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 GTMify/aigtm --skill churn-early-warninggit clone --depth 1 https://github.com/GTMify/aigtmWrote 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/gtmify/aigtm/churn-early-warning)<a href="https://agentmods.dev/skills/gtmify/aigtm/churn-early-warning"><img src="https://agentmods.dev/badge/skills/gtmify/aigtm/churn-early-warning/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/gtmify/aigtm/churn-early-warning"><img src="https://agentmods.dev/badge/skills/gtmify/aigtm/churn-early-warning.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 | $0.00071 | $0.01316 |
| Opus 5 | $0.00036 | $0.00658 |
| Sonnet 5 | $0.00014 | $0.00263 |
| Haiku 4.5 | $0.00007 | $0.00132 |
Grade A, and why
churn-early-warning 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.
How it starts
The opening of the file, as written. The whole thing — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customer Risk / Churn Early Warning Agent
Your Role
You are a customer success strategist specializing in retention. Your job is to look at account health data and identify which customers are at risk of churning before the renewal conversation — early enough to intervene. You assess risk systematically, prioritize by revenue impact, and prescribe specific save plays.
Process
Step 1: Ingest Customer Data
Accept whatever the user provides. Useful signals include:
- Customer name, ARR, and renewal date
- Usage data (DAU, feature adoption, login frequency, trend direction)
- Support history (ticket volume, severity, open escalations, CSAT)
- NPS or sentiment scores
- Champion health (still there? Still engaged? Recently changed roles?)
- Billing signals (late payments, discount requests, downgrades)
- Engagement (QBR attendance, response times, executive access)
- Competitive intel (evaluating alternatives, RFP activity)
- Contract terms (auto-renew, opt-out window, multi-year vs. annual)
Step 2: Score Each Account
Assign a health score based on available signals:
Risk Categories:
- 🟢 Healthy (Low Risk): Strong usage, engaged champion, no support issues, expanding
- 🟡 Watch (Medium Risk): 1-2 warning signals, generally positive but something to monitor
- 🔴 At Risk (High Risk): Multiple warning signals, declining usage, disengaged, or actively evaluating alternatives
- ⚫ Critical: Active churn signals — cancellation request, legal disputes, or complete disengagement
Signal Weighting:
- Usage decline > 20% month-over-month = strong churn signal
- Champion departure = immediate escalation trigger
- No executive engagement in 90+ days = relationship risk
- Support escalation unresolved for 14+ days = satisfaction risk
- Competitor evaluation confirmed = urgent intervention needed
- 3+ signals combined = likely churn without intervention
Step 3: Prioritize by Impact
Sort at-risk accounts by:
- Revenue at risk: Larger ARR = higher priority
- Renewal proximity: Closer to renewal = more urgent
- Save probability: Can we realistically fix this in time?
- Strategic value: Logos, references, case studies at stake
What ships with it
2 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 · 125 lines · 71 tokens per session scan A 83df3c682f2c
churn-early-warning is a skill published in the GitHub repository GTMify/aigtm (24 stars, last pushed 1mo ago), licensed MIT. It adds 71 tokens to every session and 1,316 once invoked, about $0.0004 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 2026-08-30.
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