Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add cnfeat/top-pm-skills --skill retention-analysisgit clone --depth 1 https://github.com/cnfeat/top-pm-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cnfeat/top-pm-skills/retention-analysis)<a href="https://agentmods.dev/skills/cnfeat/top-pm-skills/retention-analysis"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/retention-analysis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cnfeat/top-pm-skills/retention-analysis"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/retention-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00067 | $0.01449 |
| Opus 5 | $0.00034 | $0.00724 |
| Sonnet 5 | $0.00013 | $0.00290 |
| Haiku 4.5 | $0.00007 | $0.00145 |
Grade A, and why
retention-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Retention Analysis Skill
Diagnose why users leave, identify what keeps them, and recommend specific, testable interventions — not vague "improve onboarding" suggestions.
Retention Fundamentals
The retention curve has two components:
- Steepness of initial drop (D1–D7) — onboarding problem
- Long-term floor level — product-market fit indicator
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.
Retention Metrics Definitions
| Metric | Formula | What It Tells You |
|---|---|---|
| D1 Retention | Users who return on day 2 ÷ new users day 1 | Quality of first experience |
| D7 Retention | Users active on day 8 ÷ users who joined 7 days ago | Early habit formation |
| D30 Retention | Users active on day 31 ÷ users who joined 30 days ago | Product-market fit signal |
| DAU/MAU Ratio | Daily active users ÷ monthly active users | Stickiness (>20% good, >50% excellent) |
| Churn Rate | Users lost in period ÷ users at start of period | Monthly or annual |
| Net Revenue Retention | MRR at end of period ÷ MRR at start (same cohort) | Revenue health including expansion |
Retention Investigation Framework
Step 1: Segment the problem
Don't analyse "retention" — analyse retention for specific cohorts:
- New vs returning users
- Paid vs free
- Acquisition channel (organic vs paid vs referral)
- Onboarding path completed vs not
- Feature usage (power users vs lurkers)
Step 2: Find the inflection points
Where does the drop happen? D1? D7? Month 3?
- D1 drop → First session experience
- D7 drop → Habit loop not formed
- D30 drop → Value not delivered at depth
- Month 3+ drop → Boredom, competition, or lifecycle event
Step 3: Identify the "aha moment" correlation
Which early behaviour predicts long-term retention?
- Run correlation: users who did [X] in first 7 days vs 30-day retention
- Common patterns: connected an integration, invited a teammate, completed a core action N times
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 143 lines · 67 tokens per session scan A c7beea9b4744
retention-analysis is a skill published in the GitHub repository cnfeat/top-pm-skills (48 stars, last pushed 2mo ago), licensed MIT. It adds 67 tokens to every session and 1,449 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…