deep-research

claude-office-skills/skills · updated Apr 8, 2026

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$npx skills add https://github.com/claude-office-skills/skills --skill deep-research
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summary

I help you conduct comprehensive, multi-faceted research on any topic. I analyze information from multiple perspectives, synthesize findings, and deliver structured research reports suitable for decision-making.

skill.md

Deep Research Skill

Overview

I help you conduct comprehensive, multi-faceted research on any topic. I analyze information from multiple perspectives, synthesize findings, and deliver structured research reports suitable for decision-making.

What I can do:

  • Systematic topic exploration
  • Multi-angle analysis (pros/cons, different viewpoints)
  • Information synthesis and structuring
  • Key insight extraction
  • Comprehensive report generation
  • Source organization and citation

What I cannot do:

  • Access real-time internet (unless tools provided)
  • Conduct primary research (surveys, interviews)
  • Access paywalled or proprietary databases
  • Guarantee 100% accuracy of synthesized information

How to Use Me

Step 1: Define Research Question

Provide:

  • Main research question or topic
  • Specific sub-questions (if any)
  • Context and purpose of research
  • Any constraints or focus areas

Step 2: Set Research Parameters

  • Scope: Broad overview vs. narrow deep-dive
  • Perspectives: Technical, business, social, legal, etc.
  • Time frame: Historical, current, future-focused
  • Depth: Surface-level vs. comprehensive

Step 3: Specify Output Format

  • Executive Brief: 1-2 page summary
  • Standard Report: 5-10 page comprehensive report
  • Deep Dive: 15+ page detailed analysis
  • Presentation: Slide-ready bullet points

Research Methodology

Phase 1: Scoping

1. Understand the research question
2. Identify key concepts and terms
3. Define boundaries and constraints
4. Establish success criteria

Phase 2: Information Gathering

1. Explore primary aspects of the topic
2. Identify relevant subtopics
3. Collect diverse perspectives
4. Note key facts, data, and quotes

Phase 3: Analysis

1. Organize information by theme
2. Identify patterns and trends
3. Compare different viewpoints
4. Assess reliability and bias

Phase 4: Synthesis

1. Develop key insights
2. Draw conclusions
3. Identify gaps and limitations
4. Formulate recommendations

Phase 5: Reporting

1. Structure findings logically
2. Present balanced perspectives
3. Cite sources appropriately
4. Provide actionable takeaways

Research Frameworks

PESTEL Analysis (for market/industry topics)

Factor Questions to Explore
Political Regulations, government policy, political stability
Economic Growth, inflation, employment, currency
Social Demographics, culture, consumer behavior
Technological Innovation, automation, R&D
Environmental Sustainability, climate, resources
Legal Laws, compliance, litigation

5W1H Framework (for any topic)

  • What: Definition, scope, components
  • Why: Causes, motivations, rationale
  • Who: Stakeholders, actors, affected parties
  • When: Timeline, milestones, history
  • Where: Geography, context, application
  • How: Mechanisms, processes, methods

Argument Mapping

Claim: [Main thesis or finding]
├── Supporting Evidence 1
│   └── Source & strength
├── Supporting Evidence 2
│   └── Source & strength
├── Counter-argument 1
│   └── Rebuttal
└── Counter-argument 2
    └── Rebuttal

Output Format

# Research Report: [Topic]

**Research Question**: [Main question being addressed]
**Date**: [Date]
**Prepared by**: AI Research Assistant

---

## Executive Summary

[3-5 sentence overview of key findings and conclusions]

**Key Findings**:
1. [Finding 1]
2. [Finding 2]
3. [Finding 3]

**Recommendation**: [Brief actionable recommendation]

---

## Table of Contents

1. Introduction
2. Background
3. [Main Section 1]
4. [Main Section 2]
5. [Main Section 3]
6. Analysis & Discussion
7. Conclusions
8. Recommendations
9. Sources & Further Reading

---

## 1. Introduction

### Research Objective
[What this research aims to accomplish]

### Scope & Boundaries
[What is and isn't covered]

### Methodology
[How the research was conducted]

---

## 2. Background

### Context
[Relevant background information]

### Key Terms & Definitions
| Term | Definition |
|------|------------|
| [Term 1] | |
| [Term 2] | |

### Historical Overview
[Brief history if relevant]

---

## 3. [Main Section 1]

### Overview
[Introduction to this aspect]

### Key Points

#### [Subsection 1.1]
[Detailed analysis]

#### [Subsection 1.2]
[Detailed analysis]

### Summary
[Key takeaways from this section]

---

## 4. [Main Section 2]

[Similar structure...]

---

## 5. [Main Section 3]

[Similar structure...]

---

## 6. Analysis & Discussion

### Key Patterns & Trends
[Patterns identified across the research]

### Multiple Perspectives

#### Perspective A: [Viewpoint]
[Analysis of this viewpoint]

#### Perspective B: [Viewpoint]
[Analysis of this viewpoint]

### Strengths & Weaknesses
| Strengths | Weaknesses |
|-----------|------------|
| | |

### Gaps & Limitations
[What the research couldn't fully address]

---

## 7. Conclusions

### Main Findings
1. [Conclusion 1]
2. [Conclusion 2]
3. [Conclusion 3]

### Implications
[What these findings mean for the reader]

---

## 8. Recommendations

### Immediate Actions
1. [Action 1]
2. [Action 2]

### Further Research Needed
1. [Area 1]
2. [Area 2]

---

## 9. Sources & Further Reading

### Primary Sources
1. [Source 1]
2. [Source 2]

### Additional Resources
1. [Resource 1]
2. [Resource 2]

---

## Appendices

### Appendix A: [Supporting Data]
### Appendix B: [Detailed Analysis]

---

*Research conducted using AI-assisted analysis. Findings should be verified with primary sources for critical decisions.*

Example Research Topics

  1. Technology: "What are the implications of quantum computing for cybersecurity?"
  2. Business: "How are companies successfully implementing AI in customer service?"
  3. Policy: "What are the pros and cons of universal basic income?"
  4. Science: "What is the current state of CRISPR gene editing technology?"
  5. Market: "What factors are driving the growth of electric vehicles?"

Tips for Better Results

  1. Be specific about your research question
  2. Provide context about why you need this research
  3. Specify perspectives you want explored
  4. Indicate depth required (overview vs deep dive)
  5. Ask for sources if you need to verify information
  6. Request multiple viewpoints for balanced analysis

Limitations

  • Cannot access real-time web data
  • Cannot access paywalled content
  • Knowledge has a training cutoff date
  • Cannot conduct primary research
  • May have biases in synthesized information
  • Cannot guarantee accuracy of all facts

Built by the Claude Office Skills community. Contributions welcome!

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/claude-office-skills/skills --skill deep-research

The skills CLI fetches deep-research from GitHub repository claude-office-skills/skills 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

4.839 reviews
  • Aanya Gill· Dec 24, 2024

    Registry listing for deep-research matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Pratham Ware· Dec 4, 2024

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

  • Naina Kapoor· Dec 4, 2024

    I recommend deep-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Yash Thakker· Nov 23, 2024

    I recommend deep-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Meera Choi· Nov 23, 2024

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

  • Aarav Lopez· Nov 15, 2024

    Useful defaults in deep-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Dhruvi Jain· Oct 14, 2024

    Useful defaults in deep-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Evelyn Taylor· Oct 14, 2024

    Registry listing for deep-research matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Hassan Iyer· Oct 6, 2024

    I recommend deep-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Ava Desai· Sep 25, 2024

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

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