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# tools/ — executable substance (crewrift tier)

Lessons embodied by a script/program (scripts live in the source repos).

#### 1. 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>

#### 2. 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>

#### 3. Defer A* to the chosen target; rank candidates with cheap local geometry
`crewrift` · ⚠ _session-derived, unverified_ · tool: `frontier.py` · _see related: S019 (other tier)_

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>

#### 4. 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>

#### 5. 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>
