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Read your database schema, generate behavioral user segments with exact queries, and recommend targeted actions per segment. Use when the user wants to understand their user base, find power users, identify churn risk, build email cohorts, or understand usage patterns. Triggers on requests like "segment users", "who are my power users", "find churned users", "user cohorts", "churn analysis", "inactive users", "behavioral segmentation", "who's about to leave", or any mention of grouping users by activity, usage, or lifecycle.
npx skill4agent add tushaarmehtaa/tushar-skills segment-userscreated_atlast_active_atlast_login_atcreated_at-- Power Users: top 10% by credits consumed
SELECT *, (initial_credits - credits) AS credits_used
FROM users
ORDER BY credits_used DESC
LIMIT (SELECT COUNT(*) / 10 FROM users);
-- Dormant: was active, silent for 7-30 days
SELECT * FROM users
WHERE last_active_at < NOW() - INTERVAL '7 days'
AND last_active_at > NOW() - INTERVAL '30 days';
-- At Risk: paying user, usage dropped below half their average
SELECT * FROM users
WHERE plan != 'free'
AND (initial_credits - credits) < (
SELECT AVG(initial_credits - credits) * 0.5
FROM users WHERE plan != 'free'
);// Prisma: dormant users
const dormant = await prisma.user.findMany({
where: {
lastActiveAt: {
lt: new Date(Date.now() - 7 * 24 * 60 * 60 * 1000),
gt: new Date(Date.now() - 30 * 24 * 60 * 60 * 1000),
},
},
});SELECT 'power_users' AS segment, COUNT(*) AS count FROM users WHERE ...
UNION ALL
SELECT 'active' AS segment, COUNT(*) AS count FROM users WHERE ...
UNION ALL
SELECT 'dormant' AS segment, COUNT(*) AS count FROM users WHERE ...
UNION ALL
SELECT 'churned' AS segment, COUNT(*) AS count FROM users WHERE ...USER SEGMENTS — [project name]
════════════════════════════════════
CURRENT DISTRIBUTION
────────────────────────────────────
Power Users: [N] users ([X]%)
Active: [N] users ([X]%)
Casual: [N] users ([X]%)
Dormant: [N] users ([X]%)
Churned: [N] users ([X]%)
────────────────────────────────────
Total: [N] users
════════════════════════════════════/ship-email[ ] User model read from codebase — no invented fields
[ ] Usage metric identified and defined
[ ] Only segments with available signals generated
[ ] SQL + ORM queries output for each segment
[ ] Count queries included so distribution is visible
[ ] At Risk segment flagged if any paying users exist
[ ] Action recommendation per segment — specific, not generic
[ ] All column names in queries match the actual schema