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About
Use cases: research
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What's inside
- Skill 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.
- License
- MIT
- Instructions
- 151 lines in SKILL.md · view on GitHub
Files (2)
- SKILL.md
- openai.yaml · agents/
SKILL.md (first lines)
# 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: - **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 `~/.codex.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: ```markdown # [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
From mturac/everything-openai-codex, MIT.
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Signals
- Stars
- 83
- Forks
- 2
- Open issues
- 1
- Last commit (this folder)
- May 18, 2026 (5 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
deep-research skill. Agent Mart. https://agentmart.dev/skills/mturac-deep-research (last updated 2026-10-24)