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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

235

周安装

10

GitHub Stars

6

下载量

82
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/duc01226/easyplatform
仓库路径:skills/deep-research
安装命令:
npx skills add https://github.com/duc01226/easyplatform --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/duc01226/easyplatform --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 中快速定位候选结果。

  • 适用于需要根据关键词或任务场景进行信息检索和来源线索筛选的场景。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装使用。
  • 安装前需确认权限范围、维护状态及是否触发联网或文件操作。
  • 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。

SKILL.md

[BLOCKING] Execute skill steps in declared order. NEVER skip, reorder, or merge steps without explicit user approval. [BLOCKING] Before each step or sub-skill call, update task tracking: set in_progress when step starts, set completed when step ends. [BLOCKING] Every completed/skipped step MUST include brief evidence or explicit skip reason. [BLOCKING] If Task tools are unavailable, create and maintain an equivalent step-by-step plan tracker with the same status transitions.
[IMPORTANT] Use TaskCreate to break ALL work into small tasks BEFORE starting.
External Memory: For complex or lengthy work (research, analysis, scan, review), write intermediate findings and final results to a report file in plans/reports/ — prevents context loss and serves as deliverable.
Evidence Gate: MANDATORY IMPORTANT MUST ATTENTION — every claim, finding, and recommendation requires file:line proof or traced evidence with confidence percentage (>80% to act, <80% must verify first).
Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.
AI Mistake Prevention — Failure modes to avoid on every task: - Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal. - Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing. - Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain. - Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path. - When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site. - Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code. - Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks. - Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis. - Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly. - Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

Quick Summary

Goal: Deep-dive into top sources, extract key findings, cross-validate claims, build structured evidence base.

Workflow:

  1. Read source map — Load output from web-research step
  2. Fetch top sources — WebFetch top 5-8 Tier 1-2 sources
  3. Extract findings — Pull key facts, data points, quotes
  4. Cross-validate — Compare findings across sources
  5. Build evidence base — Structured findings with confidence scores

Key Rules:

  • Maximum 8 WebFetch calls per invocation
  • Every finding must cite specific source
  • Conflicting claims → present both, flag discrepancy

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

Deep Research

Step 1: Load Source Map

Read the source map from .claude/tmp/_sources-{slug}.md (output of web-research step).

Prioritize sources for deep-dive:

  1. Tier 1-2 sources first
  2. High-relevance sources
  3. Sources covering identified gaps

Step 2: Fetch Top Sources

For each priority source (max 8):

  1. Run WebFetch with the URL
  2. Extract: key claims, data points, quotes, methodology
  3. Note: publication date, author credentials, source type

Step 3: Extract Findings

For each source, extract:

  • Key claims — factual statements with specific data
  • Data points — numbers, percentages, dates
  • Quotes — notable expert statements
  • Methodology — how data was gathered (for market reports)

Step 4: Cross-Validate

Compare findings across sources:

  • Agreement — 2+ sources say the same thing → high confidence
  • Discrepancy — sources disagree → note both positions
  • Unique — only 1 source → mark as "single source, unverified"

Step 5: Build Evidence Base

Write to .claude/tmp/_evidence-{slug}.md:

# Evidence Base: {Topic}

**Date:** {date}
**Sources analyzed:** {count}

## Findings

### Finding 1: {Title}

**Confidence:** {95%|80%|60%|<60%}
**Sources:** [1], [3]
**Content:** {finding with inline citations}
**Cross-validation:** {agreement/discrepancy notes}

## Unresolved Discrepancies

- {claim X from source A vs claim Y from source B}

## Gaps Remaining

- {what couldn't be verified}

Workflow Recommendation

MANDATORY IMPORTANT MUST ATTENTION — NO EXCEPTIONS: If you are NOT already in a workflow, you MUST ATTENTION use AskUserQuestion to ask the user. Do NOT judge task complexity or decide this is "simple enough to skip" — the user decides whether to use a workflow, not you: 1. Activate research workflow (Recommended) — web-research → deep-research → synthesis → review 2. Execute /deep-research directly — run this skill standalone

Next Steps

MANDATORY IMPORTANT MUST ATTENTION — NO EXCEPTIONS after completing this skill, you MUST ATTENTION use AskUserQuestion to present these options. Do NOT skip because the task seems "simple" or "obvious" — the user decides:

  • "/business-evaluation (Recommended)" — Evaluate business viability from research
  • "/knowledge-synthesis" — If synthesizing research report
  • "Skip, continue manually" — user decides

Closing Reminders

MANDATORY IMPORTANT MUST ATTENTION break work into small todo tasks using TaskCreate BEFORE starting. MANDATORY IMPORTANT MUST ATTENTION validate decisions with user via AskUserQuestion — never auto-decide. MANDATORY IMPORTANT MUST ATTENTION add a final review todo task to verify work quality.

  • MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.
  • MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using TaskCreate.

[IMPORTANT] Analyze how big the task is and break it into many small todo tasks systematically before starting — this is very important.

Prompt-Enhance Closing Anchors

  • IMPORTANT MUST ATTENTION follow declared step order for this skill; NEVER skip, reorder, or merge steps without explicit user approval
  • IMPORTANT MUST ATTENTION for every step/sub-skill call: set in_progress before execution, set completed after execution
  • IMPORTANT MUST ATTENTION every skipped step MUST include explicit reason; every completed step MUST include concise evidence
  • IMPORTANT MUST ATTENTION if Task tools unavailable, maintain an equivalent step-by-step plan tracker with synchronized statuses

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

35.12%
按下载量换算29

Claude

30.48%
按下载量换算25

Cursor

18.88%
按下载量换算15

Gemini CLI

10.05%
按下载量换算8

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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