deep-research

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. 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"