SkillCheck validator, Cursor exports, and per-agent installers (#27)
Three more learnings from alirezarezvani/claude-skills, applied: 1. SkillCheck validator (scripts/skillcheck.mjs) — validates every SKILL.md against the authoring standard (frontmatter, name/folder match, trigger + produces clauses, required headings) plus tier referential integrity. Errors fail CI; --strict fails on warnings too. New skillcheck.yml workflow and a SkillCheck status badge in the README. Current: 0 errors / 14 advisory warnings across 172 skills. 2. Cursor export platform — build-exports.mjs now generates exports/cursor/<bundle>/<skill>/<skill>.mdc rule files. The PLATFORMS registry now supports per-skill filenames (file as a function). 3. Per-agent installers — scripts/install.sh unifies install for claude/hermes/codex/openclaw/cursor (--link, --target, --dry-run, --list). Curl-able one-liners codex-install.sh, openclaw-install.sh, and cursor-install.sh clone the library and install in a single command. README documents the one-line installs and Cursor exports; CHANGELOG and the authoring standard updated. Claude-Session: https://claude.ai/code/session_016JWn5jRD5tcEFKrubjQ6Px Co-authored-by: Claude <noreply@anthropic.com>
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description: "Structure a retention analysis, churn investigation, or engagement deep-dive for any product team. Use when asked to analyse user retention, investigate churn, measure DAU/MAU, or build a retention improvement plan. Produces a retention snapshot with root cause hypotheses, aha-moment correlation, and prioritised interventions."
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---
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# Retention Analysis Skill
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Diagnose why users leave, identify what keeps them, and recommend specific, testable interventions — not vague "improve onboarding" suggestions.
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## Retention Fundamentals
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**The retention curve has two components:**
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1. **Steepness of initial drop** (D1–D7) — onboarding problem
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2. **Long-term floor level** — product-market fit indicator
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A product with PMF has a retention curve that flattens. If it trends to zero, you have a PMF problem, not an onboarding problem. Name this distinction explicitly.
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---
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## Retention Metrics Definitions
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| Metric | Formula | What It Tells You |
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| D1 Retention | Users who return on day 2 ÷ new users day 1 | Quality of first experience |
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| D7 Retention | Users active on day 8 ÷ users who joined 7 days ago | Early habit formation |
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| D30 Retention | Users active on day 31 ÷ users who joined 30 days ago | Product-market fit signal |
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| DAU/MAU Ratio | Daily active users ÷ monthly active users | Stickiness (>20% good, >50% excellent) |
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| Churn Rate | Users lost in period ÷ users at start of period | Monthly or annual |
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| Net Revenue Retention | MRR at end of period ÷ MRR at start (same cohort) | Revenue health including expansion |
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---
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## Retention Investigation Framework
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### Step 1: Segment the problem
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Don't analyse "retention" — analyse retention for specific cohorts:
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- New vs returning users
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- Paid vs free
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- Acquisition channel (organic vs paid vs referral)
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- Onboarding path completed vs not
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- Feature usage (power users vs lurkers)
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### Step 2: Find the inflection points
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Where does the drop happen? D1? D7? Month 3?
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- D1 drop → First session experience
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- D7 drop → Habit loop not formed
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- D30 drop → Value not delivered at depth
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- Month 3+ drop → Boredom, competition, or lifecycle event
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### Step 3: Identify the "aha moment" correlation
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Which early behaviour predicts long-term retention?
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- Run correlation: users who did [X] in first 7 days vs 30-day retention
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- Common patterns: connected an integration, invited a teammate, completed a core action N times
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### Step 4: Qualify the churn
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Interview churned users — never skip this. Survey data alone is insufficient.
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- "What was the trigger that led you to cancel/stop?"
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- "What were you trying to accomplish that you couldn't?"
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- "What would need to change for you to come back?"
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---
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## Output Format
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### Retention Analysis — [Product/Segment] — [Date]
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**Question:** [Specific retention question being answered]
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**Period Analysed:** [Date range]
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**Segment:** [Which users]
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---
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**Current Retention Snapshot:**
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| Metric | Current | Industry Benchmark | Status |
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| D1 Retention | [X%] | 25–40% | 🔴/🟡/🟢 |
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| D7 Retention | [X%] | 10–25% | 🔴/🟡/🟢 |
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| D30 Retention | [X%] | 5–15% | 🔴/🟡/🟢 |
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| DAU/MAU | [X%] | 10–20% typical | 🔴/🟡/🟢 |
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**Retention Curve Shape:** [Flattening / Still declining / Trending to zero]
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**PMF Signal:** [Strong / Weak / Absent — based on curve shape]
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---
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**Root Cause Hypotheses:**
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| Hypothesis | Evidence | Confidence | Test |
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| [Cause] | [Data point] | H/M/L | [How to validate] |
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**"Aha Moment" Correlation:**
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Users who [specific action] in first [N] days retain at [X%] vs [Y%] for those who don't.
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---
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**Recommended Interventions:**
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| Intervention | Target Drop | Expected Lift | Effort | Priority |
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| [Specific change] | D1 / D7 / D30 | [X%] | S/M/L | 1/2/3 |
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**Monitoring Plan:**
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- Metric to track: [X]
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- Review cadence: [Weekly / Monthly]
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- Alert threshold: [If X drops below Y, investigate immediately]
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---
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## Required Inputs
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Ask the user for these if not provided:
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- **Product and business model** (SaaS / consumer app / marketplace / other)
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- **Current retention metrics** (D1, D7, D30 if available)
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- **Segment to analyse** (all users / paid / free / a specific cohort)
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- **Key question to answer** (why is retention dropping? what drives retention?)
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- **Available data** (analytics events, churn surveys, interview notes)
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## Quality Checks
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- [ ] Retention curve shape is diagnosed (flattening vs trending to zero = PMF vs onboarding)
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- [ ] Cohorts are segmented before analysis (not all users lumped together)
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- [ ] "Aha moment" correlation is identified or flagged as unknown
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- [ ] Interventions are specific (not "improve onboarding")
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- [ ] Churned user interviews are recommended (not just data analysis)
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- [ ] Monitoring plan includes an alert threshold
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## Anti-Patterns
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- [ ] Do not recommend "improve onboarding" without specifying what specific step to change and why
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- [ ] Do not analyse retention without segmenting by cohort — aggregate retention curves hide cohort-specific patterns
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- [ ] Do not treat DAU/MAU below 5% as a retention problem — at that level, it is a product-market fit problem
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- [ ] Do not skip qualitative research — churned user interviews reveal reasons that quantitative data cannot
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- [ ] Do not set a monitoring alert without specifying the threshold that triggers it
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## Guidelines
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- Never recommend "improve onboarding" without specifying *what* to change and *why*
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- Benchmark against industry — consumer apps, SaaS, and marketplaces have very different retention norms
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- If DAU/MAU is below 5%, that's a PMF conversation, not a retention tactics conversation
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- Always recommend talking to churned users — no amount of data replaces understanding the *reason*
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