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Agent Skill · 研究检索

autoresearch(自动研究)

autoresearch 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

autoresearch 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 37,977。

总安装 1,639周安装 69GitHub Stars 3,820下载 574核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:autoresearch(自动研究)
来源仓库:https://github.com/agricidaniel/claude-obsidian
仓库路径:skills/autoresearch
安装命令:
npx skills add https://github.com/agricidaniel/claude-obsidian --skill autoresearch
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/agricidaniel/claude-obsidian --skill autoresearch

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

autoresearch 实现自主循环式网络研究,自动归档结果至知识库。适合深度主题调研,支持自定义源偏好与置信度评分机制。运行直至达到预设深度,输出为 Obsidian 维基页面而非聊天回复。需先读取 program.md 配置目标,避免重复处理已收录内容。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/autoresearch

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/agricidaniel/claude-obsidian --skill autoresea…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

autoresearch 怎么安装?

在终端执行 npx skills add https://github.com/agricidaniel/claude-obsidian --skill autoresearch。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

autoresearch 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

autoresearch 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/agricidaniel/claude-obsidian --skill autoresearch 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

autoresearch 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

autoresearch 用的人多吗?

公开数据是累计安装 1,639 次,近一周安装 69 次,下载 574 次,GitHub 3,820 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

autoresearch 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 来源仓库有 3,820 星,维护活跃度相对可查。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
autoresearch

自动研究 · 本页

1,6393,820执行命令未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

autoresearch: Autonomous Research Loop

You are a research agent. You take a topic, run iterative web searches, synthesize findings, and file everything into the wiki. The user gets wiki pages, not a chat response.

This is based on Karpathy's autoresearch pattern: a configurable program defines your objectives. You run the loop until depth is reached. Output goes into the knowledge base.


Before Starting

Read references/program.md to load the research objectives and constraints. This file is user-configurable. It defines what sources to prefer, how to score confidence, and any domain-specific constraints.


Topic Selection

Three paths to a topic:

A. Explicit topic (always respected)

When the user says /autoresearch [topic] or "research X", use the given topic verbatim and skip the sections below.

B. Boundary-first selection (agenda control, opt-in)

This is agenda control, not pure memory. DragonScale Memory.md Mechanism 4 labels this mechanism as such because it shapes which direction the research agent moves next. Users who want a strict memory-layer subset should omit this path entirely.

When /autoresearch is invoked WITHOUT a topic AND the vault has adopted DragonScale, default to surfacing the frontier of the vault as a set of candidate topics the user can accept, override, or decline.

Feature detection (shell):

if [ -x ./scripts/boundary-score.py ] && [ -d ./.vault-meta ] && command -v python3 >/dev/null 2>&1; then
  BOUNDARY_MODE=1
else
  BOUNDARY_MODE=0
fi

When BOUNDARY_MODE=1:

  1. Run ./scripts/boundary-score.py --json --top 5. Returns the top 5 frontier pages by boundary_score = (out_degree - in_degree) * recency_weight.
  2. Helper failure handling: if the helper exits non-zero, emits invalid JSON, or returns an empty results array, set BOUNDARY_MODE=0 and fall through to section C below. Do NOT prompt the user with an empty candidate list, and do NOT improvise a topic.
  3. Present the candidate list to the user: "Your top frontier pages are: [list]. Research which one? (1-5, or type a topic to override, or say 'cancel' to be asked normally.)"
  4. If the user picks 1-5, use the selected page's title as the topic.
  5. If the user types free text, use that.
  6. If the user cancels or does not choose, fall through to C.

The boundary score is a heuristic, not an objective measure of what SHOULD be researched. The user always has the option to type a free-text topic to override the surfaced candidates.

Link-resolution semantics: the boundary helper uses filename-stem wikilink resolution only. [[Foo]] is counted as an edge to Foo.md anywhere in the vault. Aliases declared via frontmatter aliases: are not parsed. Folder-qualified links (e.g. [[notes/Foo]]) are resolved by stem only. This matches default Obsidian behavior for unique filenames but does not implement full Obsidian alias resolution.

C. User-chosen (default when B is unavailable)

When BOUNDARY_MODE=0 or the user declined every frontier pick, ask: "What topic should I research?"


Research Loop

Input: topic (from Topic Selection, above)

Round 1. Broad search
1. Decompose topic into 3-5 distinct search angles
2. For each angle: run 2-3 WebSearch queries
3. For top 2-3 results per angle: WebFetch the page
4. Extract from each: key claims, entities, concepts, open questions

Round 2. Gap fill
5. Identify what's missing or contradicted from Round 1
6. Run targeted searches for each gap (max 5 queries)
7. Fetch top results for each gap

Round 3. Synthesis check (optional, if gaps remain)
8. If major contradictions or missing pieces still exist: one more targeted pass
9. Otherwise: proceed to filing

Max rounds: 3 (as set in program.md). Stop when depth is reached or max rounds hit.

Filing Results

After research is complete, create these pages:

wiki/sources/. One page per major reference found

  • Use source frontmatter (type, source_type, author, date_published, url, confidence, key_claims)
  • Body: summary of the source, what it contributes to the topic

wiki/concepts/. One page per significant concept extracted

  • Only create a page if the concept is substantive enough to stand alone
  • Check the index first: update existing concept pages rather than creating duplicates

wiki/entities/. One page per significant person, org, or product identified

  • Check the index first: update existing entity pages

wiki/questions/. One synthesis page titled "Research: [Topic]"

  • This is the master synthesis. Everything comes together here.
  • Sections: Overview, Key Findings, Entities, Concepts, Contradictions, Open Questions, Sources
  • Full frontmatter with related links to all pages created in this session

Synthesis Page Structure

---
type: synthesis
title: "Research: [Topic]"
created: YYYY-MM-DD
updated: YYYY-MM-DD
tags:
  - research
  - [topic-tag]
status: developing
related:
  - "[[Every page created in this session]]"
sources:
  - "[[wiki/sources/Source 1]]"
  - "[[wiki/sources/Source 2]]"
---

# Research: [Topic]

## Overview
[2-3 sentence summary of what was found]

## Key Findings
- Finding 1 (Source: [[Source Page]])
- Finding 2 (Source: [[Source Page]])
- ...

## Key Entities
- [[Entity Name]]: role/significance

## Key Concepts
- [[Concept Name]]: one-line definition

## Contradictions
- [[Source A]] says X. [[Source B]] says Y. [Brief note on which is more credible and why]

## Open Questions
- [Question that research didn't fully answer]
- [Gap that needs more sources]

## Sources
- [[Source 1]]: author, date
- [[Source 2]]: author, date

After Filing

  1. Update wiki/index.md. Add all new pages to the right sections
  2. Append to wiki/log.md (at the TOP): ## [YYYY-MM-DD] autoresearch | [Topic] - Rounds: N - Sources found: N - Pages created: [[Page 1]], [[Page 2]],... - Synthesis: [[Research: Topic]] - Key finding: [one sentence]
  3. Update wiki/hot.md with the research summary

Report to User

After filing everything:

Research complete: [Topic]

Rounds: N | Searches: N | Pages created: N

Created:
  wiki/questions/Research: [Topic].md (synthesis)
  wiki/sources/[Source 1].md
  wiki/concepts/[Concept 1].md
  wiki/entities/[Entity 1].md

Key findings:
- [Finding 1]
- [Finding 2]
- [Finding 3]

Open questions filed: N

Constraints

Follow the limits in references/program.md:

  • Max rounds (default: 3)
  • Max pages per session (default: 15)
  • Confidence scoring rules
  • Source preference rules

If a constraint conflicts with completeness, respect the constraint and note what was left out in the Open Questions section.

平台分布

Codex

32.89%
按下载量换算189

Claude

32.27%
按下载量换算185

Cursor

17.14%
按下载量换算98

Gemini CLI

8.71%
按下载量换算50

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/agricidaniel/claude-obsidian --skill autoresearch 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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