A comprehensive skill for conducting deep industry research across multiple sectors including consumer, technology, healthcare, and finance industries.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionindustry-researchExecute the skills CLI command in your project's root directory to begin installation:
Fetches industry-research from rkreddyp/investrecipes and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate industry-research. Access via /industry-research in your agent's command palette.
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.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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A comprehensive skill for conducting deep industry research across multiple sectors including consumer, technology, healthcare, and finance industries.
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.
Industry-Specific News Sources
Market Data Sources
Company Information Sources
Browser Automation Workflow
Screenshot Analysis
Trend Identification
Market Analysis
Insight Generation
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
## [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]
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
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
This skill is designed to be used by specialized industry researcher agents:
consumer_researcher.md - Consumer/retail industrytech_researcher.md - Technology industryhealthcare_researcher.md - Healthcare/biotech industryfinance_researcher.md - Finance/banking industryEach agent applies this skill's methodologies to their specific industry domain.
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Useful defaults in industry-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: industry-research is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added industry-research from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: industry-research is focused, and the summary matches what you get after install.
Solid pick for teams standardizing on skills: industry-research is focused, and the summary matches what you get after install.
industry-research reduced setup friction for our internal harness; good balance of opinion and flexibility.
industry-research is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
industry-research is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for industry-research matched our evaluation — installs cleanly and behaves as described in the markdown.
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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