deep-research
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.
About this skill
The Deep Research skill empowers AI agents to perform comprehensive, multi-source investigations across the web. Utilizing powerful tools like Firecrawl and Exa MCPs (Maximum Credibility Protocols), it scours various online resources, synthesizes complex information, and generates well-structured, cited reports. This skill is specifically designed for scenarios requiring evidentiary support and source attribution, transforming raw web data into actionable intelligence. It's part of the 'everything-claude-code' repository, known for its high-quality, production-ready skills tailored for the Claude AI agent.
Best use case
Ideal for any scenario requiring a detailed, evidence-based understanding of a topic. This includes competitive analysis, technology evaluation, market sizing, due diligence on companies or investors, and general knowledge acquisition that demands thoroughness and verified sources.
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.
A well-structured, comprehensive research report that synthesizes information from various web sources. The report will include clear findings, relevant details, and full source attribution with citations for all claims and data points.
Practical example
Example input
Conduct a deep dive into the pros and cons of serverless architecture for large-scale enterprise applications, focusing on cost implications, scalability, and security challenges. Please provide citations for all claims.
Example output
### Report: Serverless Architecture for Large-Scale Enterprise Applications #### Executive Summary Serverless architecture offers significant advantages in scalability and reduced operational overhead for enterprises, but it introduces complexities regarding cost predictability, vendor lock-in, and security management. This report synthesizes findings on these aspects, providing a balanced view for adoption decisions. #### Pros of Serverless Architecture 1. **Scalability:** Serverless functions automatically scale with demand, handling varying workloads without manual intervention. (Source: AWS Lambda Documentation, 2023) This elasticity ensures applications remain responsive during peak usage. 2. **Cost Efficiency:** Organizations pay only for the compute resources consumed during execution, leading to potential cost savings compared to always-on traditional servers. (Source: Azure Functions Pricing Guide, 2023) However, cost models can become complex at scale. 3. **Reduced Operational Overhead:** Developers can focus on writing code rather than managing servers, patching, or scaling infrastructure. (Source: Google Cloud Functions Overview, 2022) #### Cons of Serverless Architecture 1. **Cost Predictability Challenges:** While potentially cheaper, predicting exact costs for complex, high-traffic applications can be difficult due to granular billing models. 'Zombie functions' or unexpected usage spikes can lead to unforeseen expenses. (Source: IBM Cloud Blog on Serverless Costs, 2021) 2. **Vendor Lock-in:** Migrating serverless applications between different cloud providers can be challenging due to proprietary function runtime environments and API differences. (Source: TechCrunch Article on Cloud Lock-in, 2020) 3. **Security Challenges:** While cloud providers manage underlying infrastructure security, developers are responsible for code-level vulnerabilities, proper IAM roles, and data security within functions. Debugging security issues across distributed functions can be complex. (Source: OWASP Top 10 Serverless Security Risks, 2019) #### Conclusion Serverless architecture presents a compelling option for enterprises seeking agility and cost optimization, particularly for event-driven microservices. However, careful planning for cost management, vendor strategy, and robust security practices is crucial for successful large-scale adoption. --- **Sources:** * AWS Lambda Documentation. (2023). *Understanding AWS Lambda Pricing*. [URL] * Azure Functions Pricing Guide. (2023). *Azure Functions Pricing*. [URL] * Google Cloud Functions Overview. (2022). *What are Cloud Functions?*. [URL] * IBM Cloud Blog. (2021). *Hidden Costs of Serverless Computing*. [URL] * TechCrunch. (2020). *The Reality of Cloud Vendor Lock-in*. [URL] * OWASP Foundation. (2019). *OWASP Serverless Top 10*. [URL]
When to use this skill
- When the user explicitly asks to research a topic in depth.
- For competitive analysis, technology evaluation, or market sizing tasks.
- When performing due diligence on companies, investors, or technologies.
- For any question requiring synthesis and attribution from several online sources.
When not to use this skill
- For simple factual lookups, quick definitions, or questions where a single, undisputed answer is sufficient.
- When immediate, un-synthesized raw search results are preferred over a curated report.
- For highly time-sensitive information that changes minute-by-minute, as web scraping has inherent processing delays.
- If the user specifically requests a creative output or brainstorming session rather than an informational report.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/deep-research/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How deep-research Compares
| Feature / Agent | deep-research | Standard Approach |
|---|---|---|
| Platform Support | Claude | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | easy | N/A |
Frequently Asked Questions
What does this skill do?
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.
Which AI agents support this skill?
This skill is designed for Claude.
How difficult is it to install?
The installation complexity is rated as easy. You can find the installation instructions above.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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 `~/.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: ```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 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" ```
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