القائمة
Generate and test policy improvement hypotheses from eval evidence. Use when deciding what code or strategy change to make after analyzing replays and opponent behavior.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Use to identify and download Coworld episode artifacts — replays, results, and per-agent logs — for completed episodes, or STREAMED live from a still-running experience request (--watch: each episode downloads as it finishes, no waiting for the batch). Triggers: 'download the replay/logs for episode X', 'pull the last N episodes <player> played', 'fetch artifacts for this experience request / pool / round', 'stream artifacts while the request runs', 'get the episode data for post-mortem'. Game-agnostic (Crewrift, amongthem, etc.).
Use to create and monitor Coworld experience requests — hosted batches of episodes you define (target, roster, roles, count) for evaluating a policy against a live field. Triggers: 'run crewborg vs the top opponents', 'make an experience request', 'request N hosted games', 'A/B a policy against the league', 'measure the imposter', 'set up an evaluation battery'. DEFAULT FLOW after create: launch the STREAMING pipeline in the background (step 4) — artifacts download and analysis prep overlap the still-running episodes; don't wait for the batch to drain.
Use to run your own built player policy in a LOCAL Coworld episode and watch it — the Gate-1 smoke test ('did my change take, does it connect → play → exit cleanly?'). Triggers: 'smoke test the player locally', 'run a local game', 'run my policy locally and watch it', 'does the build still work', 'open the replay locally'. Game-agnostic; this is correctness only, NOT a competitive matchup (you can't run other users' policies locally).
Use to submit an ALREADY-UPLOADED policy version to a Coworld league and monitor whether it QUALIFIES, then competes (and becomes champion). Triggers: 'submit crewborg to the league', 'did it qualify', 'is it disqualified / champion', 'watch the qualifier', 'monitor standings after submitting'. SUBMIT is the public, effectively-irreversible, champion-making action — explicit human go-ahead only. (Building + uploading a version is the separate `build & upload` skill.)
Use to decide whether a crewborg change ACTUALLY helped — A/B the candidate against the baseline head-to-head, fresh, right now, against the same field. Triggers: 'did my change help', 'compare v71 vs v70', 'A/B test crewborg', 'is the candidate better', 'did fixing the imposter regress the crew'. Runs two MATCHED fresh experience requests, diffs role-split metrics with significance (compare.py), renders a comparison report, and guides a qualitative side-by-side. It's also the 'designed run' that crewrift-experiment hands off to.
Use to build and query the Crewrift event warehouse — a policy-indexed DuckDB/Parquet dataset of per-tick gameplay events (kills, follows, proximity, votes, tasks, chat) over many episodes — for deep, mechanistic, cross-episode questions about a policy's behaviour. Triggers: 'how often is crewborg near a crewmate without killing', 'who trails crewborg', 'where does it abandon tasks', 'does it vote real imposters', 'build the warehouse for this round', 'query the events', 'stream an experience request into a warehouse'. For a FRESH experience request, stream_eval.py builds the warehouse WHILE the episodes run (the default; builds are incremental). This is the deep-dig mainstay after crewrift-survey flags something.
| name | policy-hypothesis-loop |
| description | Generate and test policy improvement hypotheses from eval evidence. Use when deciding what code or strategy change to make after analyzing replays and opponent behavior. |
Make one clear change at a time unless running a deliberate candidate campaign.
Each hypothesis must include:
Example:
Observation: Crewmate mean is −8 driven by vote-timeout penalties in ~20% of meetings.
Causal guess: the cursor-confirm path misses the deadline when an LLM meeting call is slow.
Change: auto-submit the deterministic top-suspect vote at ≤72 ticks regardless of LLM state.
Expected movement: vote-timeout penalties → ~0; crewmate mean +8.
Risk: voting too early on weak evidence ejects crew.
Validation: 40 games vs broad field + 40 targeted; per-role + penalty decomposition.
Rollback: crewmate win rate or imposter win rate drops.
When testing several variants:
crewborg-optimization/crewborg-suspicion-tuning).map-navigation).Before changing policy, record the hypothesis. After eval, record: