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 Ingramradical235/anty-framework --skill retention-cohortsgit clone --depth 1 https://github.com/Ingramradical235/anty-frameworkWrote 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/ingramradical235/anty-framework/retention-cohorts)<a href="https://agentmods.dev/skills/ingramradical235/anty-framework/retention-cohorts"><img src="https://agentmods.dev/badge/skills/ingramradical235/anty-framework/retention-cohorts/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/ingramradical235/anty-framework/retention-cohorts"><img src="https://agentmods.dev/badge/skills/ingramradical235/anty-framework/retention-cohorts.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 | $0.00078 | $0.01325 |
| Opus 5 | $0.00039 | $0.00662 |
| Sonnet 5 | $0.00016 | $0.00265 |
| Haiku 4.5 | $0.00008 | $0.00133 |
Grade A, and why
retention-cohorts 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 12d 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.
This is a copy
100% identical to retention-cohorts — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Retention & Cohort Analysis
When to Apply
- Measuring product-market fit
- Deciding whether to invest in growth vs product improvement
- When the founder asks "do we have PMF?"
- When retention data is available from integrations
- Quarterly PMF reassessment
Core Framework
Three Definitions (Set During Onboarding)
Before any retention measurement, guide the founder to define:
-
Cohort grouping — How new users are grouped
- Weekly: daily-use products
- Monthly: utility products
- Quarterly: infrequent-use products (travel, tax)
-
Active action — What counts as "active." Must reflect genuine value delivery.
- Ask: "Imagine watching a customer use your product. What moment tells you they're genuinely getting value?"
- B2B SaaS: "completed a core workflow"
- Consumer: "engaged with 3+ pieces of content"
- Marketplace: "completed a transaction"
-
Time granularity — How often users should ideally use the product. Cross-check against chosen action for consistency.
Triangle Chart (Cohort Retention Table)
Week 0 Week 1 Week 2 Week 3 Week 4 Week 5
Jan 100% 62% 45% 38% 35% 34% <- flattening
Feb 100% 58% 41% 33% 31% ...
Mar 100% 65% 50% 42% ...
Apr 100% 71% 55% ... <- improving
Curve Shape Analysis (Most Important Insight)
Analyze SHAPE, not absolute numbers:
| Curve Shape | PMF Signal | Agent Action |
|---|---|---|
| Flattening (stabilizes at any level) | PMF detected | Shift to growth Drivers. "Retention flattening at ~34%. Even Google Photos flattened at 20-40%." |
| Declining to zero (no stabilization) | No PMF | Shift to product/activation Drivers. Trigger WHY analysis. "Users not sticking. Understand why before investing in growth." |
| Rising (curves go up over time) | Strong PMF + network effects | Propose aggressive scaling. "Retention increasing — extremely strong signal." |
| Newer cohorts better | Product improving | "Product improvements working — newer cohorts retain better." |
| Newer cohorts worse | Product or acquisition degrading | "Warning: newer cohorts retain worse. Investigate product quality or acquisition quality." |
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.
- 12d ago First seen · 108 lines · 78 tokens per session scan A 7421f54d273a
retention-cohorts is a skill published in the GitHub repository Ingramradical235/anty-framework (1 stars, last pushed yesterday), licensed MIT. It adds 78 tokens to every session and 1,325 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to retention-cohorts, differing in 0 lines, and is treated as a copy.
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