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data-science-refit
Use for Crewrift-specific data science refit recipes when optimizing a player.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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Use for Crewrift-specific data science refit recipes when optimizing a player.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Use when improving a Coworld player through one diagnosed failure and completed comparison evidence.
Use when compiling, validating, and uploading a notsus change as a new Crewrift policy version.
Use when decoding a Crewrift replay for authoritative deaths, survival, movement, roles, tasks, or rewards.
Use when verifying that the notsus Bedrock vote advisor fires before an advisor-sensitive evaluation.
Use when classifying a stuck, failed, empty, or zero-scoring evaluation as infrastructure or policy failure.
Use when finding and statistically confirming a reproducible Crewrift crux before changing the policy.
| name | data-science-refit |
| description | Use for Crewrift-specific data science refit recipes when optimizing a player. |
On-demand recipes (2). Trigger→action heuristics; pull the relevant one when its situation arises.
crewrift · tool: suspicion_lab/tools/fit.py
A suspicion posterior is consumed downstream as a probability, so select L1 strength C by ranking on mean CV log-loss (calibration), breaking ties with AUC -- not on AUC alone; after fitting, print an out-of-fold calibration table (predicted-probability bucket vs actual positive rate) and compare the fitted intercept against the empirical prior logit to catch miscalibration before shipping. Evaluate vote/decision quality ONLY on out-of-fold posteriors so the shipping gate is not inflated by in-sample fit, and run held-out meeting decisions through a vote-policy grid, shipping only policies that beat the always-skip baseline on net parity. Ship the input-transform contract (BIN_SPEC edges, LINEAR_CLIP ceiling) INSIDE the weights JSON and mirror it exactly in the runtime scorer -- offline fit and live agent must apply identical transforms or the weights are meaningless; clip count features to a bounded range (LINEAR_CLIP=5) so one extreme game cannot dominate. sources: personal_labs/crewrift_lab/suspicion_lab/tools/fit.py, personal_labs/crewrift_lab/suspicion_lab/tools/eval.py, personal_labs/crewrift_lab/suspicion_lab/README.md
crewrift · ⚠ session-derived, unverified
The Crewrift expand-replay event log (crewrift-events/v1, per-episode JSONL) carries vote_called_button (player=caller slot, ts=tick), kill, body_state, and player_state samples (x,y,room,kill_cooldown,button_calls_used) plus map_geometry; reconstruct a runner's button approach by walking that player's player_state samples backward from their vote_called_button tick (confirmed config: kill_cooldown_ticks 500, button_calls 1, imposter_count 2). When hunting missed votes, detect from episode result fields FIRST (results.json has per-slot vote_timeout, vote_skip, vote_players; scan keyed by policy_version_id NOT seat slot because rotate_seats moves the player) and expand_replay only the few episodes still suspicious. When a format stores only config plus join/leave/input records, first check whether the results artifact exposes imposter/crew arrays directly; otherwise recover role/task assignment from the container startup logs (often a compact assignment table) or by re-running the sim's start routine from the replay config. sources: claude-code:ef964049-4e16-4c32-b7f1-32871349d20a, codex:019eaea8-4f78-7072-b4b0-539df963b7df, codex:019ea9af-7020-7550-b427-d3478fed2057