Borrowing it
Nothing to install: this file belongs to brovar/10x-pentest. 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/brovar/10x-pentest/main/.github/skills/pt-report/SKILL.mdgit clone --depth 1 https://github.com/brovar/10x-pentestWrote 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/brovar/10x-pentest/pt-report)<a href="https://agentmods.dev/skills/brovar/10x-pentest/pt-report"><img src="https://agentmods.dev/badge/skills/brovar/10x-pentest/pt-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/brovar/10x-pentest/pt-report"><img src="https://agentmods.dev/badge/skills/brovar/10x-pentest/pt-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 | $0.00114 | $0.01213 |
| Opus 5 | $0.00057 | $0.00607 |
| Sonnet 5 | $0.00023 | $0.00243 |
| Haiku 4.5 | $0.00011 | $0.00121 |
Grade A, and why
pt-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 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/pt-report — Verified Report + Exports (Stage 5)
Aggregate the engagement into a client-ready report/final-report.md, a coverage-matrix.md computed
from the ledger, and machine-readable exports (SARIF 2.1.0 + JSON + CSV), per
foundation/report-template.md. The report is deterministic: a rendering of the on-disk
artifacts, never a source of new claims. It runs only after /pt-pentest-review passes.
When to use, when to skip
- Use once
/pt-pentest-reviewreturnspass/waived, to produce the deliverables. - Skip never — but the skill self-refuses if the gate hasn't passed. Re-run to regenerate after fixes.
Initial Response
- With
<engagement-id>: proceed to Step 0. - Without: print
Usage: /pt-report <engagement-id>.and STOP.
Inputs
review/findings-review.md— [blocking gate] — must beverdict: pass/waived(§1.1), else refuse.findings/*.md— [blocking] (1:1 with reported findings).scope.md+target-profile.md+threat-model.md+test-objectives.md— [blocking context].- Stage reports — [optional].
report-template.md+ CVSS rubric — [optional reference].
Process
Step 0: Gate + validate + resume
Gate first: if review/findings-review.md verdict ∉ {pass, waived} (or missing/unparseable),
refuse. Resume: re-running regenerates the report deterministically (overwrite).
Step 1: Assemble (report-template §3)
Render, from the artifacts only: executive summary (counts by severity × confidence tier + coverage
caveat), scope & authorization, methodology (standards + the human-executed dynamic model), target
overview, findings (ordered severity → confidence, grouped by tier, each rendered from its file),
coverage, risk overview, remediation summary, appendices. No entry without a backing finding file;
scores come from the findings; false-positive excluded from counts (appendix-listed).
Step 2: Coverage matrix + machine exports
coverage-matrix.md — computed from the ledger (join ## Objectives × ## Coverage ledger on
tm_id; group by standard_refs / STRIDE / status; list excluded/unverifiable with reasons).
findings.sarif — SARIF 2.1.0, one reportingDescriptor per finding_class, each result carrying
confidence + level + CVSS + CWE + locations + evidence; default export verified + potential
(tier filtering is a flag); validate against the vendored OASIS 2.1.0 schema (populated + empty).
findings.json / .csv — flat exports.
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 · 89 lines · 0 tokens per session scan A 41b93729d559
pt-report is a skill published in the GitHub repository brovar/10x-pentest (3 stars, last pushed 27d ago), licensed Apache-2.0. It adds 114 tokens to every session and 1,213 once invoked, about $0.0006 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
orbit-notion
Open Orbit briefing skill — selected by the Orbit pipeline when Notion is the user's only connected connector, or when the user explicitly scopes their daily digest to Notion. Pulls the past 24 hours of document edits, comments, mentions, and database row changes from the user's authenticated Notion connection and…
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
baoyu-youtube-transcript
A tool for downloading the written captions, subtitles, chapter information, speaker labels, and cover image from a YouTube video using its URL or ID.
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
read
Reads URLs and PDFs by fetching source content, defaulting to concise summaries for plain read requests and clean Markdown when asked to convert, save, quote, cite, or feed downstream work. Use when users ask in any language to read, fetch, check, summarize, quote, cite, convert, or save a URL or PDF. Not for local…
overleaf-sync
A two-way connection between a local paper folder and Overleaf, a web-based LaTeX editor for writing research papers. It lets you move changes between the local files and the shared Overleaf project.