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Agent Skill · 研究检索

deep-research(深入研究)

deep-research 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。

deep-research 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;许可证为 MIT,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 63,725。

总安装 312周安装 13GitHub Stars 182下载 104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
MIT

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:deep-research(深入研究)
来源仓库:https://github.com/glittercowboy/plugin-freedom-system
仓库路径:skills/deep-research
安装命令:
npx skills add https://github.com/glittercowboy/plugin-freedom-system --skill deep-research
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/glittercowboy/plugin-freedom-system --skill deep-research

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。

deep-research 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词快速定位候选结果时使用。适用于研究检索类任务,可结合来源仓库和原始 README 核验具体用法。

通过 npx skills add 命令从指定 GitHub 仓库安装,需确认权限范围和联网需求。建议在使用前检查维护状态,避免触发不必要的文件读写或命令执行操作。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/deep-research

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/glittercowboy/plugin-freedom-system --skill de…;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

deep-research 怎么安装?

在终端执行 npx skills add https://github.com/glittercowboy/plugin-freedom-system --skill deep-researc…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

deep-research 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。

deep-research 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/glittercowboy/plugin-freedom-system --skill deep-research;npx skills add glittercowboy/plugin-freedom-system --skill "deep-research" 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。

deep-research 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

deep-research 用的人多吗?

公开数据是累计安装 312 次,近一周安装 13 次,下载 104 次,GitHub 182 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。

deep-research 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。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。

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。
  • 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
deep-research

深入研究 · 本页

312182执行命令MIT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

deep-research Skill

Purpose: Multi-level autonomous investigation for complex JUCE plugin development problems using graduated research depth protocol.

Overview

Multi-level autonomous investigation (Level 1: 5 min local → Level 2: 30 min web → Level 3: 60 min parallel). Stops at first confident answer. User controls depth.


NEVER:

  • Edit code files via Edit/Write/NotebookEdit tools
  • Run build commands (npm, CMake, compiler)
  • Modify contracts or configurations
  • Implement any solutions

ONLY:

  • Search for information (Grep, Glob, Read, WebSearch, WebFetch)
  • Analyze existing code and documentation
  • Generate reports and recommendations
  • Present findings with decision menus

ALWAYS delegate implementation to plugin-improve skill via handoff protocol. Violation of this invariant breaks the system architecture.

<handoff_protocol target_skill="plugin-improve"> User selects option 1 ("Apply solution") from decision menu

<deep_research_action> When user selects "Apply solution":

  1. Output confirmation: "User selected: Apply solution. Invoking plugin-improve skill..."
  2. Use Skill tool to invoke plugin-improve directly
  3. STOP execution (no further implementation) </deep_research_action>

<context_passing> plugin-improve reads research findings from conversation history. plugin-improve skips Phase 0.5 investigation (already completed by deep-research). </context_passing>

Note: deep-research invokes plugin-improve directly via the Skill tool when user selects "Apply solution". This ensures the handoff happens automatically without requiring main conversation orchestration.

Why separation matters:

  • Research uses Opus + extended thinking (expensive)
  • Implementation needs codebase context (different focus)
  • Clear decision gate between "here are options" and "making changes"
  • Research can't break anything (safe exploration)

Entry Points

Invoked by: troubleshooter (Level 4), /research [topic], build-automation "Investigate", or natural language ("research [topic]").

Parameters: Topic/question (required), context (optional: plugin name, stage, error), starting level (optional: /research [topic] --level 2 to skip Level 1).


<critical_sequence name="graduated_research_protocol" skip_prevention="strict"> Find quick answer from local knowledge base or JUCE API docs

<required_steps> 1. Search local troubleshooting docs 2. Quick Context7 lookup 3. Assess confidence </required_steps>

<exit_conditions> IF confidence = HIGH: Present decision menu, ALLOW user to proceed ELSE: MUST escalate to Level 2 (no skip option) </exit_conditions>

Deep-dive JUCE docs, forums, GitHub for authoritative answers

<required_steps> 1. Context7 deep-dive (advanced queries, cross-references) 2. JUCE forum search via WebSearch 3. GitHub issue search (juce-framework/JUCE) 4. Synthesize findings from multiple sources </required_steps>

<exit_conditions> IF confidence IN [HIGH, MEDIUM]: Present decision menu ELSE IF confidence = LOW OR novel_problem = true: MUST escalate to Level 3 </exit_conditions>

Parallel subagent investigation for novel/complex problems

<model_requirements> MUST use: claude-opus-4-1-20250805 MUST enable: extended-thinking with 15k budget NEVER use: Sonnet (insufficient synthesis capacity) </model_requirements>

<required_steps> 1. Switch to Opus + extended thinking 2. Identify 2-3 research approaches 3. Spawn parallel subagents via Task tool (NOT serial) 4. Synthesize findings with extended thinking 5. Generate comprehensive report </required_steps>

<exit_conditions> ALWAYS: Present decision menu (no further escalation possible) </exit_conditions>

<enforcement_rules>

  • NEVER skip Level 1 unless user explicitly requests starting at Level 2/3 (via /research [topic] --level 2 or "Start with Level 2 investigation of [topic]")
  • NEVER use serial investigation at Level 3 (must be parallel)
  • NEVER use Sonnet at Level 3 (must be Opus)
  • NEVER forget extended thinking at Level 3
  • NEVER implement solutions (always delegate to plugin-improve) </enforcement_rules> </critical_sequence>

Level 1: Quick Check (5-10 min, Sonnet, no extended thinking)

Goal: Find quick answer from local knowledge base or JUCE API docs

Sources: Local troubleshooting docs, Context7 JUCE documentation

Exit criteria: HIGH confidence solution → present decision menu, otherwise escalate to Level 2

See references/research-protocol.md#level-1-quick-check for detailed process.


Level 2: Moderate Investigation (15-30 min, Sonnet, no extended thinking)

Goal: Deep-dive JUCE docs, forums, GitHub for authoritative answers

Sources: Context7 deep-dive, JUCE forum search, GitHub issue search

Exit criteria: MEDIUM-HIGH confidence solution → present decision menu, otherwise escalate to Level 3

See references/research-protocol.md#level-2-moderate-investigation for detailed process.


Level 3: Deep Research (30-60 min, Opus, extended thinking 15k budget)

Goal: Parallel subagent investigation for novel/complex problems

Model requirements: claude-opus-4-1-20250805 with extended-thinking (15k budget)

Process: Spawn 2-3 parallel research subagents via Task tool, synthesize findings

Exit criteria: ALWAYS present decision menu (no further escalation)

See references/research-protocol.md#level-3-deep-research for detailed process.


Report Generation

Each level generates a structured report using templates in assets/:

  • Level 1: assets/level1-report-template.md
  • Level 2: assets/level2-report-template.md
  • Level 3: assets/level3-report-template.md

Reports include: findings summary, confidence assessment, recommended solution, and source references.

Progress tracking: Use assets/research-progress.md template to track investigation progress across levels.


<state_requirement name="checkpoint_protocol"> At the end of each level (when presenting findings), MUST:

  1. Present decision menu (numbered list format, NOT AskUserQuestion tool)
  2. WAIT for user response (NEVER auto-proceed)
  3. Route based on selection:

<response_handler option="1" action="apply_solution"> User selects option 1 ("Apply solution")

  1. Output confirmation: "User selected: Apply solution. Invoking plugin-improve skill..."
  2. Use Skill tool to invoke plugin-improve
  3. STOP execution (no further implementation) </response_handler>

<response_handler option="2" action="review_findings"> User selects option 2 ("Review full findings")

  1. Display complete research report
  2. Re-present decision menu
  3. WAIT for new selection </response_handler>

<response_handler option="3" action="escalate"> User selects option 3 ("Escalate to next level")

  1. Proceed to next level (Level 1 → Level 2 → Level 3)
  2. If already at Level 3, inform user no further escalation available
  3. Continue with next level's process </response_handler>

<response_handler option="other" action="clarify"> User provides custom response

  1. Ask for clarification
  2. Re-present decision menu with context
  3. WAIT for selection </response_handler>

Decision Menus

After each level, present decision menu using checkpoint protocol format:

Example (Level 1 - HIGH confidence):

✓ Level 1 complete (found solution in local docs)

Solution: [Brief description of solution]
Source: troubleshooting/[category]/[file].md
Confidence: HIGH (exact match)

What's next?

1. Apply solution (recommended)
2. Review full findings
3. Continue deeper - Escalate to Level 2
4. Other

Choose (1-4): _

Example (Level 2 - MEDIUM confidence):

✓ Level 2 complete (found 2 potential solutions)

Recommended: [Solution 1 name]
Alternative: [Solution 2 name]
Confidence: MEDIUM (verified by JUCE forum + docs)

What's next?

1. Apply recommended solution
2. Review all findings
3. Try alternative approach
4. Continue deeper - Escalate to Level 3
5. Other

Choose (1-5): _

Example (Level 3 - comprehensive investigation):

✓ Level 3 complete (parallel investigation)

Investigated 3 approaches:
- Approach A: [Brief description] (recommended)
- Approach B: [Brief description] (viable alternative)
- Approach C: [Brief description] (not recommended)

Confidence: HIGH after synthesis

What's next?

1. Apply recommended solution (recommended)
2. Review detailed comparison
3. Try alternative approach B
4. Document findings
5. Other

Choose (1-5): _

NEVER use AskUserQuestion tool for decision menus. Always use inline numbered lists with "Choose (1-N): _" format matching checkpoint protocol.

See <state_requirement name="checkpoint_protocol"> below for response handling.


Integration Points

See references/integrations.md for troubleshooter and troubleshooting-docs integration details.


Error Handling

See references/error-handling.md for timeout, failure, and fallback patterns.


平台分布

Claude Code

28.47%
按下载量换算30

windsurf

26.1%
按下载量换算27

OpenCode

17.7%
按下载量换算18

Codex

13.06%
按下载量换算14

Antigravity

7.62%
按下载量换算8

Gemini CLI

3.9%
按下载量换算4

安全审计

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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/glittercowboy/plugin-freedom-system --skill deep-research;npx skills add glittercowboy/plugin-freedom-system --skill "deep-research" 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。

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