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 skills add opendatahub-io/ai-helpers --skill python-packaging-bug-findergit 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-bug-finder)<a href="https://agentmods.dev/skills/opendatahub-io/ai-helpers/python-packaging-bug-finder"><img src="https://agentmods.dev/badge/skills/opendatahub-io/ai-helpers/python-packaging-bug-finder/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/opendatahub-io/ai-helpers/python-packaging-bug-finder"><img src="https://agentmods.dev/badge/skills/opendatahub-io/ai-helpers/python-packaging-bug-finder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 59 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00037 | $0.01553 |
| Opus 5.5 | $0.00015 | $0.00621 |
| Sonnet 5.5 | $0.00007 | $0.00311 |
| Haiku 4.5 | $0.00004 | $0.00155 |
Grade A, and why
python-packaging-bug-finder 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 1mo 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Packaging Bug Finder
Identifies known packaging and build issues for Python projects by searching GitHub repositories for relevant issues, analyzing their content and comments, and determining resolution status.
Instructions
When investigating packaging problems for a Python project, follow this workflow:
1. Find the Repository
Use the source finder skill to locate the project's GitHub repository:
Skill: python-packaging-source-finder
Args: <package_name>
If the skill returns a repository URL with high or medium confidence, proceed. If confidence is low or no URL found, stop here and return an error.
2. Search for Packaging Issues
Once you have the repository URL, search for packaging-related issues by:
-
Access the GitHub issues page: Use WebFetch to get the repository's issues page (typically
<repo_url>/issues) -
Filter for packaging keywords: Look for issues with titles containing:
- Build-related: "build", "compilation", "compile", "setup.py", "pyproject.toml", "cmake", "makefile"
- Installation: "install", "pip", "wheel", "package", "packaging", "distribution"
- Environment: "gcc", "clang", "msvc", "python", "version", "dependency"
- Platform: "windows", "linux", "macos", "arm64", "x86_64"
- Errors: "error", "fail", "broken", "issue"
-
Prioritize open issues: Focus on open issues first, then closed ones that might affect the target version
3. Analyze Each Relevant Issue
For each packaging-related issue found:
-
Fetch issue details: Use WebFetch to get the full issue page including:
- Issue description
- All comments
- Labels and milestones
- Current status (open/closed)
-
Extract key information:
- Problem description: What packaging/build problem is described?
- Affected versions: Which versions are mentioned as problematic?
- Resolution status: Is it fixed, pending, or unresolved?
- Available fixes: Are there PRs, commits, or workarounds mentioned?
- Version inclusion: If fixed, in which version was the fix included?
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.
- 1mo ago First seen · 161 lines · 37 tokens per session scan A d1a6e2cb0142
python-packaging-bug-finder is a skill published in the GitHub repository opendatahub-io/ai-helpers (38 stars, last pushed yesterday), licensed Apache-2.0. It adds 37 tokens to every session and 1,553 once invoked, about $0.0001 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 2026-09-03.
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