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/opendatahub-io/ai-helpers/python-packaging-binary-auditnpx skills add opendatahub-io/ai-helpers --skill python-packaging-binary-auditgit clone --depth 1 https://github.com/opendatahub-io/ai-helpersWrote 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/opendatahub-io/ai-helpers/python-packaging-binary-audit)<a href="https://agentmods.dev/skills/opendatahub-io/ai-helpers/python-packaging-binary-audit"><img src="https://agentmods.dev/badge/skills/opendatahub-io/ai-helpers/python-packaging-binary-audit.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 | $0.00048 | $0.02342 |
| Opus 5 | $0.00024 | $0.01171 |
| Sonnet 5 | $0.00010 | $0.00468 |
| Haiku 4.5 | $0.00005 | $0.00234 |
Grade B, and why
python-packaging-binary-audit scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletemediumDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf -- "${STAGING_DIR}" Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Packaging Binary Audit
Scans a Python package repository for compiled or binary files using Fromager-style extension and magic-header detection, then runs malcontent YARA-based analysis on any detected binaries. Produces a self-contained "Binary Scan" report section with triaged findings and a risk assessment.
Inputs
- repo_path (required): Local filesystem path to an already-cloned repository
- output_file (optional): Write the report section to this file path instead of
returning it inline. The first line of the file must be
RISK_RATING:<value>so the orchestrator can parse it without reading the full report. - binary_scan (optional): File path to pre-computed binary scanner JSON output
(
binary-scan.json). Schema:{total, findings[{path, match_type, suffix, size, magic?}]}. When provided, skip runningscan_binaries.py. - malcontent_results (optional): File path to pre-computed malcontent JSON
output (
malcontent-results.json). Schema:{status, Files?}. Status values:"success","unavailable","timeout","failed","invalid","skipped". When provided, skip runningrun_malcontent.py.
Step 1: Obtain Binary Scan Results
Option A - Pre-computed results (CI mode)
If binary_scan is provided and the file exists, read and parse it as JSON.
Use the total and findings fields directly. Skip running scan_binaries.py
and skip creating the staging directory (STAGING_DIR) since binaries are already
analyzed. Each finding has: path (relative to repo root), match_type
("extension" or "magic_header"), suffix, size, and optionally magic
(ELF, MachO, ar_archive, etc.).
If total is 0, skip to the Output section and note "No binary files detected"
in the report.
Option B - Run binary scanner locally (standalone mode)
If binary_scan is not provided, run the binary scanner to find compiled files
using Fromager-style extension and magic-header detection:
STAGING_DIR=$(mktemp -d -t malcontent-staging-XXXXXX)
./scripts/scan_binaries.py --stage-to "$STAGING_DIR" "<repo-path>"
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago First seen · 208 lines · 48 tokens per session scan B 6ed74c7f7bbf
python-packaging-binary-audit is a skill published in the GitHub repository opendatahub-io/ai-helpers (37 stars, last pushed 3d ago), licensed Apache-2.0. It adds 48 tokens to every session and 2,342 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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