deep-research

GitHub

利用firecrawl和exa MCP工具进行多源深度研究。通过澄清需求、规划子问题、执行广泛搜索及深度阅读,综合信息并生成带有引用来源的完整研究报告,适用于竞品分析、尽职调查等场景。

data/skills-md/affaan-m/everything-claude-code/deep-research/SKILL.md NeverSight/learn-skills.dev

Trigger Scenarios

用户要求对任何主题进行深入调研 需要进行竞品分析、技术评估或市场规模估算 涉及公司、投资者或技术的尽职调查 需要综合多个来源信息的复杂问题 用户使用“research”、“deep dive”、“investigate”等关键词

Install

npx skills add NeverSight/learn-skills.dev --skill deep-research -g -y
More Options

Non-standard path

npx skills add https://github.com/NeverSight/learn-skills.dev/tree/main/data/skills-md/affaan-m/everything-claude-code/deep-research -g -y

Use without installing

npx skills use NeverSight/learn-skills.dev@deep-research

指定 Agent (Claude Code)

npx skills add NeverSight/learn-skills.dev --skill deep-research -a claude-code -g -y

安装 repo 全部 skill

npx skills add NeverSight/learn-skills.dev --all -g -y

预览 repo 内 skill

npx skills add NeverSight/learn-skills.dev --list

SKILL.md

Frontmatter
{
    "name": "deep-research",
    "origin": "ECC",
    "description": "Multi-source deep research using firecrawl and exa MCPs. Searches the web, synthesizes findings, and delivers cited reports with source attribution. Use when the user wants thorough research on any topic with evidence and citations."
}

Deep Research

Produce thorough, cited research reports from multiple web sources using firecrawl and exa MCP tools.

When to Activate

  • User asks to research any topic in depth
  • Competitive analysis, technology evaluation, or market sizing
  • Due diligence on companies, investors, or technologies
  • Any question requiring synthesis from multiple sources
  • User says "research", "deep dive", "investigate", or "what's the current state of"

MCP Requirements

At least one of:

  • firecrawlfirecrawl_search, firecrawl_scrape, firecrawl_crawl
  • exaweb_search_exa, web_search_advanced_exa, crawling_exa

Both together give the best coverage. Configure in ~/.claude.json or ~/.codex/config.toml.

Workflow

Step 1: Understand the Goal

Ask 1-2 quick clarifying questions:

  • "What's your goal — learning, making a decision, or writing something?"
  • "Any specific angle or depth you want?"

If the user says "just research it" — skip ahead with reasonable defaults.

Step 2: Plan the Research

Break the topic into 3-5 research sub-questions. Example:

  • Topic: "Impact of AI on healthcare"
    • What are the main AI applications in healthcare today?
    • What clinical outcomes have been measured?
    • What are the regulatory challenges?
    • What companies are leading this space?
    • What's the market size and growth trajectory?

Step 3: Execute Multi-Source Search

For EACH sub-question, search using available MCP tools:

With firecrawl:

firecrawl_search(query: "<sub-question keywords>", limit: 8)

With exa:

web_search_exa(query: "<sub-question keywords>", numResults: 8)
web_search_advanced_exa(query: "<keywords>", numResults: 5, startPublishedDate: "2025-01-01")

Search strategy:

  • Use 2-3 different keyword variations per sub-question
  • Mix general and news-focused queries
  • Aim for 15-30 unique sources total
  • Prioritize: academic, official, reputable news > blogs > forums

Step 4: Deep-Read Key Sources

For the most promising URLs, fetch full content:

With firecrawl:

firecrawl_scrape(url: "<url>")

With exa:

crawling_exa(url: "<url>", tokensNum: 5000)

Read 3-5 key sources in full for depth. Do not rely only on search snippets.

Step 5: Synthesize and Write Report

Structure the report:

# [Topic]: Research Report
*Generated: [date] | Sources: [N] | Confidence: [High/Medium/Low]*

## Executive Summary
[3-5 sentence overview of key findings]

## 1. [First Major Theme]
[Findings with inline citations]
- Key point ([Source Name](url))
- Supporting data ([Source Name](url))

## 2. [Second Major Theme]
...

## 3. [Third Major Theme]
...

## Key Takeaways
- [Actionable insight 1]
- [Actionable insight 2]
- [Actionable insight 3]

## Sources
1. [Title](url) — [one-line summary]
2. ...

## Methodology
Searched [N] queries across web and news. Analyzed [M] sources.
Sub-questions investigated: [list]

Step 6: Deliver

  • Short topics: Post the full report in chat
  • Long reports: Post the executive summary + key takeaways, save full report to a file

Parallel Research with Subagents

For broad topics, use Claude Code's Task tool to parallelize:

Launch 3 research agents in parallel:
1. Agent 1: Research sub-questions 1-2
2. Agent 2: Research sub-questions 3-4
3. Agent 3: Research sub-question 5 + cross-cutting themes

Each agent searches, reads sources, and returns findings. The main session synthesizes into the final report.

Quality Rules

  1. Every claim needs a source. No unsourced assertions.
  2. Cross-reference. If only one source says it, flag it as unverified.
  3. Recency matters. Prefer sources from the last 12 months.
  4. Acknowledge gaps. If you couldn't find good info on a sub-question, say so.
  5. No hallucination. If you don't know, say "insufficient data found."
  6. Separate fact from inference. Label estimates, projections, and opinions clearly.

Examples

"Research the current state of nuclear fusion energy"
"Deep dive into Rust vs Go for backend services in 2026"
"Research the best strategies for bootstrapping a SaaS business"
"What's happening with the US housing market right now?"
"Investigate the competitive landscape for AI code editors"

Version History

  • e0220ca Current 2026-07-05 23:53

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Metadata

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