against candidate prompts -- enough signal for mechanical checks without full live games. Score replays with cheap mechanical checks not human judgment: valid_json, living_player_target, respects_safe_colors, chat_within_max_len, no_ai_reveal, one CSV row per response. Give the eval harness a --mock mode sourcing responses from a JSONL fixture so iteration runs in CI without burning tokens, defaulting to the same provider controller the bot uses in production for fidelity.
  <sub>sources: bitworld/among_them/players/SMART_BOT_GUIDE.md, bitworld/among_them/players/mod_talks/tools/llm_prompt_eval.py, bitworld/among_them/players/mod_talks/scripts/launch_mod_talks_llm_loc</sub>

#### 20. hosted-game self-play: anonymous slot joins fill all 8 slots, one container per slot
`loop` · tool: `run_hosted_8bulbacog.py`

`coworld hosted-game join` always sends X-Auth-Token and the server deduplicates by user, so every join from one account returns slot=0. To fill all 8 slots, drop to raw HTTP and join ANONYMOUSLY (no auth header), which claims sequential slots. To run a full self-play optimization harness one operator creates a play session then fills every slot by repeatedly POSTing the session /join endpoint with NO auth-token header, captures each returned player_url websocket address, and launches one Docker container per slot with that address passed as the engine websocket env var; before each `docker run`, best-effort `docker rm -f <name>` (check=False) so a stale container with the same fixed name does not block the launch. A repo's standard match script may only support one Python policy class for all slots, so a mixed roster needs a small temporary orchestration layer launching each bot via its own entrypoint, reusing the repo's server startup/shutdown conventions rather than modifying the shared harness.
  <sub>sources: claude-code:67ff1fe3-48c0-4738-b7fb-8b994e567434, archive/cogames_playground/bulbacog/scripts/run_hosted_8bulbacog.py, codex:019e184d-2de6-7b22-b8c0-853edbf53265</sub>

#### 21. A green build proves nothing about play; black-box smoke the image over a real socket, and climb the validation ladder
`loop` · tool: `smoke harness`

A build that succeeds and resolves the policy URI proves almost nothing about whether the player PLAYS; smoke-test the deliverable image as a BLACK BOX over a real socket, not the in-process policy imported from the host venv (importing misses runtime-dep installation, baked discovery env vars, the entrypoint invoking the bridge, the socket path, and all non-Python compiled-binary players). Climb a ladder before upload: py_compile/native-build (catches signature/type errors in seconds vs a full container build) -> unit tests -> docker build -> docker run --help -> run against a local copy of the game server -> smoke the EXACT runner-shaped argv (success = getting past argparse to connection-refused; quote the ws://...?... URL so '?' doesn't shell-glob). Use a harness that spins a fake engine WebSocket on a host port, `docker run`s the image at it, pushes protocol-correct messages, asserts well-formed responses, and confirms clean exit. Generate observations from a REAL environment (build the sim from the game recipe, step once) so they can't drift from the spec; for binary-protocol players even all-zero packed frames verify the handshake. The same built image must smoke THEN upload (build once; keep build logic out of the smoke skill — coworld-local-run's smoke.py does NOT build the image and hard-fails if no local linux/amd64 image exists, so the loop is edit -> rebuild -> smoke -> upload). Organize smoke tests per-game since observation specs and transports differ. A noop do-nothing image is the right first smoke for a new harness.
  <sub>sources: auggie:c9f833a1-50e8-4526-8839-f438394f903e, bitworld/among_them/players/how_to_submit_coworld_policy.md, claude-code:00f61c5a-0c3b-41ca-a586-969b3ce1ce83, claude-code:6064640e-b01d-47af-b5d8-b4d7d982e559 (+8)</sub>

#### 22. Build a stuck watchdog correctly and apply it on every nav path
`loop` · **negative result** · tool: `stuck-watchdog/jiggle-recovery`

A direct steer-toward-target controller with no obstacle awareness can burn an entire episode against the first barrier (5 of 6 crewmates pinned at one wall coordinate for thousands of ticks), so a position-delta watchdog that triggers replan/escape when displacement stays below epsilon for N ticks while a non-noop steer is active is essential and easy to omit. Build it with a frame-count threshold (<=2 unique positions in the last 6 moves, or N near-zero-movement frames). Recover with a deliberate PERPENDICULAR jiggle (e.g. 16 ticks, alternating sides each trigger) plus a path-cache clear -- NOT by retrying the blocked action -- and CRITICALLY keep the intended direction held while adding the perpendicular component, because versions that STOPPED pressing the intended direction just wiggled in place. Give the jiggle helper a mutable state ref so it decrements its own remaining-frames counter, and run it on EVERY navigation output path (normal, ghost, final-approach early returns), not just the main loop, or the bot still wedges in bypassing branches. When jiggle hits a blocked DIAGONAL, split it into a single axis -- holding the blocked diagonal makes the bot oscillate against the obstacle. Conversely, cardinal-only movement causes wall-sticking at corners; if the action space supports diagonals, return one when both axis deltas exceed the deadband to cover both axes at once (expect cardinal-asserting behavior tests to break -- read each, some are genuine fixture artifacts like a ~2px world/screen offset, and widen assertions rather than weakening gameplay logic). Diagnose by correlating steer targets against the locked coordinate: stuck bots at one axis value with targets on BOTH sides of the lock line means the barrier (a wall the pathfinder cannot cross), not target direction, is the cause.
  <sub>sources: claude-code:4c297fe5-08a0-4973-85ac-1b8824e638ac, codex:019e037c-5f9b-7ad1-8bf3-539709c4da15, opencode:ses_20422c08ffferRLvWnNE5DeB4w, opencode:ses_204495c66ffe7u8C8SOiEX1zdH (+5)</sub>

#### 23. Fit suspicion evidence weights from ground truth as a runtime-cheap linear log-LR model
`crewrift` · tool: `suspicion_lab/tools/fit.py`

Hand-intuition systematically mis-sizes evidence weights -- fit them from ground truth with L1-regularized logistic regression: the coefficients ARE the additive log-likelihood-ratios and the intercept absorbs logit(prior), so the in-game scorer stays sum-log-odds->sigmoid (a dot-product, zero ML dependency at runtime), negative coefficients encode exculpatory evidence, and linear instance counts mean instance-summing. In the crewrift refit over 62k rows the watched-real-task-completion exculpatory cue reached ~-10.8 log-LR (imposters produced ZERO such events), the hand-estimated tailing cue was ~10x too strong (fitted +0.49 vs hand-guessed ln 6.5), and follow-to-death was the strongest incriminating graded cue at ~+2.4. To learn a graded cue's SHAPE without nonlinear machinery, bin the value into indicator columns at fixed edges (e.g. dwell 0-12/12-48/48+ ticks). Print a per-cue sanity report (base rate, imposter-mean vs crew-mean, crude lift) before fitting.
  <sub>sources: personal_labs/crewrift_lab/crewrift/crewborg/docs/designs/suspicion-le, personal_labs/crewrift_lab/crewrift/crewborg/docs/designs/suspicion.md, personal_labs/crewrift_lab/suspicion_lab/tools/fit.py, personal_labs/crewrift_lab/suspicion_lab/tools/build_dataset.py</sub>

#### 24. Lint tuning constants against a snapshot and warn when the live binary drifts
`loop` · tool: `trace_smoke.sh tuning-snapshot lint`

Keep a tuning-snapshot manifest in sync with policy constants via CI: a smoke step greps public const declarations in policy modules against keys registered in tuning_snapshot.nim, with an escape hatch for excluded values (layout coords, hash seeds, derived constants), so every new knob must be registered or the lint fails. Also WARN when the live binary's reported constants drift from source -- a stale binary serving listen-window 250 vs source 100 can confound diagnosis.
  <sub>sources: opencode:ses_21f5b8db7ffe8xs4COwtdx913c, bitworld/among_them/players/mod_talks/LLM_SPRINTS.md</sub>

#### 25. Crewrift tooling: sprite-input byte, waypoint_editor.py, and tasks.nim assignment
`crewrift` · tool: `waypoint_editor.py`

To send a wander/movement input in Coworld Sprite v1, emit bytes([0x84, mask & 0x7f]) each frame. Edit the Crewrift navigation graph (waypoints and edges over the walk mask) with the waypoint_editor.py pygame tool operating on nav_graph.json; navigation paths are then derived by BAKING A* over the walk mask rather than computed live. Crewrift's tasks.nim assigns task IDs per crewmate by building a walkable-grid distance matrix between task points and improving assignments via local swaps (capped at MaxImprovementSwaps=64), scoring to balance duplicates, spread, maxCost, and totalCost -- know this when reasoning about which tasks a given crewmate will be routed to (TSP-style pathing).
  <sub>sources: metta/agent-plugins/kitchensink/skills/ks.observe-protocol/SKILL.md, players_checkouts/players/users/james/personal_cogs/AGENTS.md, coworlds/coworld-crewrift/src/crewrift/tasks.nim</sub>

#### 26. Expose tunables as named sweepable knobs; run sweeps as one multi-candidate pass; generalize nav/rules/loop
`loop`

The seam that makes a Coworld policy sweepable is a typed frozen-dataclass 'knobs' config: Cogames runners forward kw.<name>=<value> CLI args to a policy __init__ as strings, so a small pipeline (coerce strings to declared field type, layer over a Python base config, report overrides for telemetry) lets every helper module expose tunables to CLI sweeps and to a GA/auto-tuner — name and document each knob as a sweep target (e.g. a heart-capacity curve [(100,3),(300,10)], role-mix counts). For a parameter-tuned scripted Among-Them/Crewrift policy, inject params into the scripted baseline at runtime with no LLM calls, reduce episode results to a single fitness number (winRate), and start from a baseline that already wins sometimes; a small bounded set suffices (voteThreshold 0.3-0.9, suspicionDecay 0.5-0.99, taskPriority 0.0-1.0, killCooldownAggression 0.0-1.0, reportTruthfulness 0.0-1.0). The Crewrift results schema is the score target — per-slot arrays scores (required scalar), win (bool), tasks, kills, imposter/crew (1/0), vote_players, vote_skip, vote_timeout, connect_timeout, disconnect_timeout, all length 1-16. Run any numerical-knob tuning (threshold, K, gamma, alpha) as a single multi-candidate sweep in ONE A/B invocation (baseline runs are shared so wall-clock is barely higher) and name each sweep subclass after its value (e.g. W120T1) for a clean monotonicity picture later. Canonicalize variant naming (AlphaFullV<N>a/V<N>b) so it never collides with ship versioning. The three things worth generalizing across coworld games are navigation (cost-shaped weighted A*, repulsor/attractor potential fields, decaying tabu trails, sidestep, wall-folding, stuck detection/recovery, lane/phase machinery), rule-authoring, and the optimization loop — NOT game-specific perception; a CLIPS-style production-rule language for per-tick behavior is easier to audit and tune than hand-written Python chains, pairs with the knobs layer, and lifted tournament rank when fed into the GA.
  <sub>sources: claude-code:7db8d397-c6dc-47b6-9b95-8f90675dd1b8, claude-code:2426100f-257d-4100-ba93-2097175a999c, alpha_cog/AGENTS.md, opencode:ses_206bfd18dffe1hUfJBOccuaLDC (+2)</sub>

#### 27. Resolve the framework checkout dynamically and derive package metadata from it, never hardcode
`generic` · **negative result** · ⚠ _session-derived, unverified_

Generate a player against a real discoverable framework checkout using layered path resolution (explicit arg > env var like *_FRAMEWORK_DIR > a list of fallback paths probed for a marker file like __init__.py), because multiple machines/clones (~/coding/metta, ~/coding/metta2) make a single hardcoded path brittle. Do not hardcode framework package metadata: derive framework name, package, and package_source_root from the actual framework directory, because a hardcoded source-root (cogames_agents.cyborg) that did not match the real framework (agent_policies.frameworks.coborg) produced wrong import paths in generated agents. When a generated player cannot import its framework or builds a nonexistent path (e.g. .../frameworks/src instead of .../src), suspect the generator's path-derivation/source-root logic before the generated agent code.
  <sub>sources: claude-code:7b66497b-650c-4bba-b410-6c854f59163d</sub>

#### 28. Replace fixed time.sleep() in threading/async convergence tests with a bounded poll-until-condition helper
`generic` · ⚠ _session-derived, unverified_

Fixed sleeps make episode/threading tests both slow and flaky. In player thread/async convergence tests (outer-loop, watchdog, pipeline round-trip), replace hard time.sleep() with a bounded poll-until-condition helper (`_wait_until(lambda: cond, timeout~2.0)` with only ~10ms sleeps inside the poll), keeping the poll timeout generous for CI headroom.
  <sub>sources: codex:019e03bf-6d47-7093-a40b-351c325bde5a, codex:019e03bf-982c-7fc0-8ba9-d4c640feb779</sub>

#### 29. Decode the Coworld Sprite v1 protocol by capturing real traffic first
`crewrift`

Coworld games (crewrift, Among Them) speak Sprite v1: a binary screen-streaming protocol with no semantic do-task/vote API -- the engine sends sprites plus a walkability map and the player sends gamepad inputs, so guessing the pixel format and writing strategy blind fails silently. Connect to COGAMES_ENGINE_WS_URL with no subprotocol and no join message (the URL carries slot/token) and parse each server message by leading byte: 0x01 sprite (id u16, w/h u16, clen u32, snappy-RAW RGBA, label u16+ascii), 0x02 object (id, x/y/z i16, layer u8, sprite u16), 0x03 delete, 0x04 clear, 0x05 viewport, 0x06 layer; decompress sprite payloads with cramjam.snappy.decompress_raw (raw block, not stream). Capture real traffic and dump unique sprite labels before coding strategy.
  <sub>sources: metta/agent-plugins/kitchensink/skills/ks.observe-protocol/SKILL.md</sub>

#### 30. Write perception kernels in numpy first; promote to numba only after profiling, and warm the JIT at startup
`crewrift`

Implement hot per-tick perception kernels in numpy first and promote to numba only after profiling proves the budget is breached (target: under 8 ms total perception per tick on an M-series Mac, sprite_match under 1 ms per sprite). The all-anchors sprite-match kernel uses a zero-copy sliding_window_view over the frame, premasks each sprite into stable/tint/transparent masks once, broadcasts equality for stable pixels and a 256-entry boolean LUT membership test for tint pixels, then thresholds matched-stable/matched-tint/miss counts; recover per-anchor player color via broadcast equality against a (16,h,w) mask volume plus argmax over axis 0, ties to the lowest index to match np.argmax. If you do use numba, its 1-2s cold-start can break the player container's startup deadline, so eagerly warm kernels during init before signaling ready (or AOT-compile with cache=True). A 128x128 (16384-pixel) frame parses well under the one-frame budget with NumPy, so write perception in the same language as the agent (Python) for direct-import integration and treat a C/Rust FFI port as premature.
  <sub>sources: players_checkouts/players/archive/players/among_them/coborg/PLAN.md, DESIGN_perception.md (Persephone perception module design)</sub>

#### 31. Apply the correct per-feature normalization divisor when parsing mettagrid observations
`loop` · ⚠ _session-derived, unverified_

Each mettagrid observation feature has a normalization divisor that MUST be applied when parsing; the wrong divisor corrupts the value. Observed divisors: agent:group and last_action norm 10; last_reward norm 100; episode_completion_pct, agent_id, lp:*, and vibe norm 255; inv:* norm 256; last_action_move norm 1; territory:here norm 3; territory:<dir> norm 9. episode_completion_pct is a 0-255 byte (divide by 255.0).
  <sub>sources: codex:019e05c3-544b-7153-8a0f-cfd681fa5345, opencode:ses_219cf4bdbffeNdmIXfRPfKWSJD</sub>

#### 32. Decode mettagrid territory tokens: here is a global observer-relative token, directionals pack boundary pairs, and two encodings conflict
`loop` · ⚠ _session-derived, unverified_

Verified against compiled mettagrid_c.so (Python only registers metadata). territory:here is a GLOBAL token (is_global=True, packed-location byte 254=GLOBAL_LOCATION, 255=empty/padding); its value is observer-relative: 0=neutral/tie, 1=friendly (observer shares the winning team tag), 2=enemy (norm 3, using NEUTRAL_TERRITORY=0/FRIENDLY=1/ENEMY=2). The four directional tokens (territory:north/south/east/west, norm 9) are SPATIAL, carrying a real Location(row,col) in the 13x13 window, and pack a boundary pair into one byte: value = this_cell*3 + adjacent_cell; decode here=value//3, adjacent=value%3. They are emitted ONLY at cells where territory differs from the named-direction neighbor, so same-on-both-sides values (0,4,8) never appear, the location marks the boundary cell, and tokens are sparse and appear late (~step 11 once the window spans an edge) -- a parser assuming a dense per-step grid sees almost nothing; missing directions default to territory_here. BEWARE a SECOND CONFLICTING encoding: junction Ownership uses UNOBSERVED=0, NEUTRAL=1, COGS_CONNECTED=2, COGS_DISCONNECTED=3, CLIPS_CONNECTED=4, CLIPS_DISCONNECTED=5 (Ownership.NEUTRAL==1 but territory NEUTRAL==0), and they are NOT interchangeable. Keep live territory:here authoritative for mode-EXIT decisions and guard against stale projected memory making an unsafe current cell look safe. Junctions expose net:cogs/net:clips connectivity SEPARATELY from team:cogs/team:clips ownership: a junction can show team:cogs while having lost its net: tag (disconnected, not projecting territory, not scoring) -- track net: and team: independently. Gated by mission flag obs.territory=True (cogsguard/missions/mission.py); ids are not registered when off. Also: the agent observation is a CIRCLE of radius 13, NOT a 13x13 square, so honor the circular footprint when writing observed cells or you corrupt corners.
  <sub>sources: codex:019e147d-3e35-7f00-a855-0d39cb511d70, codex:019e147f-2fcd-7e83-93ab-fab57ce504b4, opencode:ses_1fa66f0b7ffeIF47rk2pDlz4RX, opencode:ses_200c9dd21ffe1aW5O7lRu8HbG6 (+3)</sub>

#### 33. Gate experiments behind one preflight command and a tuning-constant CI check
`loop`

Provide a single preflight command that enforces the project's safety checks in order (dirty-tree warning, substrate regression check, spot-check after touching decision code, shipped-config snapshot, args-schema sync, short smoke run) rather than relying on human memory. Add a CI exhaustiveness check that scans policy modules for tuning constants (e.g. `Name* = value`) and fails if any is neither registered in the tuning snapshot nor whitelisted, so no tunable parameter silently escapes the A/B optimization surface.
  <sub>sources: alpha_cog/audits/research-audit-2026-05-04.md, bitworld/among_them/players/modulabot/TODO.md</sub>

#### 34. Build a frame-buffer-to-image converter and annotated perception overlay; capture compact attributable replays validated against real frames
`generic`

The highest-leverage tool for a perception-driven player is an overlay rendering the raw frame beside an annotated frame showing exactly what the policy consumes: detected sprite boxes with color/label, projected (world-to-screen) object positions, planned path/waypoints, camera-lock status, phase/role, stuck ticks, velocity -- it turns 'perception is broken' into a specific bug (wrong color index, missed sprite, bad projection). Build two converters early so a vision-capable agent can inspect frames directly: (1) raw .npy frame buffer to PNG with the correct palette (e.g. PICO-8); (2) a text-render to a 128x128 grid of A-P characters (one per palette color, ~16KB) the agent reads in-context to reason about what perception SHOULD detect vs what it does. Capture full game replays as fixed-size binary frames (e.g. 16384 bytes/frame, ~24 MB/min) so trace size is predictable, and stamp experiment metadata (experiment_id, git_sha) into the trace for attribution; do NOT record DERIVED data (e.g. the perception mask) -- re-derive it in the parity harness to keep files compact and the source of truth canonical. Arm trace init BEFORE constructing the policy (native trace-init before native new-policy so env vars like MODULABOT_TRACE_DIR and its level/snapshot-period/meta/frames-dump variants take effect in the FFI load path, not only the CLI path). Rotate external frame dumps with a sweep tool that keeps the last K (e.g. 10) runs plus a RETAIN sentinel and deletes ONLY the large frames dump (never manifest/events/decisions/snapshots), with --dry-run/--verbose. Anchor the perception/parsing test suite on REAL captured frames as fixtures (a capture client records frames to .npy with per-frame metadata JSONL, a timeline tool summarizes view transitions, a fixture extractor turns one captured frame into a test fixture with draft assertions, a PNG renderer via the palette), not synthetic frames, so the parser is validated against actual renderer output. To capture a view gated behind a proximity check, override the config distance/range thresholds (killRange:1000, reportRange:1000) rather than solving navigation, drive all view-exploration agents from a SINGLE launcher with per-target configs storing frames periodically plus on change, and bias change-detection toward false positives (redundant frames are cheaper than a missed transition).
  <sub>sources: opencode:ses_20b179f2fffeJe4vpbx2D4tWuR, opencode:ses_20c066070ffeBjXMnfQULai0Lw, opencode:ses_1ffe75d89ffeHvW4p2ARluHh51, opencode:ses_21fc35ba9ffeK5TywAEvVvlBBl (+6)</sub>

#### 35. A hand-built navigation waypoint graph needs companion authoring tooling
`loop` · ⚠ _session-derived, unverified_

A hand-built navigation waypoint graph stays maintainable only with companion authoring tooling, so budget for a visual map editor (not just the runtime) that displays the map, auto-suggests waypoints, lets you create/edit/remove/drag nodes and edges, regenerates edges while pruning ones that intersect walls, and pins task waypoints so they can't be moved accidentally.
  <sub>sources: opencode:ses_204495c66ffe7u8C8SOiEX1zdH</sub>

#### 36. Build a prompt-eval harness that replays captured LLM contexts and scores objectively
`loop`

Build a prompt-eval harness that replays captured LLM call contexts (captured only under an opt-in flag) against the live provider or a mock and scores objective properties (valid JSON, target is a living player, respects forbidden-ally list, chat within length cap, no AI self-reveal), emitting CSV plus per-call-kind summaries.
  <sub>sources: bitworld/among_them/players/mod_talks/LLM_SPRINTS.md</sub>
