Christian Nennemann
4f8e2a9962
- archeflow-decision.sh records decision points during runs - archeflow-replay.sh: timeline, whatif, compare commands - What-if replay with adjustable archetype weights - /af-replay skill for interactive use - Tests in archeflow-replay.bats
2.0 KiB
2.0 KiB
name, description, user-invocable
| name | description | user-invocable |
|---|---|---|
| af-replay | Replay and analyze a recorded ArcheFlow run: decision timeline and weighted what-if. Usage: /af-replay <run-id> [--timeline|--whatif|--compare] [--weights arch=w,...] | true |
ArcheFlow Run Replay
Inspect a completed or in-progress run logged in .archeflow/events/<run_id>.jsonl. Use this to study which archetypes drove outcomes and to simulate weighted consensus (what-if).
Recording (during PDCA)
After each meaningful orchestration choice, log a decision point (in addition to review.verdict where applicable):
./lib/archeflow-decision.sh <run_id> <phase> <archetype> '<input_summary>' '<decision>' <confidence> [parent_seq]
Fields stored: phase, archetype, input, decision, confidence, ts (event timestamp). The event type is decision.point.
Lower-level alternative:
./lib/archeflow-event.sh "$RUN_ID" decision.point check guardian \
'{"archetype":"guardian","input":"diff","decision":"needs_changes","confidence":0.85}' 7
Commands (from project root)
| Action | Shell |
|---|---|
| Timeline | ./lib/archeflow-replay.sh timeline <run_id> |
| What-if | ./lib/archeflow-replay.sh whatif <run_id> [--weights guardian=2,sage=0.5] [--threshold 0.5] [--json] |
| Both | ./lib/archeflow-replay.sh compare <run_id> [--weights ...] |
- Timeline lists
decision.pointrows andreview.verdict(check phase). - What-if reads the last
review.verdictper archetype in check. Original outcome uses strict any-veto (any non-approve → BLOCK). Replay uses weighted mean strictness: each reviewer contributes weight × (1 if not approved, else 0); BLOCK if mean ≥ threshold (default 0.5). --jsonemits machine-readable output for dashboards or scripts.
Learning effectiveness
Correlate decision.point confidence and verdicts with cycle outcomes (cycle.boundary, run.complete) and ./lib/archeflow-score.sh extract to see which archetypes add signal for which task shapes.