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autoresearch

Stateful single-mission improvement loop with strict evaluator contract, markdown decision logs, and max-runtime stop behavior

DeepseekModel 官方收录技能 质量 优秀 · 90 v1.0.0

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https://deepseekmodel.com/api/download.php?id=yeachan-heo-oh-my-claudecode-skills-autoresearch-skill-md&format=skill
下载 .skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
name autoresearch description Stateful single-mission improvement loop with strict evaluator contract, markdown decision logs, and max-runtime stop behavior argument-hint [--mission-dir <path>] [--max-runtime <duration>] [--cron <spec>] [--resume <run-id>] 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. <Use_When> 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 </Use_When> <Do_Not_Use_When> 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 </Do_Not_Use_When> - 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 <Required_Artifacts> Canonical persistent storage lives under .omc/autoresearch/<mission-slug>/ and/or .omc/logs/autoresearch/<run-id>/ . Minimum required artifacts: mission spec evaluator script or command reference per-iteration evaluation JSON markdown decision logs 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> 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 <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: keep one mission per scheduled job preserve the same mission/evaluator contract append new run artifacts rather than overwriting prior experiments </Cron_Integration> <Execution_Policy> 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 </Execution_Policy>
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下载的 .skill 包内含以下字段。
字段 说明
format格式标识(skill/v1)
skill_id技能唯一 ID
name技能名称
version版本号
description技能描述
category所属分类(数组)
trigger_words触发词列表
tags标签列表
source来源标识
source_url来源链接(本页地址)
exported_at导出时间(每次下载生成)
system_prompt系统提示词正文
model_config模型参数:provider / model / temperature / max_tokens / top_p
examples示例
install_guide各平台导入说明(Coze / Dify / Claude / 自定义框架)
同一份技能可按不同平台格式导出。
.skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用 下载
.skillpro 增强格式,额外含脚本 / 工具 / 依赖 / 钩子占位 下载
.json 纯 JSON 导出,只含 system_prompt 与模型参数 下载
Coze 带 frontmatter 的 Markdown,Coze 平台导入用 下载
Dify Dify DSL,创建应用后直接导入 下载

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