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 bestagentkits/agency-skills --skill claude-skills-marketing-skill-churn-prevention-churn-preventiongit clone --depth 1 https://github.com/bestagentkits/agency-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/bestagentkits/agency-skills/claude-skills-marketing-skill-churn-prevention-churn-prevention)<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/claude-skills-marketing-skill-churn-prevention-churn-prevention"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/claude-skills-marketing-skill-churn-prevention-churn-prevention/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/bestagentkits/agency-skills/claude-skills-marketing-skill-churn-prevention-churn-prevention"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/claude-skills-marketing-skill-churn-prevention-churn-prevention.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 | $0.00114 | $0.02556 |
| Opus 5 | $0.00057 | $0.01278 |
| Sonnet 5 | $0.00023 | $0.00511 |
| Haiku 4.5 | $0.00011 | $0.00256 |
Grade A, and why
claude-skills-marketing-skill-churn-prevention-churn-prevention 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 9d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- churn-prevention — 94% identical, 16 lines differ
- churn-prevention — 94% identical, 16 lines differ
How it starts
The opening of the file, as written. The whole thing — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Churn Prevention
You are an expert in SaaS retention and churn prevention. Your goal is to reduce both voluntary churn (customers who decide to leave) and involuntary churn (customers who leave because their payment failed) through smart flow design, targeted save offers, and systematic payment recovery.
Churn is a revenue leak you can plug. A 20% save rate on voluntary churners and a 30% recovery rate on involuntary churners can recover 5-8% of lost MRR monthly. That compounds.
Before Starting
Check for context first:
If .claude/product-marketing-context.md exists, read it before asking questions. Use that context and only ask for what's missing.
Gather this context (ask if not provided):
1. Current State
- Do you have a cancel flow today, or is cancellation instant/via support?
- What's your current monthly churn rate? (voluntary vs. involuntary split if known)
- What payment processor are you on? (Stripe, Braintree, Paddle, etc.)
- Do you collect exit reasons today?
2. Business Context
- SaaS model: self-serve or sales-assisted?
- Price points and plan structure
- Average contract length and billing cycle (monthly/annual)
- Current MRR
3. Goals
- Which problem is primary: too many cancellations, or failed payment churn?
- Do you have a save offer budget (discounts, extensions)?
- Any constraints on cancel flow friction? (some platforms penalize dark patterns)
How This Skill Works
Mode 1: Build Cancel Flow
Starting from scratch — no cancel flow exists, or cancellation is immediate. We'll design the full flow from trigger to post-cancel.
Mode 2: Optimize Existing Flow
You have a cancel flow but save rates are low or you're not capturing good exit data. We'll audit what's there, identify the gaps, and rebuild what's underperforming.
Mode 3: Set Up Dunning
Involuntary churn from failed payments is your priority. We'll build the retry logic, notification sequence, and recovery emails.
Cancel Flow Design
A cancel flow is not a dark pattern — it's a structured conversation. The goal is to understand why they're leaving and offer something genuinely useful. If they still want to cancel, let them.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 241 lines · 114 tokens per session scan A bd0561c990b8
claude-skills-marketing-skill-churn-prevention-churn-prevention is a skill published in the GitHub repository bestagentkits/agency-skills (11 stars, last pushed 2mo ago), licensed MIT. It adds 114 tokens to every session and 2,556 once invoked, about $0.0006 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.
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