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.
git clone --depth 1 https://github.com/rajitsaha/100xprismWrote 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/rules/rajitsaha/100xprism/churn-prevention)<a href="https://agentmods.dev/rules/rajitsaha/100xprism/churn-prevention"><img src="https://agentmods.dev/badge/rules/rajitsaha/100xprism/churn-prevention.svg" alt="Measured on agentmods" 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 | $0.00032 | $0.03896 |
| Opus 5 | $0.00016 | $0.01948 |
| Sonnet 5 | $0.00006 | $0.00779 |
| Haiku 4.5 | $0.00003 | $0.00390 |
Grade A, and why
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 4d 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 — 382 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Churn Prevention
Reduce voluntary churn (customers choosing to cancel) and involuntary churn (failed payments) via cancel flows, dynamic save offers, proactive retention, and dunning.
Before Starting
Product context: If .agents/product-marketing-context.md exists (or .claude/product-marketing-context.md in older setups), read it first and tailor output to it; only ask for what it doesn't cover.
Gather this context (ask if not provided):
- Current churn situation — Monthly churn rate (voluntary vs. involuntary if known)? Active subscribers? Average MRR per customer? Existing cancel flow, or instant cancel?
- Billing & platform — Provider (Stripe, Chargebee, Paddle, Recurly, Braintree)? Monthly, annual, or both intervals? Pausing or downgrades supported? Retention tooling (Churnkey, ProsperStack, Raaft)?
- Product & usage data — Feature usage tracked per user? Engagement drop-offs identifiable? Cancellation reason data from past churns? Activation metric (what do retained users do that churned users don't)?
- Constraints — B2B or B2C (affects flow design)? Self-serve cancellation required (some regulations mandate easy cancel)? Offboarding brand tone (empathetic, direct, playful)?
How This Skill Works
Two churn types, two strategies:
| Type | Cause | Solution |
|---|---|---|
| Voluntary | Customer chooses to cancel | Cancel flows, save offers, exit surveys |
| Involuntary | Payment fails | Dunning emails, smart retries, card updaters |
Voluntary is typically 50-70% of total churn; involuntary is 30-50% but often easier to fix.
Three modes:
- Build a cancel flow — Design from scratch with survey, save offers, and confirmation
- Optimize an existing flow — Analyze cancel data and improve save rates
- Set up dunning — Failed payment recovery with retries and email sequences
Cancel Flow Design
The Cancel Flow Structure
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.
- 4d ago First seen · 382 lines · 3,896 tokens per session scan A 7e8184bc7979
churn-prevention is a cursor rule published in the GitHub repository rajitsaha/100xprism (10 stars, last pushed 7d ago), licensed MIT. It adds 32 tokens to every session and 3,896 once invoked, about $0.0002 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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