
Deep Research
FreeConduct comprehensive, cited research across multiple sources.
Free · Opens the source repo
What Deep Research does
Deep Research is a powerful tool designed for users who require thorough and well-cited research reports on a variety of topics. Leveraging advanced web searching capabilities through firecrawl and exa MCP tools, this skill allows users to synthesize information from multiple sources, ensuring that findings are not only accurate but also well-supported with citations. Whether you are conducting competitive analysis, evaluating new technologies, or performing due diligence on companies, Deep Research streamlines the process of gathering and organizing information.
The workflow begins by clarifying the user's goals, which helps in structuring the research effectively. Users can specify the depth and angle of the research, or simply request a general inquiry. The skill breaks down the main topic into several sub-questions, ensuring a comprehensive exploration of the subject. This methodical approach allows for a detailed investigation into key themes, leading to a well-rounded understanding of the topic at hand.
Once the sub-questions are defined, Deep Research executes multi-source searches using both firecrawl and exa tools. This dual approach maximizes coverage and ensures a diverse range of sources, from academic papers to reputable news articles. The skill emphasizes the importance of reading full content from key sources rather than relying solely on search snippets, which enhances the depth of the final report.
After gathering and analyzing the information, Deep Research synthesizes the findings into a structured report, complete with an executive summary, key takeaways, and a comprehensive list of sources. This final product is designed to be easily digestible, whether for quick reference or in-depth review. Overall, Deep Research is ideal for professionals and researchers seeking reliable, evidence-based insights across various domains.
When to use it
Use this skill when you need in-depth research on a topic, such as competitive analysis, technology evaluation, or market sizing.
When not to use it
This skill may not be suitable for quick queries or when only surface-level information is needed, as it focuses on comprehensive research.
What you can build with it
Market Analysis for New Product
A product manager uses Deep Research to gather insights on market trends, competitors, and consumer behavior before launching a new product.
Technology Evaluation
An investor conducts a deep dive into emerging technologies, using the skill to assess potential investment opportunities and risks.
Due Diligence on a Startup
A venture capitalist employs Deep Research to compile a comprehensive report on a startup's market position, technology, and financial health.
How to install Deep Research
View source1. Install with the skills CLI
npx skills add affaan-m/ecc/deep-research --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by affaan-mDeep 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:
- firecrawl —
firecrawl_search,firecrawl_scrape,firecrawl_crawl - exa —
web_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
- Every claim needs a source. No unsourced assertions.
- Cross-reference. If only one source says it, flag it as unverified.
- Recency matters. Prefer sources from the last 12 months.
- Acknowledge gaps. If you couldn't find good info on a sub-question, say so.
- No hallucination. If you don't know, say "insufficient data found."
- 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"
Frequently asked questions about Deep Research
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