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 petrkindlmann/qa-skills --skill risk-based-testinggit clone --depth 1 https://github.com/petrkindlmann/qa-skillsWrote 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/petrkindlmann/qa-skills/risk-based-testing)<a href="https://agentmods.dev/skills/petrkindlmann/qa-skills/risk-based-testing"><img src="https://agentmods.dev/badge/skills/petrkindlmann/qa-skills/risk-based-testing.svg" alt="Measured on agentmods" height="20"></a>- Socket pass
- Snyk 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 | $0.00156 | $0.04867 |
| Opus 5 | $0.00078 | $0.02433 |
| Sonnet 5 | $0.00031 | $0.00973 |
| Haiku 4.5 | $0.00016 | $0.00487 |
Grade A, and why
risk-based-testing 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 8d 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 — 338 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quick Route
| Situation | Start at |
|---|---|
| New product, no risk model yet | Phase 1 (Identification) → run the full 6 phases |
| Post-incident reassessment | Phase 6 (Reassessment triggers), then re-score the affected items in Phase 2 |
| AI/LLM feature to assess | Phase 3 (AI/LLM failure classes), score each class in Phase 2 |
| Sprint refresh of an existing matrix | Phases 4–5 (Heatmap + Coverage alignment) on changed features |
| Verify an old heatmap is still true | Phase 6 signals + Anti-Pattern "Risk Theater" |
Discovery Questions
Check .agents/qa-project-context.md first — if it exists, use it as the foundation and skip questions already answered there. Gather the rest from stakeholders across engineering, product, and operations.
Revenue-Critical Flows
- Which user flows directly generate revenue? (checkout, subscription, billing, upgrades)
- What is the revenue impact per hour of downtime for each flow?
- Are there time-sensitive flows? (flash sales, market-hours trading, payroll deadlines)
- Which flows have contractual SLAs with financial penalties?
Recent Failures
- What broke in the last 3 releases? What escaped to production?
- What were the root causes? (code defect, config error, third-party failure, data migration)
- What was the blast radius of each incident? (users affected, revenue lost, reputation impact)
- Were there near-misses caught late in testing that could have escaped?
Fragile Areas
- Which parts of the codebase change most frequently? (high churn = high risk)
- Which modules have the lowest test coverage today?
- Which areas have the most complex business logic or the most conditional branches?
- Which code was written by engineers who have since left the team?
What ships with it
1 file 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.
- 8d ago First seen · 338 lines · 156 tokens per session scan A d39f564e2389
risk-based-testing is a skill published in the GitHub repository petrkindlmann/qa-skills (113 stars, last pushed 2mo ago), licensed MIT. It adds 156 tokens to every session and 4,867 once invoked, about $0.0008 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.
Other skills, from other repositories
test
Enter the Test phase of CocoBrew. Reads spec.md test requirements, generates test cases, executes SQL validation and quality checks, records results in test.md. Can be re-run without full rebuild. Requires Build phase completion.
meter-compare
Compare CocoMeter accuracy and cost results with correctness-first ordering. Usage: $meter compare .
Vibe Coding Mastery
The complete operating system for building software with AI. From first prompt to production deployment — prompting frameworks, architecture patterns, testing strategies, debugging playbooks, and production graduation checklists. Works with Claude Code, Cursor, Windsurf, Copilot, and any AI coding tool.
contract
Outcome-driven Cortex function development — declares a behavioral contract before generation begins, enforces evidence-tiered proof before $ship, and defends against the self-oracle evaluation failure mode.
Auth Bypass Tester
Comprehensive authentication and authorization bypass testing including session hijacking, privilege escalation, JWT manipulation, and access control verification.
Angry User Simulator
Simulate aggressive user behavior patterns including rapid clicking, random navigation, form abuse, tab spamming, and unexpected interaction sequences to find UI resilience issues.