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-static-auditnpx skills add opendatahub-io/ai-helpers --skill python-packaging-static-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-static-audit)<a href="https://agentmods.dev/skills/opendatahub-io/ai-helpers/python-packaging-static-audit"><img src="https://agentmods.dev/badge/skills/opendatahub-io/ai-helpers/python-packaging-static-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 | $0.00041 | $0.01537 |
| Opus 5 | $0.00020 | $0.00768 |
| Sonnet 5 | $0.00008 | $0.00307 |
| Haiku 4.5 | $0.00004 | $0.00154 |
Grade A, and why
python-packaging-static-audit 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 2d 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 — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Packaging Static Audit
Runs hexora static analysis on a Python package repository to detect suspicious code patterns such as code execution, encoding/serialization, and native code usage. Produces a self-contained "Hexora Static Analysis" 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. - hexora_results (optional): File path to pre-computed hexora JSON output
(
hexora-results.json). Schema:{status, total, findings[]}. When provided, skip runningrun-hexora.shand parse this file directly for triage. Status values:"success","unavailable","timeout","error".
Step 1: Obtain Hexora Results
Option A - Pre-computed results (CI mode)
If hexora_results is provided and the file exists, read and parse it as JSON:
- If
statusis"success": use thefindingsarray as the hexora output. Each entry is a raw hexora JSONL object (rule, severity, file, line, etc.). Rules HX1000, HX1020, HX1030, HX3010, HX3040, HX5000-HX5050, HX6020-HX6060, HX7000-HX7020 are already excluded by the CI runner; minimum confidence ismedium. Proceed directly to Step 2 (Triage). - If
statusis"unavailable","timeout", or"error": skip to the Output section and produce the report withrisk_rating = needs_review, noting the degraded status and anyerrormessage from the JSON. Do not re-run hexora. The CI runner already attempted execution under controlled conditions, and re-running in the agent container would likely hit the same failure. - For any other
statusvalue not listed above: treat it as an error and reportrisk_rating = needs_review, noting the unrecognized status value.
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.
- 2d ago First seen · 141 lines · 41 tokens per session scan A c41905d6bd1e
python-packaging-static-audit is a skill published in the GitHub repository opendatahub-io/ai-helpers (37 stars, last pushed yesterday), licensed Apache-2.0. It adds 41 tokens to every session and 1,537 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 2026-09-03.
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