deep-research

affaan-m/everything-claude-code · updated May 18, 2026

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$npx skills add https://github.com/affaan-m/everything-claude-code --skill deep-research
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summary

Produce thorough, cited research reports from multiple web sources using firecrawl and exa MCP tools.

skill.md

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:

  • firecrawlfirecrawl_search, firecrawl_scrape, firecrawl_crawl
  • exaweb_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

  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"
how to use deep-research

How to use deep-research on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add deep-research
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/affaan-m/everything-claude-code --skill deep-research

The skills CLI fetches deep-research from GitHub repository affaan-m/everything-claude-code and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/deep-research

Reload or restart Cursor to activate deep-research. Access the skill through slash commands (e.g., /deep-research) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.638 reviews
  • Mia Ndlovu· Dec 16, 2024

    deep-research reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Xiao Kapoor· Nov 7, 2024

    We added deep-research from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • William Reddy· Oct 26, 2024

    Keeps context tight: deep-research is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Xiao Gonzalez· Sep 21, 2024

    We added deep-research from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Oshnikdeep· Sep 9, 2024

    Keeps context tight: deep-research is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Ama Mehta· Sep 9, 2024

    deep-research reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Ira Ramirez· Sep 5, 2024

    deep-research has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Ganesh Mohane· Aug 28, 2024

    We added deep-research from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Meera Li· Aug 28, 2024

    deep-research is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Kabir Garcia· Aug 24, 2024

    deep-research fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

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