industry-research

rkreddyp/investrecipes · updated Apr 8, 2026

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$npx skills add https://github.com/rkreddyp/investrecipes --skill industry-research
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summary

A comprehensive skill for conducting deep industry research across multiple sectors including consumer, technology, healthcare, and finance industries.

skill.md

Industry Research Skill

A comprehensive skill for conducting deep industry research across multiple sectors including consumer, technology, healthcare, and finance industries.

Description

This skill provides reusable methodologies, frameworks, and best practices for analyzing industry trends, identifying key market players, understanding market dynamics, tracking industry news, and forecasting future outlooks.

Core Research Areas

1. Industry Trends Analysis

  • Current market trends and growth patterns
  • Emerging technologies and innovations
  • Consumer behavior shifts
  • Regulatory and policy changes
  • Market disruptions and transformations

2. Key Companies & Market Leaders

  • Market leaders and their market share
  • Notable players and emerging competitors
  • Competitive positioning
  • Company performance metrics
  • Strategic initiatives and investments

3. Market Dynamics

  • Market size and growth rates
  • Key performance indicators (KPIs)
  • Market segmentation
  • Pricing trends
  • Supply chain dynamics

4. Industry News & Developments

  • Recent industry news and events
  • Regulatory changes and compliance updates
  • Major mergers and acquisitions
  • Product launches and innovations
  • Industry conferences and announcements

5. Future Outlook

  • Emerging trends and opportunities
  • Market predictions and forecasts
  • Technology disruptions on the horizon
  • Regulatory changes expected
  • Investment and growth opportunities

Research Methodology

Phase 1: Source Identification

  1. Industry-Specific News Sources

    • Identify top industry publications
    • Track industry trade publications
    • Monitor industry association websites
    • Follow industry analysts and thought leaders
  2. Market Data Sources

    • Financial data platforms (Yahoo Finance, Finviz)
    • Market research reports (Gartner, Statista, industry reports)
    • Government data sources
    • Industry databases
  3. Company Information Sources

    • Company websites and investor relations
    • Financial filings and reports
    • Industry databases (Crunchbase, PitchBook)
    • News aggregators

Phase 2: Data Collection

  1. Browser Automation Workflow

    • Navigate to each source using Playwright MCP servers
    • Capture screenshots for visual analysis
    • Extract structured data from pages
    • Verify data accuracy against screenshots
  2. Screenshot Analysis

    • Always capture screenshots before extraction
    • Use Read tool to visually analyze screenshots
    • Extract only what is visible in screenshots
    • Verify extracted data matches screenshot content

Phase 3: Analysis & Synthesis

  1. Trend Identification

    • Group related information by theme
    • Identify patterns across multiple sources
    • Distinguish between trends and isolated events
    • Note confidence levels based on source diversity
  2. Market Analysis

    • Compile market size and growth data
    • Compare metrics across companies
    • Identify market leaders and their positions
    • Analyze competitive dynamics
  3. Insight Generation

    • Synthesize information into actionable insights
    • Connect trends to market implications
    • Identify opportunities and threats
    • Provide forward-looking analysis

Industry-Specific Frameworks

Consumer Industry Framework

  • Focus Areas: Retail performance, e-commerce growth, consumer spending, brand analysis
  • Key Metrics: Same-store sales, online vs. offline growth, consumer sentiment, brand value
  • Sources: Retail Dive, Consumer Reports, NRF, Statista consumer data

Technology Industry Framework

  • Focus Areas: Innovation trends, market leaders, sector performance, emerging technologies
  • Key Metrics: Market share, R&D spending, patent activity, adoption rates
  • Sources: TechCrunch, The Verge, Ars Technica, Gartner reports

Healthcare Industry Framework

  • Focus Areas: Healthcare trends, regulatory changes, biotech developments, market dynamics
  • Key Metrics: FDA approvals, clinical trial results, market size, growth rates
  • Sources: STAT News, Fierce Healthcare, Healthcare Dive, industry reports

Finance Industry Framework

  • Focus Areas: Banking trends, fintech innovation, regulatory changes, market performance
  • Key Metrics: Assets under management, loan growth, fintech adoption, regulatory compliance
  • Sources: American Banker, Financial Times banking, Fintech News, industry reports

Best Practices

Data Quality Standards

  • ✅ All data should be current (latest available)
  • ✅ Verify data against multiple sources when possible
  • ✅ Extract only what is visible in screenshots
  • ✅ Note data recency and source reliability
  • ✅ Distinguish between facts and opinions

Research Workflow

  1. Start Broad: Begin with industry overview and major trends
  2. Narrow Focus: Drill down into specific companies and metrics
  3. Cross-Reference: Verify information across multiple sources
  4. Synthesize: Combine insights into coherent analysis
  5. Document: Save all screenshots and raw data for reference

Source Evaluation

  • Primary Sources: Company websites, financial filings, official reports
  • Secondary Sources: News articles, industry publications, analyst reports
  • Tertiary Sources: Aggregators, summaries, third-party analysis
  • Reliability: Prioritize primary sources, cross-check secondary sources

Output Structure

All research outputs follow this directory structure:

outputs/
└── <agent_name>/
    └── <customer_name>/
        ├── reports/        # Final markdown research reports
        ├── scripts/         # Generated research code
        ├── raw/            # JSON/CSV data files
        └── screenshots/    # PNG screenshots of sources

Report Template

## [Industry] Research Report

**Generated:** [Date/Time]
**Research Period:** [Date range]
**Sources Analyzed:** [List of sources]

---

### Executive Summary
[2-3 paragraph overview of key findings]

---

### Industry Trends
[Current trends with analysis and sources]

---

### Key Companies & Market Leaders
[Top companies with market position and analysis]

---

### Market Dynamics
[Market size, growth rates, key metrics with sources]

---

### Recent Developments
[Industry news and events with dates and sources]

---

### Future Outlook
[Emerging trends, predictions, and opportunities]

---

### Source Attribution
[List of all sources and URLs used]

Code Examples

Basic Industry Research Workflow

import asyncio
from playwright.async_api import async_playwright

async def research_industry_trends(industry_sources):
    """
    Research industry trends from multiple sources.
    
    Args:
        industry_sources (list): List of URLs to research
        
    Returns:
        dict: Research findings organized by source
    """
    findings = {}
    
    async with async_playwright() as p:
        browser = await p.chromium.launch()
        page = await browser.new_page(viewport={"width": 1920, "height": 1080})
        
        for source_url in industry_sources:
            try:
                # Navigate and capture
                await page.goto(source_url, wait_until="domcontentloaded", timeout=120000)
                await page.screenshot(path=f"screenshot_{source_url.replace('/', '_')}.png", full_page=True)
                
                # Extract data (implement based on page structure)
                # ... extraction logic ...
                
                findings[source_url] = extracted_data
            except Exception as e:
                print(f"Error researching {source_url}: {e}")
        
        await browser.close()
    
    return findings

Market Data Extraction

async def extract_market_metrics(page, selector_mapping):
    """
    Extract market metrics from a financial data page.
    
    Args:
        page: Playwright page object
        selector_mapping (dict): Mapping of metric names to CSS selectors
        
    Returns:
        dict: Extracted metrics
    """
    metrics = {}
    
    for metric_name, selector in selector_mapping.items():
        try:
            element = await page.query_selector(selector)
            if element:
                text = await element.inner_text()
                metrics[metric_name] = text.strip()
        except Exception as e:
            print(f"Error extracting {metric_name}: {e}")
    
    return metrics

Common Research Tasks

1. Industry Trend Analysis

  • Identify recurring themes across multiple sources
  • Track trend evolution over time
  • Distinguish between fads and lasting trends
  • Analyze trend drivers and implications

2. Competitive Landscape Mapping

  • Identify market leaders and their positions
  • Map competitive relationships
  • Analyze market share distribution
  • Identify emerging competitors

3. Market Size Estimation

  • Gather market size data from multiple sources
  • Compare estimates across sources
  • Note methodology differences
  • Provide range estimates when sources differ

4. Regulatory Impact Assessment

  • Track regulatory changes and proposals
  • Assess impact on industry players
  • Identify compliance requirements
  • Forecast regulatory trends

Integration with Agents

This skill is designed to be used by specialized industry researcher agents:

  • consumer_researcher.md - Consumer/retail industry
  • tech_researcher.md - Technology industry
  • healthcare_researcher.md - Healthcare/biotech industry
  • finance_researcher.md - Finance/banking industry

Each agent applies this skill's methodologies to their specific industry domain.

Dependencies

  • Playwright for browser automation
  • Screenshot capture capabilities
  • Visual analysis tools (Read tool)
  • Data extraction and processing capabilities

Performance Notes

  • Research multiple sources in parallel when possible
  • Cache screenshots for reference
  • Save raw data for future analysis
  • Organize findings by research area for easy access
how to use industry-research

How to use industry-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 industry-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/rkreddyp/investrecipes --skill industry-research

The skills CLI fetches industry-research from GitHub repository rkreddyp/investrecipes 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/industry-research

Reload or restart Cursor to activate industry-research. Access the skill through slash commands (e.g., /industry-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.

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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

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)
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general reviews

Ratings

4.671 reviews
  • Henry Wang· Dec 28, 2024

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

  • Henry Li· Dec 20, 2024

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

  • Diya Martinez· Dec 16, 2024

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

  • James Anderson· Dec 16, 2024

    Solid pick for teams standardizing on skills: industry-research is focused, and the summary matches what you get after install.

  • Dhruvi Jain· Dec 12, 2024

    Solid pick for teams standardizing on skills: industry-research is focused, and the summary matches what you get after install.

  • James Desai· Dec 12, 2024

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

  • James Jackson· Dec 8, 2024

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

  • Diya Robinson· Dec 4, 2024

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

  • Henry Nasser· Nov 19, 2024

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

  • Soo Martin· Nov 11, 2024

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

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