Run repeated change-and-evaluation cycles for one predefined improvement mission within an explicit runtime limit. Save each evaluation as JSON and each decision in readable Markdown logs, with support for periodic reruns through Claude Code native cron.
中文简介正在准备中。当前显示英语简介。
Stateful single-mission improvement loop with strict evaluator contract, markdown decision logs, and max-runtime stop behavior
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
<Use_When>
/deep-interview --autoresearch.omc/autoresearch/<Do_Not_Use_When>
/deep-interview --autoresearch firstomc autoresearch CLI flow — it is no longer authoritative
</Do_Not_Use_When><Required_Artifacts>
Canonical persistent storage lives under .omc/autoresearch/<mission-slug>/ and/or .omc/logs/autoresearch/<run-id>/.
Minimum required artifacts:
Recommended canonical shape:
.omc/autoresearch/<mission-slug>/
mission.md
evaluator.json
runs/<run-id>/
evaluations/
iteration-0001.json
iteration-0002.json
decision-log.md
Reuse existing runtime artifacts when available rather than duplicating them unnecessarily. </Required_Artifacts>
<Workflow> 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 </Workflow><Cron_Integration> 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:
<Execution_Policy>
omc autoresearchsrc/autoresearch/* runtime/schema helpers where they already match the stricter contract还没有评价。欢迎分享你的使用感受。
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Write or revise instructions, specifications, tickets, and skill files so an agent can act without prior chat history. Make rules checkable, explain their reasons, define completion criteria, and keep authoritative information in one place.
中文简介正在准备中。当前显示英语简介。
Simplify bloated AI-generated code by removing dead code, duplicated logic, and unnecessary layers while checking that existing behavior stays intact. Use a limited cleanup scope, verify changes with relevant checks, or request a review without edits.
中文简介正在准备中。当前显示英语简介。
Review module relationships and change history to identify structural improvements in a codebase. Receive ranked candidates with file references, suggested changes, and risks, plus a recommendation for where to start, without modifying code.
中文简介正在准备中。当前显示英语简介。
Send questions, review requests, or implementation tasks to a supported local AI command-line tool through one shared command. The wrapper handles provider-specific options and saves the result as a Markdown file for later reference.
中文简介正在准备中。当前显示英语简介。
Turn an unclear idea into linked decision tickets on a repository’s issue tracker. Work through one decision per session, record outcomes and dependencies, and prepare a brief defining the objective, scope, and exclusions for delivery.
中文简介正在准备中。当前显示英语简介。
Turn a short product idea into working code through requirements analysis, technical planning, implementation, testing, and review. It coordinates agents across these stages, fixes detected problems, and reports assumptions that still need a human decision.
中文简介正在准备中。当前显示英语简介。