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Stateful time-bounded improvement loop with evaluator contract and dual logging
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Baseado na classificação ocupacional SOC
Agent Communication Protocol — fan-out dispatch reference for Claude plugin orchestration
Disciplined 6-phase bug diagnosis loop. Build feedback loop, reproduce, hypothesise, instrument, fix with regression test, cleanup with post-mortem. Use for all bugs — bypasses PRD entirely. Ends with advisor-gate completion gate.
Interview-based planning skill. Ask one question at a time with recommended answers, cross-reference codebase, detect and defer to project-level planning conventions, and synthesize a concise plan that feeds vertical-slice. Use for features, small changes, and bug scoping.
Run the groundwork acceptance test harness. Documents how to test the plugin locally.
MANDATORY user acceptance testing before advisor-gate. Enforces real end-user testing — TUI via tmux/expect, API via real HTTP calls in docker-compose, web apps via Playwright browser. Unit/integration tests are INSUFFICIENT unless they exercise the system exactly as end-users experience it. No exceptions.
Engage maximum parallel fan-out mode. Use when you want to work 10x faster by dispatching all independent work simultaneously to specialist agents. Triggers on "ultrawork", "ulw", "fan out hard", "go parallel".
| name | autoresearch |
| description | Stateful time-bounded improvement loop with evaluator contract and dual logging |
| argument-hint | [--mission-dir <path>] [--max-runtime <duration>] [--resume <run-id>] |
Autoresearch is a stateful skill for bounded, evaluator-driven iterative improvement. It runs one mission at a time, iterates through non-passing results, and records each evaluation as durable artifacts. It stops only when the max-runtime ceiling or another explicit terminal condition is reached.
deep-interview --autoresearch to generate mission.md and evaluator.json — autoresearch does NOT generate its own evaluatorpass: boolean and optional score: number.pi/autoresearch/<mission-slug>/
mission.md ← what are we trying to improve or prove
evaluator.json ← evaluator command/script reference
runs/<run-id>/
evaluations/
iteration-0001.json ← {"pass": bool, "score"?: number, "notes"?: string}
iteration-0002.json
decision-log.md ← human-readable narrative per iteration
Each run gets a new <run-id> directory. Cron-scheduled reruns append new run dirs — never overwrite.
Verify artifacts: Confirm mission.md and evaluator.json exist under .pi/autoresearch/<mission-slug>/. If not, stop and instruct the user to run deep-interview --autoresearch first.
Record state: Create runs/<run-id>/ directory. Record: mission slug, iteration count (start: 0), started timestamp, max-runtime deadline.
Iterate — repeat until stop condition:
a. Run one experiment or change cycle based on mission.md guidance
b. Run the evaluator and capture its JSON output
c. Persist evaluations/iteration-NNNN.json (machine-readable)
d. Append a human-readable entry to decision-log.md (what was tried, what the evaluator returned, what to try next)
e. Continue unconditionally — pass: false is data, not a stop signal
Stop: When max-runtime is reached or user explicitly cancels. Do not self-terminate on pass.
Summarize: Read decision-log.md and produce a summary: how many iterations ran, trend in score if present, best result seen, recommended next steps.
The evaluator must output valid JSON to stdout:
{"pass": false, "score": 0.73, "notes": "routing accuracy improved but still missing security cases"}
Only pass is required. score and notes are optional but recommended for trend tracking.
## Iteration 0001 — <timestamp>
**Tried**: <what change or experiment was run>
**Evaluator result**: pass=false, score=0.73
**Observation**: <what the result tells us>
**Next**: <what to try in the next iteration>