--- name: autoresearch version: 1.0.0 category: 思维与人格 trigger_words: tags: platform: coze source: DeepseekModel source_url: https://deepseekmodel.com/skill?id=yeachan-heo-oh-my-claudecode-skills-autoresearch-skill-md --- name autoresearch description Stateful single-mission improvement loop with strict evaluator contract, markdown decision logs, and max-runtime stop behavior argument-hint [--mission-dir ] [--max-runtime ] [--cron ] [--resume ] level 4 Autoresearch is a stateful skill for bounded, evaluator-driven iterative improvement. It owns one mission at a time, keeps iterating through non-passing results, records each evaluation and decision as durable artifacts, and stops only when an explicit max-runtime ceiling or another explicit terminal condition is reached. You already have a mission and evaluator from /deep-interview --autoresearch You want persistent single-mission improvement with strict evaluation You need durable experiment logs under .omc/autoresearch/ You want a supported path for periodic reruns via Claude Code native cron You need evaluator generation at runtime — use /deep-interview --autoresearch first You need multiple missions orchestrated together — v1 forbids that You want the deprecated omc autoresearch CLI flow — it is no longer authoritative - Single-mission only in v1 - Mission setup/evaluator generation stays in `deep-interview --autoresearch` - Evaluator output must be structured JSON with required boolean `pass` and optional numeric `score` - Non-passing iterations do **not** stop the run - Stop conditions are explicit and bounded, with max-runtime as the primary strict stop hook Canonical persistent storage lives under .omc/autoresearch// and/or .omc/logs/autoresearch// . Minimum required artifacts: mission spec evaluator script or command reference per-iteration evaluation JSON markdown decision logs Recommended canonical shape: .omc/autoresearch// mission.md evaluator.json runs// evaluations/ iteration-0001.json iteration-0002.json decision-log.md Reuse existing runtime artifacts when available rather than duplicating them unnecessarily. 1. Confirm a single mission exists and evaluator setup is already available. 2. Ensure mode/state is active for `autoresearch` and records: - mission slug/dir - evaluator reference - iteration count - started/updated timestamps - explicit max-runtime or deadline 3. On every iteration: - run exactly one experiment/change cycle - run the evaluator - persist machine-readable evaluation JSON - append a human-readable markdown decision log entry - continue even when evaluation does not pass 4. Stop when: - max-runtime ceiling is reached - user explicitly cancels - another explicit terminal condition is recorded by the runtime Claude Code native cron is a supported integration point for periodic mission enhancement. In v1, prefer documenting/configuring cron inputs over building a large scheduler UI. If cron is used: keep one mission per scheduled job preserve the same mission/evaluator contract append new run artifacts rather than overwriting prior experiments Do not hand execution back to omc autoresearch Do not create multi-mission orchestration Prefer reusing src/autoresearch/* runtime/schema helpers where they already match the stricter contract Keep logs useful to humans, not only machines