map-your-market
Given a product description, category keywords, or competitor names (any combination), searches Reddit, Hacker News, GitHub Issues, G2, and Google Trends for the real pains your market experiences, then synthesizes everything into a positioning framework showing who your ICP is, what they say out loud, and exactly how to talk to them. Use when asked to understand a market, find ICP pain points, map competitors, build a positioning doc, find messaging angles, or answer who is my customer and what do they actually care about. Trigger when a user says map my market, who is my ICP, what pains does
npx skills add Varnan-Tech/opendirectory --skill map-your-market --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Map Your Market Take a product description, category keywords, or competitor names. Search Reddit, HN, GitHub Issues, G2, and Google Trends for real pain signals. Score and cluster them. Build a complete positioning framework: ICP definition, ranked pain themes with verbatim quotes, market size signals, and messaging angles derived from actual language people use. --- **Critical rule:** Every pain quote in the output must exist verbatim in the raw data collected by the script. Every vendor name in the market map must come from G2 scrape results or GitHub search results. Market size must say "signals suggest" -- never estimate a dollar figure from thin proxies. If a source returns 0 results, report 0 -- do not supplement with invented examples. --- ## Common Mistakes | The agent will want to... | Why that's wrong | |---|---| | Invent pain points or market size numbers | Every pain quote must be verbatim from raw data. Market size must cite signals found. Never estimate "typical" market size. | | Score by post count instead of pain_score | A post with 2,000 upvotes about pricing is stronger than 50 posts with 10 upvotes each. Use the pain_score formula from references/pain-scoring.
- Common Mistakes
- Step 1: Setup Check
- Step 2: Parse Input
- Step 3: Run the Standalone Data Collection Script
- Step 4: AI Pain Clustering
- Step 5: ICP Profiling
- Step 6: Market Size Synthesis
- Step 7: Positioning Framework
- Step 8: Self-QA and Save Output
echo "GITHUB_TOKEN: ${GITHUB_TOKEN:-not set -- GitHub Issues search runs at 60 req/hr unauthenticated}"
echo "No other API keys required."
echo ""
echo "Data sources this run will use:"
echo " Reddit public JSON (no auth, 10 req/min)"
echo " HN Algolia API (no auth, free)"
echo " GitHub Issues API (${GITHUB_TOKEN:+authenticated, }60-5000 req/hr)"
echo " G2 category scrape (no auth, HTML parse)"
echo " Google Trends (no auth, unofficial endpoint)"
python3 << 'PYEOF'What does the map-your-market skill do?
Given a product description, category keywords, or competitor names (any combination), searches Reddit, Hacker News, GitHub Issues, G2, and Google Trends for the real pains your market experiences, then synthesizes everything into a positioning framework showing who your ICP is, what they say out loud, and exactly how to talk to them. Use when asked to understand a market, find ICP pain points, map competitors, build a positioning doc, find messaging angles, or answer who is my customer and what do they actually care about. Trigger when a user says map my market, who is my ICP, what pains does
How do I install it?
Run `npx skills add Varnan-Tech/opendirectory --skill map-your-market --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Varnan-Tech/opendirectory, a repository with 571 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.