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 thaolst/ai-growth-agents-for-marketers --skill retention-analyzergit clone --depth 1 https://github.com/thaolst/ai-growth-agents-for-marketersWrote 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/thaolst/ai-growth-agents-for-marketers/retention-analyzer)<a href="https://agentmods.dev/skills/thaolst/ai-growth-agents-for-marketers/retention-analyzer"><img src="https://agentmods.dev/badge/skills/thaolst/ai-growth-agents-for-marketers/retention-analyzer/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/thaolst/ai-growth-agents-for-marketers/retention-analyzer"><img src="https://agentmods.dev/badge/skills/thaolst/ai-growth-agents-for-marketers/retention-analyzer.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 | $0.00054 | $0.00580 |
| Opus 5 | $0.00027 | $0.00290 |
| Sonnet 5 | $0.00011 | $0.00116 |
| Haiku 4.5 | $0.00005 | $0.00058 |
Grade A, and why
retention-analyzer 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 11d 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.
What it actually says
Retention Analyzer
Checks
.agents/product-marketing-context.mdfor product context. Checks.agents/growth-metrics-context.mdfor baseline retention. If growth-mcp connected, pulls real cohort data viaanalyze_retentionandpredict_churn_risk. Otherwise, use expert defaults below.
Fintech Retention Benchmarks (SEA)
| Metric | Good | Average | Poor |
|---|---|---|---|
| D1 Retention | > 50% | 30-50% | < 30% |
| D7 Retention | > 30% | 15-30% | < 15% |
| D30 Retention | > 20% | 10-20% | < 10% |
| Monthly Churn | < 15% | 15-30% | > 30% |
Diagnostic Framework
When retention drops, check:
- Seasonal effect — holiday spending spree → natural D1 dip
- Campaign hangover — big promo → users wait for next promo
- Feature regression — bug, UX change, performance issue
- Competitor activity — competitor launched similar mechanic
- Segment shift — acquired wrong user segment (incentive-driven)
Intervention Matrix
| Problem | Intervention | Expected Lift |
|---|---|---|
| D1 drop (activation) | Onboarding flow fix, welcome voucher | +5-15% |
| D7 drop (habit) | Push nudge series, streak reward | +3-10% |
| D30 drop (churn risk) | Re-engagement campaign, winback voucher | +2-8% |
| General decay | Loyalty program, points economy | +5-20% over 3 months |
Related Skills
- growth-mcp-connect — pull real cohort data
- churn-intervention — design save offers
- campaign-brief — write retention campaign brief
- voucher-mechanic-designer — design winback voucher
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.
- 11d ago First seen · 60 lines · 54 tokens per session scan A e88060ae4641
retention-analyzer is a skill published in the GitHub repository thaolst/ai-growth-agents-for-marketers (5 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 580 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 2026-08-31.
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