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# AGENTS.md — always-on heuristics (Optimization-loop learnings (hypothesis-driven A/B over a scripted Coworld policy))

Heuristics specific to THIS kind of work — optimizing a scripted Coworld player via a hypothesis-driven A/B loop with a slower data-science refit: experience requests, A/B attribution, build/package/submit, the player SDK & wire protocol, perception & navigation for game policies, cogames/coworld tooling. Game-agnostic across Coworlds but NOT general software advice. For crewrift game-specifics see the **crewrift** package; for lessons true of any software/agent work see the **generic** package.

_Load every session. 17 always-on heuristics. ⚠ marks session-derived, unverified items._

## A/B methodology & attribution

#### 1. Scale seed count to the decision, keep a fixed standard set, and reserve a true holdout
`loop`

Scale A/B seeds to the stakes: 6 seeds is directional ONLY and must never decide a ship (a vibe-coordination flag read +0.19/2W4T0L at 6 seeds but -0.12/3W4T5L at 12 on the same flag — the 6-seed win was luck); 12 seeds is the minimum accept/reject filter; ~50 seeds at the full competition horizon is the ship gate. A promising 6-seed result escalates to 12, a 12-seed result to 50/long-horizon, never directly to shipping; and a +0.775 signal over 12 seeds shrank to +0.194 over 50 fresh seeds. Use a fixed standard set (1 7 13 42 99 123 256 512 777 1024 1337 9999) so runs are comparable, but once a set has shaped hypotheses it is no longer unseen — define a SEPARATE holdout set in advance, never used to pick parameters or write hypotheses, and use it only after a single candidate is chosen. Run the small (~12-seed) sweep to FIND the tuning and the broad (~50-seed) sweep to learn whether it generalizes; expect navigation changes to swing large in both directions per-seed even when objectively better (an A* fix was neutral at 12 seeds, +15.64 to -17.61, but +5.47 mean / 41W-0T-9L at 50). Record both the seed-generation command and the expanded list so confirmation/holdout runs are reproducible.
  <sub>sources: opencode:ses_21fa102bfffedRhIfKKjobTRkK, opencode:ses_2204f83deffedZP7TPbuAnQIhs, alpha_cog/AGENTS.md, archive/cogames_playground/alpha_cog/experiments/notebook/protocol.md (+5)</sub>

## A/B methodology & attribution

#### 2. Promote on mean>=baseline AND wins>=losses; treat variance reduction and Pareto-dominance as first-class signals
`loop`

Promote a flag only if its mean is at least baseline AND it wins on at least as many seeds as it loses — the wins>=losses clause blocks lottery-ticket flags carried by one or two lucky seeds (a 6W/0T/6L candidate fails even with a positive mean), and a marginal pass like 21W/10T/19L (+0.85) with a loss tail (-10.6,-9.4) as large as the win tail (+13.4,+12.9) is knife-edge — compare against clean prior ships (V12 at 41W/9L) and offer tune-and-retest. Separate three verdicts variance conflates: 'wrong mechanism (reject)', 'right mechanism mid-tuning dip (keep iterating)', and 'real but high-variance (not-shippable, keep as lead)'; optimization over a scripted policy is non-monotonic so never kill a mechanism on its first bad seed, and try GATING a regressing behavior (aligner heart preloading went -0.63 ungated to +1.90/33W-0T-17L gated on 'hub element surplus >= 2x heart recipe'). Variance reduction at equal mean is a legitimate, sometimes superior ship signal (raise the worst-case floor on the minority split) — but a 'mean>=baseline' rule correctly REJECTS a recovery tuning that only redistributes variance (W120T1 was +0.02 on 12 seeds, -0.09 on 50, variance only 3.60->3.31). A Pareto-dominant pair (ties-or-beats on every seed) is the cleanest possible signal — call it out explicitly.
  <sub>sources: alpha_cog/AGENTS.md, alpha_cog/experiments/README.md, archive/cogames_playground/alpha_cog/experiments/notebook/protocol.md, alpha_cog/audits/research-audit-2026-05-04.md (+7)</sub>

## A/B methodology & attribution

#### 3. Local Coworld play is a smoke test only, never competitive comparison
`loop` · **negative result**

Use Coworld local play only for smoke/correctness checks. coworld run-episode overrides the manifest with a stripped config (among_them v0.1.20: maxTicks=300, tasksPerPlayer=1, voteTimerTicks=120) but leaves killCooldownTicks=900, so an imposter kill is structurally impossible in a 300-tick game (episodes finish ~5s, draw on time, zero kills) -- use it only to validate image startup, websocket protocol, trace-surface reachability, and artifact production. For real behavior use full-manifest coworld play MANIFEST_URI [PLAYER_IMAGES] (full 8-player, 10000 ticks, manifest-default cooldown) or a league. You generally cannot run other users' policies locally, so a local zero from a trivial fixture does not mean the player is broken; competitive judgment comes from experience requests.
  <sub>sources: player_labs/best_practices.md, players_checkouts/players/users/james/personal_cogs/among_them/guided_, players_checkouts/players/users/james/personal_cogs/among_them/guided_</sub>

## A/B methodology & attribution

#### 4. Match episode length and score band to the mechanism under test; late-game flags are inert at 2k
`loop` · **negative result**

Pick the A/B horizon to expose the dynamics under test (machina_1 runs 10000 steps): 500 steps is too short (hub stock undepleted, no expansion events), 2000 is the cheap discovery/regression-smoke filter, and 10000 is required for any plausible ship (~50 seeds at 10k to activate in the shipping policy). Flags whose mechanism only matters late-game are systematically undervalued at 2k and grow large at 10k: avoid-enemy-territory was +0.004 at 2k vs +0.863 at 10k; a round-trip aligner was near-inert at 2k vs +0.181 at 10k — run an exploratory 5k/10k A/B before rejecting or shipping such a flag. When a short-horizon score is ambiguous but a durability proxy (HP/survival) improved, promote to long-horizon confirmation rather than reject, judging survivability-vs-aggression changes by what actually scores (territory held over time): a change that dipped seed-42's 3k score but massively improved HP was correctly kept. Know the score band before judging (a bulbacog cogs-vs-clips score ~10 is bad, ~30-40 is good), and when a self-play gap is STRUCTURAL target major strategy fixes not single-knob tuning — a 7.9 self-play score against leaders at 35-40 means ~79k held junction-ticks vs ~350-400k; diagnose via 'what fraction of the map is held for what fraction of the 10k ticks,' not parameter sweeps.
  <sub>sources: claude-code:425dc11e-1c8d-4d12-bf44-adf01b6fa796, opencode:ses_200d7b1fbffehZb7LsbgTZtHzN, opencode:ses_20abe5b3fffetrbr0xbbFMFGgo, opencode:ses_1fbb083dfffeFNp623MAOdar2j (+15)</sub>

## Experiment discipline

#### 5. Separate upload from league submit, keep submission a deliberate manual gate, and decouple experiments from the live entry
`loop`

Keep 'upload a new version' and 'submit to a league' as separate steps and gate league submission behind explicit human approval: a new league submission may be auto-marked champion as soon as it qualifies, so an unvalidated version risks rolling back the current champion. Use a new uploaded version for experience-request evals and A/B tests, but do not submit to a league until validated. Keep submission a deliberate MANUAL gate even in a 'commit after every experiment' workflow via a wrapper exposing ship/status/history subcommands requiring an explicit version (never auto-ship). Any nightly auto-ship/refit cron MUST gate before shipping so a bad fit can never auto-submit — require cross-validation AUC >= a threshold, a minimum corpus size, the full player test suite green, and a local smoke test; any failure aborts and leaves the current champion untouched with the reason logged. Document its failure modes: the cron silently skips if the host Mac is asleep at run time, and an expired login token aborts it until manual re-login. Treat a new rewrite (e.g. a pixel-first Python agent) as a parallel experiment and explicitly do not modify the production bot or its daily-league submission flow while building it, so the experiment cannot regress the live entry; likewise do not pull a large dependency forward into an earlier stack entry just to test a feature that depends on it (the among-them task-icon scan depends on localize, so it was deferred to the same stage as localize rather than injecting a synthetic camera offset or pulling localize forward into a mega-commit).
  <sub>sources: claude-code:0e7b14ca-ffd5-4137-9456-47485d3c6f87, claude-code:63658f66-83c7-482e-a805-d58527e10975, players/archive/players/among_them/coborg/README.md, players/archive/players/among_them/coborg/PLAN.md</sub>

## Experiment discipline

#### 6. Use debug side channels for eval only; a policy that reads them is cheating
`loop` · ⚠ _session-derived, unverified_

Treat any debug side channels a game exposes (commands that dump global state or reward) as eval/diagnostic tools ONLY, never as inputs to the shipped policy. They are useful for validating a player's inferences against ground truth, but a policy that reads them is cheating and will not transfer to real matches.
  <sub>sources: claude-code:d4cde1e2-f4b5-4bd6-bf05-dd48002edf58</sub>

## Build, package & submit

#### 7. Upload freely, submit rarely: the two-gate async Coworld lifecycle
`loop`

Treat `coworld upload-policy <image> --name <name>` and `coworld submit <name>[:vN] --league <id>` as fundamentally different. Upload is routine, inert, ungated (Gate 1): it registers a new monotonic version vN (server-assigned next_version=(MAX or 0)+1, NO skip-if-unchanged even for identical bytes — though the ECR push itself dedupes a deterministic SHA256 client_hash of the saved image tar, ~3s vs ~60-90s), competing with nothing, so upload every iteration. Submit (Gate 2, the human's, rare) is public, can make you champion once qualified, and is effectively permanent (no unsubmit/deactivate/delete; rejected/failed submissions persist forever). Submit omitting :vN resolves to the latest version; pin X:vN to submit or roll back a non-latest build. Submission is a two-step CLI flow then ASYNC placement: upload-policy mints a versioned policy (<name>:vN auto-incrementing GLOBALLY per name, not per season), then `coworld submit <name>:<ver> --league <id>` returns a sub_... id and placement runs pending -> processing -> placed/rejected (~6-7 min for CvC; the CLI exits immediately and the first scheduled league round only runs on the next 30-minute boundary). Resolve league IDs LIVE via `coworld leagues` (IDs rotate between seasons; a stale id submits to the wrong game), match the human's existing naming/versioning (continue the existing policy name, e.g. v9, not $USER-my-player) by listing existing versions first, and submit only when the player is demonstrably better AND the human gives explicit go-ahead — not submitting is your rollback.
  <sub>sources: player_labs/AGENTS.md, player_labs/README.md, player_labs/best_practices.md, player_labs/.claude/skills/coworld-policy-lifecycle/SKILL.md (+13)</sub>

## Build, package & submit

#### 8. Build AND run every player image for linux/amd64; on Apple Silicon build inside Docker
`loop` · **negative result**

Production runs on linux/amd64 Kubernetes nodes and the upload/episode-runner hard-rejects arm64, so 'works on macOS' proves nothing: always `docker buildx build --platform linux/amd64 ... --load` and `docker run --platform=linux/amd64`. On Apple Silicon the shippable artifact MUST be built inside Docker under qemu emulation; a host-native compile produces an arm64 binary (e.g. a stray notsus.out / evidencebot Mach-O arm64) that runs locally but fails on upload or in the cluster. Runners may pull on a different arch: an amd64-only GHCR image fails to schedule ('no matching manifest for linux/arm64/v8') and a wrong-arch image that schedules exits with 'exec format error' (almost always wrong-arch, not a bot bug) — fix by pushing a multi-arch manifest and verify with `docker pull --platform <arch>`. Tournament validation builds/exercises the policy inside the Linux container (e.g. ghcr.io/metta-ai/episode-runner:latest), compiling platform-specific artifacts (.so) under Linux.
  <sub>sources: personal_labs/crewrift_lab/docs/designs/building_players.md, player_labs/.claude/skills/coworld-local-run/SKILL.md, metta/agent-plugins/kitchensink/skills/ks.build-submit-policy/SKILL.md, players_checkouts/players/docs/coworld-player-packaging.md (+4)</sub>

## Build, package & submit

#### 9. A zero-image / wrong-arg smoke runs the reference player; pass --run everywhere or your change is never under test
`loop` · **negative result**

A smoke run with ZERO policy images silently runs the coworld's REFERENCE player (run-episode reuses one positional image for every self-play slot), so your change is not under test. For an image bundling multiple roles/entrypoints, pass your policy's run command explicitly via `--run` on `upload-policy` AND on every local `run-episode`/`play` (e.g. `--run python --run /app/player.py`): this is the quietest failure in the loop — omit it and run-episode silently substitutes the manifest's reference/baseline player (its CMD, e.g. /bin/notsus), so your change uploads and submits fine but is never tested (or fails 'no such file or directory' if that baseline entrypoint is absent from your image). Substituting a different image keeps the manifest player env (a leftover COGAMES_POLICY_URI can hijack a naive image) but DISCARDS the manifest run, so the substitute needs a working Docker CMD or an explicit --run. The Gate-1 signal is connect -> play -> exit cleanly (the CLI exits non-zero on container crash, health timeout, rejected token, or results.json schema failure, and a score of 0 is NOT a smoke failure); when a player 'plays the whole episode but still fails', check the container EXIT CODE first ('Player container exited with status 1'), not the gameplay. Beware fixtures that SKIP when a Docker image is missing: a full run can pass while testing nothing, so deliberately decide whether missing images should fail, build-on-demand, or skip, and say plainly when the build and test halves have no single orchestrating command.
  <sub>sources: bitworld/among_them/players/how_to_submit_coworld_policy.md, player_labs/.claude/skills/coworld-local-run/SKILL.md, player_labs/.claude/skills/coworld-policy-lifecycle/SKILL.md, metta/agent-plugins/kitchensink/skills/ks.build-submit-policy/SKILL.md (+6)</sub>

## Build, package & submit

#### 10. Gate 1 is a smoke check, never a matchup; competitor policies are never locally runnable
`loop`

Coworld Gate 1 (the agent's, every iteration) is ONLY a smoke/correctness check, never comparative: confirm the change took and the player connects, plays, and exits without crashing via a local non-comparative run, then rebuild and upload as a new version. You cannot run other users' policies locally so judge competitiveness only via experience requests against the hosted environment. For a league game only two things are publicly downloadable: the game plus its bundled baseline player(s) (for crewrift, `notsus`) via `coworld download`, and leaderboard metadata via `coworld memberships`/`submissions`/`results`. Competitor policies are NOT locally runnable — each submission's image is pushed to per-user private ECR (immutable, not pullable by others) and the Observatory publishes no source; there is no flag to turn a competitor submission into a runnable opponent, so do not promise that. To field a downloaded BASELINE as an opponent in the optimizer: (a) `coworld download`, (b) set the game DB row's download_dir and images_json columns to the on-disk manifest, (c) run the library sync (resolveGameRunSpec({download:true}) reads the local docker tag only when download_dir is set; lib/games/game-spec.ts). Keep a version log mapping each uploaded version to its changes, image, submission id, qualifier/division membership.
  <sub>sources: player_labs/AGENTS.md, player_labs/best_practices.md, coworld-source-repos.crewrift-upload/optimizers/.cursor/skills/downloa, coworld-source-repos.crewrift-upload/optimizers/.cursor/skills/downloa</sub>

## Build, package & submit

#### 11. Editing source does not change the running player; rebuild the image AND the shared library before judging a fix
`loop` · **negative result**

Editing policy/wrapper/native source does NOT change the running Coworld/cogames player: the agent runs from a built Docker image (and, for FFI-fronted players, from a compiled shared library loaded by the Python launcher). After ANY change you must rebuild the image AND the shared library (not just the standalone native binary) before the next match, or you run a 'fixed' match against stale code and draw wrong conclusions. State the rebuild explicitly as part of 'fixed' (e.g. build_guided_bot.py / build_evidencebot_v2.py must run before play_local.py). Tells of stale code: a 0-byte/empty trace file, or expected stderr debug output that never appears. A crash whose traceback path is /usr/local/lib/python3.12/site-packages/... (not .venv/...) is a missing dependency in the IMAGE, not the local venv — `uv add` only fixes in-process scripts and pytest, so add the dep to the Dockerfile and rebuild. Tag diagnostic-only vs actual-fix images separately so the last-known-good image stays available for A/B, and don't trust a previously-uploaded image tag without a smoke check (stale ECR tags may be linux/amd64 yet lack the runnable wrapper /bin/<player>). The same rule applies to baked data: editing nav_graph.json requires re-baking nav_paths.bin before a live run reflects it.
  <sub>sources: codex:019e2819-d56b-72f3-9349-60fc965b6ed2, opencode:ses_1fc949520ffe2UNDsOqbbL5T9a, opencode:ses_2044035bcffeRp9M35307uA4gR, opencode:ses_204495c66ffe7u8C8SOiEX1zdH (+8)</sub>

## Build, package & submit

#### 12. Submitted policy bundle must do no I/O and import nothing from experiments/
`loop`

A submitted game-policy bundle (Coworld/CvC) must do NO network I/O, NO LLM calls (beyond the sanctioned upload-time-injected credential path), NO filesystem writes outside its scratch, and NO cross-episode persistence: the outer improvement loop (LLM edit-proposals, trace mining, A/B harnesses) lives entirely outside the bundle, and all 'learning' is baked into program text and a frozen skill library at bundle time. Enforce it mechanically by keeping outer-loop tooling in an experiments/ tree and forbidding the shipped package from importing anything under experiments/ (the reverse is fine); `cogames create-bundle`/`validate-bundle` enforces the boundary. A helper shared by both inner loop and offline analyzer must live in the inner loop (lean enough to ship) or be duplicated as a minimal copy, never left in ambiguous shared code.
  <sub>sources: opencode:ses_21a181ab8ffe3nrttiVWHpM4LJ, opencode:ses_21fa102bfffedRhIfKKjobTRkK, opencode:ses_2204f83deffedZP7TPbuAnQIhs, alpha_cog/AGENTS.md (+2)</sub>

## Harness, SDK & wire protocol

#### 13. Structure a Coworld agent as a deterministic inner loop plus a swappable strategy loop (cyborg two-loop SDK)
`loop`

Build game agents on the Coworld Player SDK (players.player_sdk, the two-loop strategy/reflex framework formerly Coborg/Cyborg) rather than ad-hoc scaffolding, but recognize it as a GENERIC skeleton generic over six type parameters (Observation, Percept, Belief, ActionState, Intent, Command); you must supply every game-specific piece (perceive, update_belief, resolve_action, Mode subclasses, a Strategy). The fast deterministic inner loop runs every tick (<1ms): raw observation -> percept -> belief update -> single mode decision -> action command, reused across live and replay. The slower replaceable outer/strategy loop takes a read-only BeliefSnapshot and emits exactly one typed ModeDirective plus an inferences dict the framework swaps in wholesale (don't mutate in place; avoids async races); it cannot mutate inner-loop state and must be idempotent on stable input so extra inner ticks before consumption stay correct. The inner loop must NEVER block on strategy: connect the loops with size-one newest-wins buffers (OverwriteBuffer/latest-value) so a stale directive is dropped not queued-and-executed after the phase changed; if no fresh directive is ready it applies a configured default, and directives carry a TTL so on expiry the runtime falls back to default. Share state only through lock-protected SharedMemory under short read()/write() scopes, and NEVER hold the lock across an LLM/network/summarization call -- build context under read(), release, then call the model. Pick the runner to match blocking behavior: SynchronousStrategyRunner (deterministic/cadence), ThreadedStrategyRunner (blocking clients), AsyncStrategyRunner (async LLM clients, build it inside the running loop), ManualStrategyRunner (tests). The SDK's coworld_json_bridge.py speaks JSON coworld.player.v1 for token/grid games and is NOT usable for a binary Sprite-v1 game, which must write its own websocket bridge.
  <sub>sources: claude-code:940b2eb9-3677-4c05-8754-c9cfa163f531, codex:019e02f7-0f59-7f43-ad7b-f4e3c2d37a2f, codex:019e18b2-4478-78d0-8882-752b7b0d55c9, codex:019e18d7-532e-7540-82ea-9abe1726aec7 (+8)</sub>

## Harness, SDK & wire protocol

#### 14. Operate the optimizer platform through its MCP tools, reading the LIVE tool list and preferring async pairs
`loop` · tool: `coagent-optimizer-mcp`

Operate the optimizer platform through its ~63-68 MCP server tools (thin validated adapters over typed platform functions) as the first-class interface for running episodes, evaluating/gating, creating/mutating/promoting policies, submitting to leagues, reading standings, and driving the loop. Do NOT bypass them with `bunx tsx -e`, hand-rolled scripts, direct Postgres/Drizzle writes, or raw coworld/docker CLI when a tool exists, because that skips guardrails. The ONLY sanctioned shell escape hatch is the three primitive domains with no tool: raw filesystem/source edits a tool can't express, Coworld CLI auth/login, and Docker build internals; when you drop to shell, say so and name why. Before guessing tool schemas, verify the server is up and read the LIVE tool list as ground truth (a static source count undercounts the live set): over Docker HTTP `curl http://localhost:3100/healthz` then POST tools/list to http://localhost:3100/mcp; for local non-Docker `bunx tsx mcp/smoke.ts`. The server opens Postgres LAZILY (only tool CALLS hit the DB) so it boots and lists tools without a database, and every tool returns structured JSON converting failures to { isError: true } -- branch on isError instead of defensive shell wrapping. Prefer the non-blocking ASYNC tool pairs over blocking *_sync variants because episodes/evals take MINUTES (episode_launch+episode_status, eval_enqueue+eval_status, eval_launch_campaign+eval_get_campaign). For LOCAL cross-policy episodes use episode_launch `lineup` (omit=self-play; {mode:'baseline-vs-candidate',opponentPolicyId}; {mode:'library-mix'}; {mode:'roster'}); for HOSTED play vs league champions use experience_request_create. Compose the skills in order: optimizer-mcp -> improve-policy -> policy-eval-gate -> continuous-optimization, with optimizer-safety always active; one iteration is OBSERVE (episode_launch/status, eval_aggregate_metrics) -> DIAGNOSE (episode_events, memory_search) -> ACT (policy_create_version for tuning, policy_mutate for evolution, skill_propose_edit for meta-improvement) -> VERIFY (eval_enqueue/status) -> PROMOTE only on gateDecision==='promote' then policy_promote and memory_create. Call coworld_status before any league op. Watch schema deviations (eval_launch_campaign requires gameId+suiteId+name+candidates[]; gate_dry_run requires a structured candidate; skill_propose_edit requires name+description).
  <sub>sources: coworld-source-repos.crewrift-upload/optimizers/.cursor/skills/mcp-opt, coworld-source-repos.crewrift-upload/optimizers/.cursor/skills/mcp-opt, coworld-source-repos.crewrift-upload/optimizers/docs/optimizer-primiti, coworld-source-repos.crewrift-upload/optimizers/CLAUDE.md (+5)</sub>

## Harness, SDK & wire protocol

#### 15. Scope 'no raw frame-capture' to live play paths only
`loop` · ⚠ _session-derived, unverified_

An 'API-only / no raw frame-capture' project rule targets capturing frames during LIVE play to bypass the official observation API. Offline asset bakers and inspectors that read already-recorded trace data or static bitmaps are fine. Explicitly exempt those offline tools in AGENTS.md so a future agent does not delete them as rule violations.
  <sub>sources: auggie:02627f61-aac0-42a0-a290-3595e010abba, auggie:5d1ff8d5-2505-4dca-af45-56d3070363e2</sub>

## Agent workflow & collaboration

#### 16. Never push/submit live tournament entries without explicit human approval; bucket upload changes
`loop`

Do not push, publish, upload, or submit live leaderboard/tournament entries without explicit human permission; local branches and commits are permitted. After a multi-fix upload effort, report changes as distinct buckets -- required-for-certification, required-only-for-this-upload-pipeline, and genuinely durable upstream changes -- and track which checkouts are left ahead of upstream.
  <sub>sources: archive/cogames_playground/BULBACOG_LEADERBOARD_GOAL.md, codex:019ea9fe-29e5-7180-b111-57e3039314a2</sub>

## Agent workflow & collaboration

#### 17. Layer agent guidance as game-agnostic root process plus a game-specific overlay; human steers, agent enables
`loop`

Layer agent guidance as game/world-agnostic root docs plus a game-specific overlay, with a clear conflict rule: root defines PROCESS (the improvement loop, gates, shared skills) and the game-specific layer defines the GAME; read root first, then the overlay, and keep game-specific code/tests/rules/tooling under that game's own lab directory while root code/docs/lab-wide skills serve any game. In the human+agent player-improvement loop, the human originates strategic direction and judges gameplay quality while the agent implements changes, builds the observability that reveals where improvement is possible, measures rigorously, and holds the correctness gate. Turn report signals into improvement hypotheses by investigating replays/logs/code for the mechanistic WHY a behavior happens or fails, present candidate directions to the human as OPTIONS not directives, and prefer exploring an unexplored mechanism over a marginal tweak to something well-tuned. The agent's highest-leverage work is making the human's strategic judgment cheap and well-informed, not replacing it.
  <sub>sources: personal_labs/crewrift_lab/.claude/skills/crewrift-diagnose/SKILL.md, personal_labs/crewrift_lab/AGENTS.md, player_labs/AGENTS.md, claude-code:3c867a71-3057-4e87-a149-4185e82ee728</sub>
