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 Neeeophytee/finding-unknowns-skills --skill change-quizgit clone --depth 1 https://github.com/Neeeophytee/finding-unknowns-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/neeeophytee/finding-unknowns-skills/change-quiz)<a href="https://agentmods.dev/skills/neeeophytee/finding-unknowns-skills/change-quiz"><img src="https://agentmods.dev/badge/skills/neeeophytee/finding-unknowns-skills/change-quiz/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/neeeophytee/finding-unknowns-skills/change-quiz"><img src="https://agentmods.dev/badge/skills/neeeophytee/finding-unknowns-skills/change-quiz.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 | $0.00051 | $0.00479 |
| Opus 5 | $0.00026 | $0.00239 |
| Sonnet 5 | $0.00010 | $0.00096 |
| Haiku 4.5 | $0.00005 | $0.00048 |
Grade A, and why
change-quiz 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 11d 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.
What it actually says
Change quiz
After a long session the agent has often done more than the user realizes, and a diff only shows surface. Behavior lives in how the change interacts with existing code paths. The user should merge only what they can pass a quiz on.
Steps
- Build the report first, in four short sections:
- Context — what problem this session set out to solve.
- What changed — grouped by intent (feature, fix, refactor), not by file.
- How it interacts — the existing code paths the change touches, and what now behaves differently even in files the diff doesn't show.
- Intuition — the 2-3 mental-model updates the user should walk away with ("retries are now idempotent because X").
- For long sessions, offer the report as a single self-contained HTML page with the quiz at the bottom — it reads better than a wall of markdown.
- Then the quiz: 5-8 questions targeting what would bite an unaware maintainer.
- Mix recall ("what happens to in-flight jobs during deploy now?") with prediction ("if someone calls X with a stale token, what do they see?").
- Weight questions toward deviations, edge cases, and interaction effects — not trivia about names.
- Grade honestly, one round at a time. For each miss, explain the right answer AND flag it: a miss is either a gap in the user's model or a sign the change is too clever — say which.
- Pass = merge-ready. Fail = point back to the specific report sections to reread, then offer a fresh variant quiz. Do not soften the bar; the whole point is that unread changes don't ship.
Guardrails
- The quiz covers the change and its blast radius, not general knowledge.
- If the user can't pass after two rounds, the recommendation is to simplify the change or split it, not to keep quizzing.
- Never mark the user correct out of politeness. A false pass defeats the skill.
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.
- 11d ago First seen · 29 lines · 51 tokens per session scan A 5e6835411aaf
change-quiz is a skill published in the GitHub repository Neeeophytee/finding-unknowns-skills (328 stars, last pushed yesterday), licensed MIT. It adds 51 tokens to every session and 479 once invoked, about $0.0003 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
few-shot-quality-prompting
Master guide for crafting prompts that make AI models produce professional-quality code and UI consistently. Trigger whenever the user asks about prompt engineering, improving AI output quality, building system prompts, few-shot examples, making AI write better code, prompt optimization, or says "how to prompt"…
claude-md-improver
Audit and improve CLAUDE.md files in repositories. Use when user asks to check, audit, update, improve, or fix CLAUDE.md files. Scans for all CLAUDE.md files, evaluates quality against templates, outputs quality report, then makes targeted updates. Also use when the user mentions "CLAUDE.md maintenance" or "project…
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
gke-workload-security
Audits, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running cluster security audits (auditcluster.sh), configuring Workload Identity Federation (impersonation, KSA/GSA binding, and pod setup), enforcing Network Policies (default-deny…
gke-reliability
Improves GKE workload reliability, using PDBs, health probes, and topology spread constraints. Use when configuring GKE workload reliability, setting up PDBs, or configuring GKE health probes (liveness, readiness, startup). Don't use for disaster recovery setup or full cluster backups (use gke-backup-dr instead).
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.