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 ericrisco/rsc-harness --skill retentiongit clone --depth 1 https://github.com/ericrisco/rsc-harnessWrote 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/ericrisco/rsc-harness/retention)<a href="https://agentmods.dev/skills/ericrisco/rsc-harness/retention"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/retention/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/ericrisco/rsc-harness/retention"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/retention.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00096 | $0.02855 |
| Opus 5 | $0.00048 | $0.01427 |
| Sonnet 5 | $0.00019 | $0.00571 |
| Haiku 4.5 | $0.00010 | $0.00285 |
Grade A, and why
retention 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 6d 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 — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Retention
You run retention as a program, not as a reaction. The customer is already won; your job is to stop the slow leak out the bottom of the funnel — measure who is healthy, catch the at-risk ones 30+ days before they cancel, run the right save play, and win back the ones who already left.
This is the program layer. It is not:
- the single furious customer threatening to cancel right now — that live ticket
is
../customer-support/SKILL.md. - the first-30-days welcome/activation flow for a brand-new account — that is
../client-onboarding/SKILL.md. Onboarding prevents early churn; you start once the customer is established and the renewal is at stake.
What you produce
Three decision artifacts — judgment, not prose:
- A health-score model — weighted dimensions → a 0–100 number → green / yellow / red.
- A save-play decision table — exit reason → the play that retains the most life.
- A win-back cadence — a 30/60/90-day ladder with an escalating offer.
You do not write the production NPS or win-back email copy — that is
../newsletter/SKILL.md. You define the cadence and the offer ladder; the polished
words are a writing skill.
The retention loop (the spine)
Work these five steps in order. Each one feeds the next.
- Measure — build the health score and run NPS, so "at risk" is a number, not a hunch.
- Flag — set leading-indicator thresholds that fire 30+ days before the churn event, so you have time to act.
- Intervene — pick a save play before renewal, matched to the account's stated or signalled reason.
- Recover — run a win-back sequence on the ones who left anyway.
- Read the meters — NRR / GRR / logo churn / save rate tell you whether the loop is working and what to fix next.
Build the health score
A single signal lies. A composite of 4+ weighted dimensions predicts churn ~34% more accurately than any one-dimension gauge (Totango 2025). Weight activity heaviest, because when customers stop showing up, everything else follows.
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.
- 6d ago First seen · 207 lines · 96 tokens per session scan A ef41b3e82a23
retention is a skill published in the GitHub repository ericrisco/rsc-harness (78 stars, last pushed today), licensed MIT. It adds 96 tokens to every session and 2,855 once invoked, about $0.0005 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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