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 Cratis/AI --skill risk-based-testing-with-code-healthgit clone --depth 1 https://github.com/Cratis/AIWrote 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/cratis/ai/risk-based-testing-with-code-health)<a href="https://agentmods.dev/skills/cratis/ai/risk-based-testing-with-code-health"><img src="https://agentmods.dev/badge/skills/cratis/ai/risk-based-testing-with-code-health/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/cratis/ai/risk-based-testing-with-code-health"><img src="https://agentmods.dev/badge/skills/cratis/ai/risk-based-testing-with-code-health.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 | $0.00059 | $0.01373 |
| Opus 5.5 | $0.00024 | $0.00549 |
| Sonnet 5.5 | $0.00012 | $0.00275 |
| Haiku 4.5 | $0.00006 | $0.00137 |
Grade A, and why
risk-based-testing-with-code-health 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 7d 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Risk-Based Testing With Code Health
Use only in repositories that opted into cratis/codescene and have CodeScene MCP tools available; otherwise skip without blocking and say so. The Cratis policy in .cratis/ai/rules/codescene.md overrides this skill where they differ.
Overview
Use this skill when the goal is to turn CodeScene risk signals into practical testing priorities. The output should help testers and developers focus limited test time on the code most likely to produce defects.
The primary signal is always the current branch's change set — what has actually changed is more immediately actionable than long-standing hotspots. Hotspots provide background context and catch systemic risk, but they should not displace focus from files the branch has already touched.
When to Use
- A user asks where testing should focus based on CodeScene hotspots.
- A user asks for a tester-friendly test plan from technical debt or code health findings.
- A user wants pull-request risk translated into concrete test scenarios.
- A release owner asks for risk-based regression scope before ship.
Do not use this skill when the user only asks for conceptual definitions of Code Health.
Use explaining-code-health for fundamentals.
Do not use this skill when the user asks for quantified financial ROI of refactoring.
Use making-the-business-case-for-code-health for ROI framing.
Minimum Inputs
project_id(CodeScene project identifier)base_ref(for change-set analysis, e.g.mainororigin/main)git_repository_path(absolute local repository path)- Optional: business criticality hints from the user (for example: release-blocking areas, customer-facing commands)
Quick Reference
list_technical_debt_hotspots_for_project: Identify high-risk files using low Code Health and high past churn.analyze_change_set: Evaluate immediate risk in the current branch vs target base.code_health_score: Inspect specific files in detail.code_ownership_for_path: Route high-risk areas to likely reviewers/domain experts.list_technical_debt_goals_for_project: Include existing debt goals when present.
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.
- 7d ago Changed 260dfebf0321
- 11d ago First seen · 131 lines · 59 tokens per session scan A a43bd69abf49
risk-based-testing-with-code-health is a skill published in the GitHub repository Cratis/AI (4 stars, last pushed yesterday), licensed MIT. It adds 59 tokens to every session and 1,373 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-11.
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