Borrowing it
Nothing to install: this file belongs to pskoett/measuring-ai-proficiency. 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/pskoett/measuring-ai-proficiency/main/.claude/skills/verify-gate/SKILL.mdgit clone --depth 1 https://github.com/pskoett/measuring-ai-proficiencyWrote 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/pskoett/measuring-ai-proficiency/verify-gate)<a href="https://agentmods.dev/skills/pskoett/measuring-ai-proficiency/verify-gate"><img src="https://agentmods.dev/badge/skills/pskoett/measuring-ai-proficiency/verify-gate/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/pskoett/measuring-ai-proficiency/verify-gate"><img src="https://agentmods.dev/badge/skills/pskoett/measuring-ai-proficiency/verify-gate.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 | $0.00076 | $0.01883 |
| Opus 5 | $0.00038 | $0.00941 |
| Sonnet 5 | $0.00015 | $0.00377 |
| Haiku 4.5 | $0.00008 | $0.00188 |
Grade A, and why
verify-gate 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 9d 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Verify Gate
Machine verification gate between implementation and quality review. Runs the project's compile, test, and lint commands. If any fail, enters a fix loop. If all pass, unblocks simplify-and-harden.
This is the inner loop's verify step. Without it, the agent hands off code with zero machine signal about whether it actually works.
When to Use
- After any implementation work completes, before signaling "done"
- Before running simplify-and-harden
- After fixing audit findings from agent-teams-simplify-and-harden
- Any time you want a machine-verified green signal
Pipeline Position
[implementation] → verify-gate → simplify-and-harden → self-improvement
↻ fix loop
Step 1: Discover Project Commands
Read the project's configuration to find verification commands. Check these sources in order:
- Project instruction files (CLAUDE.md, AGENTS.md, .github/copilot-instructions.md) — look for a
## Verificationor## Test Commandssection - package.json —
scripts.test,scripts.lint,scripts.typecheck,scripts.build. Also check for abun.lock/bun.lockbalongside it → preferbun run <script>overnpm run <script>when present. Check forpnpm-lock.yaml→ preferpnpm run. Check foryarn.lock→ preferyarn. - Makefile / Justfile —
test,lint,check,buildtargets - Cargo.toml —
cargo build,cargo test,cargo clippy - pyproject.toml / setup.cfg —
pytest,mypy,ruff - go.mod —
go build ./...,go test ./...,go vet ./... - deno.json / deno.jsonc —
deno task <name>for any defined tasks
If no commands are discoverable, ask the user once and suggest they add a ## Verification section to their project instruction files (CLAUDE.md, AGENTS.md, or equivalent) for future sessions:
## Verification
- Build: `npm run build`
- Test: `npm test`
- Lint: `npm run lint`
- Type check: `npx tsc --noEmit`
Step 2: Run Verification
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.
- 9d ago First seen · 202 lines · 76 tokens per session scan A d4377c845276
verify-gate is a skill published in the GitHub repository pskoett/measuring-ai-proficiency (11 stars, last pushed 1mo ago), licensed MIT. It adds 76 tokens to every session and 1,883 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.