deep-research
affaan-m/ecc
Multi-source research reports with citations using firecrawl and exa web search MCPs.
What is deep-research?
Produces thorough, cited research reports by searching multiple web sources, synthesizing findings, and delivering structured reports with source attribution. Use when you need evidence-based research on any topic with proper citations and cross-referenced claims.
- Breaks research topics into 3-5 sub-questions for comprehensive coverage
- Searches using firecrawl and/or exa MCP tools with multiple keyword variations
- Deep-reads 3-5 key sources by fetching full content via scraping
- Synthesizes findings into structured markdown reports with inline citations
- Supports parallel research using subagents for broad topics
- Flags unverified claims, gaps, and distinguishes facts from estimates
How to install deep-research
npx skills add null --skill deep-research- At least one MCP configured: firecrawl (with firecrawl_search, firecrawl_scrape, firecrawl_crawl) or exa (with web_search_exa, web_search_advanced_exa, crawling_exa)
- Both MCPs together provide best coverage; configure in ~/.claude.json or ~/.codex/config.toml
How to use deep-research
- 1.Ask 1-2 clarifying questions about the research goal (learning, decision-making, or writing)
- 2.Break the topic into 3-5 research sub-questions covering different angles
- 3.Execute multi-source searches for each sub-question using available MCP tools, aiming for 15-30 unique sources
- 4.Fetch and deep-read 3-5 most promising sources in full using firecrawl_scrape or crawling_exa
- 5.Synthesize findings into structured report with executive summary, themed sections, key takeaways, and full source list
- 6.Deliver full report inline for short topics or save to file with summary in chat for longer reports
Use cases
- Competitive analysis and technology evaluation across multiple vendors
- Due diligence research on companies, investors, or emerging technologies
- Market sizing and trend analysis with current data and sources
- Deep dives into policy, regulatory, or industry landscape questions
- Evaluating technical approaches (e.g., Rust vs Go for specific use cases)
- Researchers and analysts needing evidence-based reports
- Product managers evaluating competitive landscapes
- Decision-makers requiring cited, multi-source validation
- Engineers comparing technology options with current data
- Business strategists assessing market opportunities
deep-research FAQ
The skill works with either firecrawl or exa alone, but both together provide better coverage. Configure at least one in ~/.claude.json or ~/.codex/config.toml.
Target 15-30 unique sources total across all sub-questions. Prioritize academic, official, and reputable news sources over blogs and forums.
Acknowledge the gap explicitly in the report. Say 'insufficient data found' rather than guessing or hallucinating.
Yes, use Claude Code's Task tool to launch multiple research agents in parallel, each investigating different sub-questions, then synthesize results.
Flag them as unverified in the report. Cross-reference findings across sources and separate verified facts from single-source claims.
Full instructions (SKILL.md)
Source of truth, from affaan-m/ecc.
name: deep-research 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. metadata: origin: ECC
Deep Research
Drift-prone skill. Firecrawl/Exa MCP tool names, quotas, and result shapes change. Verify the configured MCP tools and current API docs before promising coverage or quoting live source counts.
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"
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