audit-report
Skill0 starsChanged 8 days agoMITView on GitHub
Render audit findings as a designed, paginated A4 PDF report with a cover page, severity donut and category bar charts, colored severity chips, evidence-backed strengths, prioritized recommendations, and copy-ready GitHub issue blocks. English by default, with a pt-BR label pack for when the user is working in Portuguese. Self-verifying (page count plus page rasterization before delivery), and it leaves a re-runnable generator script beside the report. Use when the user asks for a security audit report as a PDF, a findings report with charts, a formatted deliverable from a code review or audit, GitHub issue text generated from findings, or says "gere um relatório em PDF" / "relatório de auditoria". Pairs with /wstg mode 2 (which produces the findings) and works for any audit, not only security. Runs in an isolated venv, installs nothing globally.
- Installs packages
3 other projects use a version of this.
Adopted by
Most recently taken up by
- harness/harness-skillstheir own version, first seen Nov 1, 2026
- ChariereFiedler/ronce-racinetheir own version, first seen Oct 22, 2026
- bbeliso/belisodw-their own version, first seen Jun 21, 2026
Other versions3 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 3
- harness/harness-skills★ 127same name
- ChariereFiedler/ronce-racine★ 9same name
- bbeliso/belisodw-★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository31 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- ai-estimateSkill
- aso-craftSkill
- babysit-prsSkill
- blueprintSkill
- brag-docSkill
- business-briefSkill
- cagan-checkSkill
- code-craftSkill
- commit-reportSkill
- compassSkill
- cv-craftSkill
- dev-flowSkill
- grill-meSkill
- implementation-reviewSkill
- llm-wikiSkill
- parallel-worktreesSkill
- periscopeSkill
- portfolio-syncSkill
- pr-craftSkill
- problem-solvingSkill
- publish-skillSkill
- review-passSkill
- security-auditSkill
- semverSkill
- sentinelSkill
- seoSkill
- testing-philosophySkill
- to-prdSkill
- write-a-skillSkill
- wstg-security-testingSkill
The file
We split this skill into the rules it gives an agent
9 rules. 0 are common, 4 are distinctive, and 5 are very specific to this project.
0
Common
Most of the 3 edited versions we read keep these.
4
Distinctive
This author's own choices. Worth a look.
5
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 20
Confirm the findings exist: If the user asked for a report without an audit having run, run the audit first (/wstg mode 2) or ask for the …
- Very specific: line 23
Pick the output directory: Default docs/security-audit/ for security audits, docs/audits/<topic>/ otherwise.
- Distinctive: line 35
Fix defects and re-render: before handing anything over.
- Very specific: line 63
Add .venv/ and _verify/ to the project's .gitignore.
- Very specific: line 70
Only project and findings are required.
- Distinctive: line 123
Weaknesses are the two or three sentences a lead reads: Not a restatement of the findings list: the systemic shape behind it.
- Distinctive: line 162
Keep code lines in the Markdown under ~90 characters.
- Distinctive: line 171
Verify before delivering: Read the rasterized pages.
- Very specific: line 176
English by default: Write the report in English unless the user is working in another language, then mirror theirs and set lang to match.
“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
audit-report
Turns a findings list into a document someone will actually read. This skill owns report
production only: it does not find anything. The findings come from /wstg mode 2 (see its
reference/CODEBASE-AUDIT.md), from /security-audit on a diff, or from any other review.
The contract is a JSON file. You write the JSON, the bundled script renders the PDF. That split is the point: re-running the report after a fix means editing JSON and re-running one command, not regenerating a document by hand.
Protocol
- Confirm the findings exist. If the user asked for a report without an audit having run,
run the audit first (
/wstgmode 2) or ask for the findings. Never invent findings to fill a template, and never soften a severity to make a chart look better. - Pick the output directory. Default
docs/security-audit/for security audits,docs/audits/<topic>/otherwise. Everything lands there: the JSON, the script, the PDF. - Write
findings.jsonper the schema below. Write it in English unless the user is working in another language, in which case match theirs and setlangaccordingly. The script shipsen(default) andpt-BRlabel packs. - Copy the generator from this skill's
scripts/render_report.pyinto the output directory, so the report is reproducible without the skill. - Bootstrap the venv (below) and render with
--verify. - Look at the pages.
--verifywrites_verify/page-NN.png. Read them. Check: charts present and not overlapping, no text overflowing a cell, no path broken mid-token, code blocks not clipped at the right margin, header and footer on every page except the cover, accented characters rendering. - Fix defects and re-render before handing anything over. A report delivered without looking at it is not verified, whatever the script printed.
- Report the paths: PDF, JSON, script, and the page count.
Environment
Isolated venv, nothing global.
# Windows (python on PATH here is 2.x, so use the py launcher)
py -3 -m venv docs/security-audit/.venv
docs/security-audit/.venv/Scripts/python.exe -m pip install --quiet reportlab matplotlib pypdf pymupdf
docs/security-audit/.venv/Scripts/python.exe docs/security-audit/render_report.py \
docs/security-audit/findings.json -o docs/security-audit/report.pdf --verify
# POSIX
python3 -m venv docs/security-audit/.venv
docs/security-audit/.venv/bin/pip install --quiet reportlab matplotlib pypdf pymupdf
docs/security-audit/.venv/bin/python docs/security-audit/render_report.py \
docs/security-audit/findings.json -o docs/security-audit/report.pdf --verifyreportlab and matplotlib are required. pypdf and pymupdf power --verify only, and the
script degrades with a printed notice if they are missing, which means you lose the visual
check, so install them unless something blocks it.
Add .venv/ and _verify/ to the project's .gitignore. Commit the JSON, the script, and
the PDF.
findings.json schema
Only project and findings are required. Every other key is optional and its section is
omitted when absent.
{
"lang": "en", // "en" (default) | "pt-BR"
"project": "Acme Console",
"report_title": "Security Audit Report", // optional, defaults per lang
"date": "28 August 2026",
"scope": "What was audited, in one paragraph.",
"methodology": "How each category was mapped to the detected stack.",
"stack": [["Linguagem", "TypeScript 5.4"], ["Auth", "JWT HS256"]],
"findings": [{
"id": "F1",
"severity": "critical", // critical|high|medium|low|informational
// (pt-BR names also accepted: critica|alta|...)
"category": "Missing tenant isolation",
"title": "Short description, one line",
"file": "src/routes/reports.ts",
"lines": "88-104",
"wstg": "WSTG-ATHZ-02", // optional
"code": "the actual snippet, unmodified",
"why": "Who sends what request and what they get.",
"preconditions": "Feature flag, role, config. Or 'Nenhuma.'",
"impact": "What the attacker achieves.",
"fix": "The specific control at the specific place."
}],
"strengths": [{ "title": "...", "evidence": "file, mechanism, lines verified" }],
"weaknesses": ["The central risks, in prose."],
"not_applicable": [{ "category": "...", "reason": "why this category has no equivalent" }],
"coverage": [{ "sweep": "IDOR", "checked": "47 handlers, all verbs", "findings": 1 }],
"recommendations": [{ "priority": "P1", "text": "...", "refs": ["F1", "F3"] }],
"issues": [{ "n": 1, "markdown": "full issue body, see below" }]
}Severity drives the palette, which is fixed in the script: crítica #B91C1C, alta
#EA580C, média #D97706, baixa #2563EB, informativa #64748B, ponto forte #059669.
Change it in PALETTE only if the user asks.
A finding with no code renders without an evidence block. Include the snippet, it is what
makes the report checkable.
Writing the sections
Strengths need evidence, not adjectives. "Autorização parece correta" is worthless.
"src/routes/documents.ts — all 9 GET handlers load through requireDocAccess() (:14),
verified at :31, :48, :66, ..." is the coverage proof that makes the whole report credible.
Weaknesses are the two or three sentences a lead reads. Not a restatement of the findings list: the systemic shape behind it. "Isolation depends on a manual predicate repeated in 63 places, correct in 62" says more than any individual finding.
not_applicable is a feature. A skipped category with a stated reason is a stronger report than a forced finding. Say which categories the stack has no equivalent for.
Recommendations are ordered work, not a restatement. P1 is what someone does today. Group findings that share one fix into one recommendation and list the refs.
GitHub issue blocks
The issues array holds full Markdown bodies. The script wraps each in
--- ISSUE n --- / --- END ISSUE n --- delimiters (FIM under pt-BR) and renders it
monospaced, so the reader selects between the markers and pastes into GitHub.
Every issue carries:
- Title
# [Security] <short description>([Segurança]underpt-BR). - Labels line:
securityplus the severity label. - Problem — what is wrong and why it is exploitable.
- Evidence —
file:lineand the fenced snippet. - Impact — what the attacker achieves.
- Suggested fix — the fix, with a code block when the fix is code.
- Acceptance criteria — a verifiable checklist. Each box must be something a reviewer can confirm by looking. "Fix the handler" fails that test, "an integration test with two tenants confirms A cannot see B's invoices" passes.
Issue bodies are written in the report's language, so they match the surrounding document.
Group related trivia into one issue. Four secret defaults across compose, env, CI, and a chart are one issue titled for the theme, not four. Group when the fix is one change or one sitting of work; split when they touch different subsystems or different owners.
Not every finding earns an issue. Informative findings and confirmed non-issues stay in the report. Actionable ones become issues.
Keep code lines in the Markdown under ~90 characters. The renderer hard-wraps at 96 with a continuation indent, which is safe but reads worse than lines that already fit.
Rules
- Never print a live secret value.
file:lineplus a redacted prefix. This applies to thecodefield, which is copied verbatim into the PDF. - Verify before delivering. Read the rasterized pages. Report the page count.
- Leave the generator behind. The script goes in the output directory, not just the venv. A report nobody can regenerate is a dead end after the first fix.
- Do not restyle per report. The layout and palette are fixed so two reports on the same project are comparable. User request overrides this.
- English by default. Write the report in English unless the user is working in another
language, then mirror theirs and set
langto match. Do not infer a language from the codebase, the client, or a previous report. To add a language, add a key toLABELSin the script. - The script renders, it does not judge. Severity, wording, and grouping are decided in the audit and written into the JSON.
- Obey CLAUDE.md conventions (
rtkprefix on shell commands, no attribution trailers in issue text or commits).
Files
| Path | Purpose |
scripts/render_report.py | The generator. Copy into the output directory per run. |
assets/findings.example.json | A complete worked example, all sections populated. |
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

