where-your-customer-lives
Given a product utility and ICP, researches the internet to find the specific channels. Where your customer actually lives, ranked by reachability with a full per-channel playbook. Returns evidence that your ICP is there, one entry tactic, one content angle, and specific anti-patterns per channel. Use when asked where my customer hangs out, what communities should I post in, where is my ICP, find channels for outreach, what forums does my ICP use, where should I spend time for distribution, or which communities are right for my product.
npx skills add Varnan-Tech/opendirectory --skill where-your-customer-lives --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.
# Where Your Customer Lives Given a product utility and ICP, trace real ICP pain posts back to their source communities. Layer in competitor discussion signals. Discover Slack/Discord/newsletter/podcast/conference channels via DuckDuckGo. Score every channel by ICP signal count, size, activity, and competitor presence. Output a ranked playbook: evidence, entry tactic, content angle, anti-patterns -- one per channel. No guessing. Signal-traced channels only. --- **Critical rule:** Every channel name in the output must exist in either the Reddit API response or DuckDuckGo search results from this run. Every member count must come from the `about.json` API or a search snippet -- never estimated. Every ICP signal count must match the raw data. If a channel type returns 0 results, report 0 -- do not fabricate channels. --- ## Common Mistakes | The agent will want to... | Why that's wrong | |---|---| | Recommend generic channels ("LinkedIn", "Twitter") | Every channel must be specific with a name, member count, and URL. "LinkedIn Group: DevOps for Enterprise Teams (45K members)" -- not just "LinkedIn". | | Use the same channels for every ICP | Signal-trace is ICP-specific. A DevOps ICP a
- Common Mistakes
- Step 1: Setup Check
- Step 2: Parse ICP
- Step 3: Run the Standalone Data Collection Script
- Step 4: Print Channel Summary
- Step 5: AI Channel Enrichment
- Step 6: Generate Full Ranked Output
- Step 7: Self-QA
- Step 8: Save Output and Clean Up
echo "GITHUB_TOKEN: ${GITHUB_TOKEN:-not set -- competitor layer runs at 60 req/hr unauthenticated}"
echo ""
echo "Data sources this run will use:"
echo " Reddit public JSON (no auth, signal-trace)"
echo " Reddit about.json (no auth, subreddit metadata)"
echo " HN Algolia API (no auth, signal-trace)"
echo " DuckDuckGo HTML (no auth, channel discovery)"
echo " GitHub API (${GITHUB_TOKEN:+authenticated, }optional for competitor enrichment)"
python3 << 'PYEOF'
ls scripts/fetch.py 2>/dev/null && echo "script available" || echo "not found"What does the where-your-customer-lives skill do?
Given a product utility and ICP, researches the internet to find the specific channels. Where your customer actually lives, ranked by reachability with a full per-channel playbook. Returns evidence that your ICP is there, one entry tactic, one content angle, and specific anti-patterns per channel. Use when asked where my customer hangs out, what communities should I post in, where is my ICP, find channels for outreach, what forums does my ICP use, where should I spend time for distribution, or which communities are right for my product.
How do I install it?
Run `npx skills add Varnan-Tech/opendirectory --skill where-your-customer-lives --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.