market-research

Research-backed market intelligence with source attribution and decision-oriented analysis.

affaan-m/everything-claude-codeUpdated Jun 18, 2026

Works with

Claude CodeCursorClineWindsurfCodexGooseGitHub CopilotZed

7

total installs

7

this week

142.9K

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Install Skill

Run in your terminal

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

7

installs

7

this week

142.9K

stars

What it does

  • Covers investor diligence, competitive analysis, market sizing, and technology vendor research with structured output including findings, implications, risks, and recommendations

  • Enforces sourcing standards: every claim requires attribution, stale data is flagged, and contrarian evidence is included alongside supporting data

  • Separates fact, inference, and recommendation clearly to support deci

Category

Productivity

Last updated

Jun 18, 2026

Installation Guide

How to use market-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 machine
  • Node.js 16+ with npm — verify with node --version
  • Active project directory where you want to add market-research
2

Run the install 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 market-research

Fetches market-research from affaan-m/everything-claude-code and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/market-research

Restart Cursor to activate market-research. Access via /market-research in your agent's command palette.

Security 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 environment. Always review source, verify the publisher, and test in isolation before production.

Documentation

Market Research

Produce research that supports decisions, not research theater.

When to Activate

  • researching a market, category, company, investor, or technology trend
  • building TAM/SAM/SOM estimates
  • comparing competitors or adjacent products
  • preparing investor dossiers before outreach
  • pressure-testing a thesis before building, funding, or entering a market

Research Standards

  1. Every important claim needs a source.
  2. Prefer recent data and call out stale data.
  3. Include contrarian evidence and downside cases.
  4. Translate findings into a decision, not just a summary.
  5. Separate fact, inference, and recommendation clearly.

Common Research Modes

Investor / Fund Diligence

Collect:

  • fund size, stage, and typical check size
  • relevant portfolio companies
  • public thesis and recent activity
  • reasons the fund is or is not a fit
  • any obvious red flags or mismatches

Competitive Analysis

Collect:

  • product reality, not marketing copy
  • funding and investor history if public
  • traction metrics if public
  • distribution and pricing clues
  • strengths, weaknesses, and positioning gaps

Market Sizing

Use:

  • top-down estimates from reports or public datasets
  • bottom-up sanity checks from realistic customer acquisition assumptions
  • explicit assumptions for every leap in logic

Technology / Vendor Research

Collect:

  • how it works
  • trade-offs and adoption signals
  • integration complexity
  • lock-in, security, compliance, and operational risk

Output Format

Default structure:

  1. executive summary
  2. key findings
  3. implications
  4. risks and caveats
  5. recommendation
  6. sources

Quality Gate

Before delivering:

  • all numbers are sourced or labeled as estimates
  • old data is flagged
  • the recommendation follows from the evidence
  • risks and counterarguments are included
  • the output makes a decision easier

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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

Steps

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

Related Skills

Reviews

4.630 reviews
  • L
    Layla VermaDec 20, 2024

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

  • A
    Aisha AgarwalDec 12, 2024

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

  • S
    Shikha MishraDec 8, 2024

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

  • A
    Amelia AbbasDec 8, 2024

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

  • Y
    Yash ThakkerNov 27, 2024

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

  • D
    Daniel ChawlaNov 27, 2024

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

  • D
    Dhruvi JainOct 18, 2024

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

  • A
    Amelia RamirezOct 18, 2024

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

  • Z
    Zaid GuptaSep 13, 2024

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

  • F
    Fatima SanchezAug 4, 2024

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

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