Borrowing it
Nothing to install: this file belongs to jerseycheese/Narraitor. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/jerseycheese/Narraitor/main/.claude/skills/narraitor-validation-and-qa/SKILL.mdgit clone --depth 1 https://github.com/jerseycheese/NarraitorWrote 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/jerseycheese/narraitor/narraitor-validation-and-qa)<a href="https://agentmods.dev/skills/jerseycheese/narraitor/narraitor-validation-and-qa"><img src="https://agentmods.dev/badge/skills/jerseycheese/narraitor/narraitor-validation-and-qa/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/jerseycheese/narraitor/narraitor-validation-and-qa"><img src="https://agentmods.dev/badge/skills/jerseycheese/narraitor/narraitor-validation-and-qa.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 | $0.00093 | $0.01400 |
| Opus 5 | $0.00046 | $0.00700 |
| Sonnet 5 | $0.00019 | $0.00280 |
| Haiku 4.5 | $0.00009 | $0.00140 |
Grade A, and why
narraitor-validation-and-qa scanned grade A with 1 finding 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 10d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| Manual QA walkthrough | Human/agent plays the real app: onboarding → world + character creation → live story loop (real key) → consequences → endings → theming; smoke AI routes via curl | The product actually works, in How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Validation & QA — what counts as evidence
1. Purpose
Map every verification tier to what it actually proves, so claims ride on the right tier and gaps are named instead of papered over.
2. When to use
Choosing tests for a change; interpreting failures; assessing a "tests pass" claim; planning release QA.
3. When not to use
- Environment-level failures →
narraitor-build-test-env. - AI output quality →
narraitor-ai-quality-discipline(its matrix is the tier for generation behavior).
4. Inputs required
The change class (per change-control) and the diff.
5. Procedure — the tier table
| Tier | Command / surface | Proves | CANNOT prove |
|---|---|---|---|
| Unit/integration (Jest, jsdom) | npm test — co-located src/**/__tests__, *.test.* |
Logic, parsing, template assembly, store actions | Rendering in a real browser, hydration, visual truth, AI prose quality |
| Storybook | npm run storybook + lint:ds-canon |
Design intent, isolated render states, a11y addon checks | App integration, real data shapes, route/network behavior (S0/S1 on the parity ladder) |
Visual regression / e2e critical (Playwright — NOTE: test:visual and test:e2e:critical are the SAME command, playwright test --project=chromium; two script names, one tier) |
npm run test:visual (dev server running; macOS baselines *-chromium-darwin.png, threshold 0.2 / maxDiffPixels 10k; post-hasHydrated store seeding) |
Pixel stability of the SEEDED states + wired flows the specs exercise | Anything the seeds don't cover; correctness of behavior; live AI generation (gated off via isPlaywrightEnv()); cross-world generality |
| Manual QA walkthrough | Human/agent plays the real app: onboarding → world + character creation → live story loop (real key) → consequences → endings → theming; smoke AI routes via curl | The product actually works, including the AI loop | Regression protection over time (it's a point-in-time gate) |
Rules of evidence:
- A claim may only cite tiers that cover it. "Tests pass" (unit) says nothing about a layout bug; "visual suite green" says nothing about a prompt change (AI is gated off there).
- Green-but-irrelevant is a coverage gap, not evidence — check what state a baseline actually seeds before citing it.
- The AI play loop has NO automated tier by design. Claims about it require the manual walkthrough or the ai-quality-discipline matrix. This is the project's largest standing gap — say so rather than implying coverage.
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.
- 10d ago First seen · 68 lines · 93 tokens per session scan A c408d4e4b709
narraitor-validation-and-qa is a skill published in the GitHub repository jerseycheese/Narraitor (30 stars, last pushed today), licensed MIT. It adds 93 tokens to every session and 1,400 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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