Borrowing it
Nothing to install: this file belongs to kint4/autoframe. 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/kint4/autoframe/main/.claude/skills/report/SKILL.mdgit 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/report)<a href="https://agentmods.dev/skills/kint4/autoframe/report"><img src="https://agentmods.dev/badge/skills/kint4/autoframe/report/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/kint4/autoframe/report"><img src="https://agentmods.dev/badge/skills/kint4/autoframe/report.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 | $0.00040 | $0.00375 |
| Opus 5.5 | $0.00016 | $0.00150 |
| Sonnet 5.5 | $0.00008 | $0.00075 |
| Haiku 4.5 | $0.00004 | $0.00038 |
Grade A, and why
report 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 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.
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
/report — Stakeholder Coverage Summary
Type: Reporting Description: Generates a non-technical test coverage summary for stakeholders — PMs, CTOs, or boards. Built for Persona 4.
Input Format
Optional:
- A feature scope
- Allure results / latest run results (if available)
With no input, summarize the current state of the suite from the spec files.
Output Format
A non-technical, outcome-focused summary (no code):
- Headline — one sentence on overall coverage and health
- What's tested — features covered, in plain language
- What's not yet tested — known gaps and risk
- Recent results — pass/fail trend if run data is available
- Recommendation — what to prioritize next
Step-by-Step Instructions
- Use Persona 4 tone: non-technical, outcome-focused, confident, no code.
- Build the coverage picture from the spec files (reuse
/coverage-maplogic if needed). - If Allure or run results are available, fold in pass/fail and trend; otherwise state coverage only and say results aren't included.
- Translate features into business language ("Users can log in and reset their password" — not "auth.spec.ts").
- Be honest about gaps and frame them as risk, not blame.
- End with a short, prioritized recommendation.
Rules
- No code in the output unless the user explicitly asks.
- Never overstate coverage — be honest about gaps.
- Keep it shareable: clean headings, plain language, no jargon.
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 · 44 lines · 40 tokens per session scan A f60e32153377
report is a skill published in the GitHub repository kint4/autoframe (8 stars, last pushed 4mo ago), licensed MIT. It adds 40 tokens to every session and 375 once invoked, about $0.0002 per session on Opus 5.5. 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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