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-hardest-problem-campaign/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-hardest-problem-campaign)<a href="https://agentmods.dev/skills/jerseycheese/narraitor/narraitor-hardest-problem-campaign"><img src="https://agentmods.dev/badge/skills/jerseycheese/narraitor/narraitor-hardest-problem-campaign/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-hardest-problem-campaign"><img src="https://agentmods.dev/badge/skills/jerseycheese/narraitor/narraitor-hardest-problem-campaign.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 | $0.00105 | $0.02405 |
| Opus 5.5 | $0.00042 | $0.00962 |
| Sonnet 5.5 | $0.00021 | $0.00481 |
| Haiku 4.5 | $0.00011 | $0.00241 |
Grade A, and why
narraitor-hardest-problem-campaign 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 2mo 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.
npm run dev # then: curl -s localhost:3000 | head -1 -> HTML How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Campaign: close the v1.0 gate (and prove the AI play loop)
1. Purpose
Drive the release gate to a decision with evidence. Everything automated is already strong (2399 unit tests, visual regression, boundary gates); the hard residue is the live AI loop, which only a disciplined manual campaign can validate. This skill is that campaign, resumable by any session.
2. When to use
Release-gate work; "is v1.0 shippable"; full play-loop validation; resuming a partially-run campaign (start at the phase whose evidence is missing).
3. When not to use
- A single bug inside the loop →
narraitor-debugging-playbook+ change-control fix loop. - Post-1.0 ambitions →
narraitor-product-frontier.
4. Inputs required
Local Mac (visual suites are macOS-only), ~2 focused hours for Phase 3, and a working Gemini key: for the dev server, set GEMINI_API_KEY in .env.local (server-side fallback via resolveApiKey); alternatively enter a key in the app at /settings/providers (BYO path — sent per-request as the x-provider-api-key header). Phase 3 must exercise the BYO path at least once, since that's what players use.
5. Procedure
Phase 0 — preflight.
git fetch && git status # on develop, clean, up to date — or stop
npm ci && npm test && npm run type-check && npm run lint && npm run lint:css
lsof -nP -iTCP:3000 -sTCP:LISTEN # know who owns the port before starting
npm run dev # then: curl -s localhost:3000 | head -1 -> HTML
Expected: all green (baseline 2026-07-04: 353 suites / 2399 tests). Branch: any red here is NOT campaign work — fix via debugging-playbook first.
Phase 1 — confirm what the gate still contains.
gh issue view 1417 --json title,state,body # v1.0 QA tracking epic
gh issue view 1320 --json title,state # release tracking
gh issue list --milestone "" --label MVP --state open
gh issue view 1434 --json state,title # last open QA finding (character-creation DS/UX)
gh issue view 1477 --json state,title # touch targets - launch-blocking? ASK OWNER
Expected (as of 2026-07-04): QA findings #1423–#1438 closed except #1434 and post-1.0 #1438. Branch: if new blockers appeared, list them, triage against the roadmap (public_docs/development/mvp-roadmap.md), and get owner sign-off on the blocking set before proceeding. #1477's launch status is owner-confirmation-needed — do not decide it yourself.
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.
- 2mo ago First seen · 112 lines · 105 tokens per session scan A 6c47936b51c4
narraitor-hardest-problem-campaign is a skill published in the GitHub repository jerseycheese/Narraitor (32 stars, last pushed 4d ago), licensed MIT. It adds 105 tokens to every session and 2,405 once invoked, about $0.0004 per session on Opus 5.5. 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.
Other skills, from other repositories
regression-suite
Map test coverage to GDD critical paths, find fixed bugs lacking regression tests, flag drift from new features.
soak-test
Soak test protocol for extended play — what to observe and log for slow leaks, fatigue, late-appearing edge cases.
playtest-report
Structured playtest report template, or turn existing playtest notes into structured feedback.
team-polish
Orchestrate the polish team — performance-analyst, technical-artist, sound-designer, qa-tester — to optimize and harden a feature or area.
develop-web-game
Use when Codex is building or iterating on a web game (HTML/JS) and needs a reliable development + testing loop: implement small changes, run a Playwright-based test script with short input bursts and intentional pauses, inspect screenshots/text, and review console errors with rendergametotext.
smoke-check
Critical-path smoke gate before QA hand-off — runs the automated suite. A failed check means the build is not QA-ready.