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deep-research深入研究

Agent Skill

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。

总安装

870

周安装

37

GitHub Stars

323

下载量

305
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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/pedronauck/skills
仓库路径:skills/deep-research
安装命令:
npx skills add https://github.com/pedronauck/skills --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。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/pedronauck/skills --skill deep-research

简介

deep-research 用于查找、检索和筛选相关信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装并使用。
  • 安装前需确认权限范围、维护状态,以及是否会触发联网或文件读写操作。
  • 建议结合原始 README 核验具体用法和功能边界。

SKILL.md

Deep Research

This skill provides a systematic approach to researching and analyzing the codebase and external dependencies. It is designed to be used before starting complex implementation tasks.

<critical_tools> You MUST strictly follow the rules defined in the project's command guides.

  1. Local Code Discovery

- REQUIRED SUB-SKILL: Use codebase_search for local code discovery. - MANDATORY: Use codebase_search for local code discovery.

  1. Sourcebot (External Search)

- Reference: .claude/commands/sourcebot.md - MANDATORY: Use Sourcebot (5-7 times) for EXTERNAL libraries/frameworks only. - NEVER use it for local code. - Workflow: list_repos -> Identify ID -> search_code with filterByRepoIds.

  1. Pal MCP Toolkit (Architecture Analysis)

- Reference: .claude/commands/pal.md (unified Pal skill) - MANDATORY: Use pal skill and appropriate Pal MCP tools before complex tasks. - Available Tools: mcp__zen__analyze, mcp__zen__debug, mcp__zen__planner, mcp__zen__refactor, mcp__zen__codereview, mcp__zen__tracer - Scope: Architecture, Quality, Performance, Security, Tech Debt. - CRITICAL ENFORCEMENT: - YOU MUST ALWAYS complete ALL THE STEPS when using any Pal MCP tool. - NEVER stop in the middle of the steps. - If you don't complete the steps until next_step_required: false, YOUR TASK WILL BE INVALIDATED. - NO EXCEPTIONS. - MODEL REQUIREMENT: - MANDATORY: Always use model: "anthropic/claude-opus-4.6" for ALL Pal MCP tool calls. - NEVER use any other model when calling Pal MCP tools.

  1. Context7 & Perplexity (External Knowledge)

- Reference: .claude/commands/perplexity.md - Context7: Use for 3rd party libraries NOT in Sourcebot. Resolve ID -> Get Docs. - Perplexity: Use for ANY broad research, latest docs, or debugging. </critical_tools>

<research_strategy> MANDATORY: You MUST use your tools for EVERY research task. NO EXCEPTIONS.

  1. Understand the Request: Identify the specific task, goal, or question.
  2. Internal Discovery:

- REQUIRED SUB-SKILL: Use codebase_search for local code discovery. - Search for relevant local code concepts, patterns, and files using natural language. - Identify existing architectural patterns. - Use codebase_search to search specific areas.

  1. External Research (Sourcebot):

- If the task involves external libraries, search for their usage patterns and docs using Sourcebot.

  1. External Knowledge (Context7 & Perplexity):

- Use Context7 for libraries not in Sourcebot. - Use Perplexity for broad research or debugging.

  1. Deep Analysis (Pal Analyze):

- Run the zen_analyze tool to get a comprehensive view of the architecture, risks, and quality. - Complete the analysis loop.

  1. Synthesize: Combine findings from all sources into the final report. </research_strategy>

<quality_standards>

  • MANDATORY: Use tools extensively before answering.
  • Conciseness: Keep summaries high-signal and low-noise.
  • Authoritative: Prioritize findings from the codebase and official documentation (via Sourcebot/Context7).
  • No Hallucinations: NEVER include information not found via tools.
  • Direct Output: Do NOT create files. Return the report directly in the chat. </quality_standards>

<output_format> Return a SINGLE comprehensive message with the following structure:

Deep Analysis Report: [Topic]

1. Context Summary

[Concise summary of what was found in the local codebase, including key files and architectural patterns]

2. External Insights

[Relevant patterns, best practices, or documentation found for external libraries]

3. Deep Analysis Findings

[Insights on Architecture, Security, Performance, and Tech Debt derived from Pal Analyze]

4. Implementation Plan / Recommendations

[Concrete next steps, solution design, or answer to the user's request]

5. Relevant Files

[List of absolute paths to the most important files discovered during research] </output_format>

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

31.7%
按下载量换算97

Claude

31.39%
按下载量换算96

Cursor

20.64%
按下载量换算63

Gemini CLI

9.28%
按下载量换算28

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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