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 eddiebelaval/squire --skill churn-predictorgit clone --depth 1 https://github.com/eddiebelaval/squireWrote 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/eddiebelaval/squire/churn-predictor)<a href="https://agentmods.dev/skills/eddiebelaval/squire/churn-predictor"><img src="https://agentmods.dev/badge/skills/eddiebelaval/squire/churn-predictor/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/eddiebelaval/squire/churn-predictor"><img src="https://agentmods.dev/badge/skills/eddiebelaval/squire/churn-predictor.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 | $0.00018 | $0.02761 |
| Opus 5 | $0.00009 | $0.01380 |
| Sonnet 5 | $0.00004 | $0.00552 |
| Haiku 4.5 | $0.00002 | $0.00276 |
Grade A, and why
Churn Predictor 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 — 351 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Churn Predictor
Expert churn prediction system that identifies at-risk customers before they leave using behavioral signals, engagement patterns, and predictive analytics. This skill provides structured workflows for building churn models, monitoring risk signals, and executing retention interventions.
Churn is the silent killer of growth. By the time a customer announces they're leaving, it's often too late. This skill helps you identify churn risk early when intervention can still make a difference, prioritize retention efforts, and systematically reduce churn.
Built on data science best practices and customer success methodologies, this skill combines leading indicator analysis, risk scoring, and intervention playbooks to predict and prevent churn before it happens.
Core Workflows
Workflow 1: Churn Signal Identification
Map the behaviors that predict churn
-
Behavioral Signals
Signal Type Examples Risk Level Usage Decline 30%+ drop in logins, sessions, actions High Feature Abandonment Stopped using key features Medium-High Engagement Drop No response to emails, missed meetings Medium Support Patterns Spike in tickets, negative sentiment High Billing Issues Failed payments, downgrade requests High -
Account Signals
- Champion departure (key user leaves)
- Company layoffs or restructuring
- Merger/acquisition announcements
- Budget cuts affecting your category
- Competitor evaluation signals
- Contract not renewed on auto-renew
-
Relationship Signals
- NPS score decline (9-10 → 7 or below)
- Missed QBRs or check-ins
- Unresponsive to outreach
- Escalated support issues
- Negative sentiment in communications
-
Time-Based Signals
- Approaching renewal (90/60/30 days)
- End of trial or pilot
- Anniversary of bad experience
- Post-implementation plateau
- Seasonal usage patterns
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 · 351 lines · 18 tokens per session scan A dabb582dafd3
Churn Predictor is a skill published in the GitHub repository eddiebelaval/squire (21 stars, last pushed 25d ago), licensed MIT. It adds 18 tokens to every session and 2,761 once invoked, about $0.0001 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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