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 agentmods add skills/kint4/autoframe/review-specnpx skills add kint4/autoframe --skill review-specgit clone --depth 1 https://github.com/kint4/autoframeWrote 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/kint4/autoframe/review-spec)<a href="https://agentmods.dev/skills/kint4/autoframe/review-spec"><img src="https://agentmods.dev/badge/skills/kint4/autoframe/review-spec.svg" alt="Measured on agentmods" 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 | $0.00039 | $0.00425 |
| Opus 5 | $0.00019 | $0.00212 |
| Sonnet 5 | $0.00008 | $0.00085 |
| Haiku 4.5 | $0.00004 | $0.00042 |
Grade A, and why
review-spec 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 5d 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
/review-spec — Review a Spec from a QA Perspective
Type: Quality Description: Reviews a spec file from a QA perspective, flagging weak assertions, missing coverage, and implementation-focused tests. Built for Persona 2 and 3.
Input Format
- A path to a spec file (or a pasted spec)
Output Format
A structured review:
- Strengths — what the spec does well
- Findings — each issue with severity (blocker / major / minor), location, and a concrete fix
- Missing coverage — scenarios not yet tested (negative / edge)
- Verdict — ship / revise
Step-by-Step Instructions
- Read the spec and its Page Object(s).
- Read the spec conventions in CLAUDE.md.
- Evaluate against QA quality criteria:
- Assertion strength: Are assertions meaningful, or just
toBeVisible()everywhere? Do they verify the actual outcome? - Behavior vs implementation: Does it test what the user experiences, or internal/DOM details? (Common Persona 3 mistake.)
- Coverage: Are Happy Path, Negative, and Edge cases all present and non-trivial?
- Structure: AAA comments, plain-language names, fixture usage, no logic in
describe. - Isolation: No order dependencies or shared mutable state.
- Assertion strength: Are assertions meaningful, or just
- List findings with severity, exact location, and a concrete fix each.
- Call out missing scenarios explicitly and suggest
/new-specor/tc-to-specto fill them. - Give a clear verdict.
Rules
- Be specific — cite line locations and propose concrete fixes.
- Favor behavior-focused assertions over implementation checks.
- Do not rewrite the whole file unless asked; this skill reviews. Offer
/refactorfor big rewrites.
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.
- 5d ago First seen · 44 lines · 0 tokens per session scan A c7aef2811979
review-spec is a skill published in the GitHub repository kint4/autoframe (6 stars, last pushed 2mo ago), licensed MIT. It adds 39 tokens to every session and 425 once invoked, about $0.0002 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.
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