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 HolobiomicsLab/asb-skill-collections --skill python-dependency-version-resolutiongit clone --depth 1 https://github.com/HolobiomicsLab/asb-skill-collectionsWrote 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/holobiomicslab/asb-skill-collections/python-dependency-version-resolution)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/python-dependency-version-resolution"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/python-dependency-version-resolution/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/holobiomicslab/asb-skill-collections/python-dependency-version-resolution"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/python-dependency-version-resolution.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00068 | $0.02177 |
| Opus 5 | $0.00034 | $0.01089 |
| Sonnet 5 | $0.00014 | $0.00435 |
| Haiku 4.5 | $0.00007 | $0.00218 |
Grade A, and why
python-dependency-version-resolution 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 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.
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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Dependency Version Resolution
Summary
Resolve and verify Python library versions (pysam, bx-python, numpy, scipy) and their inter-dependencies within a conda environment to ensure compatibility with a bioinformatics pipeline's requirements. This skill is critical when constructing reproducible computational environments for tools like HiC-Pro that depend on specific minimum versions of scientific Python packages.
When to use
When setting up a new conda environment for a Python-based bioinformatics pipeline and you need to confirm that all declared dependencies (e.g., pysam >=0.15.4, bx-python >=0.8.8, numpy >=1.18.1, scipy >=1.4.1) are both installed and meet the minimum version thresholds, or when troubleshooting import failures or version-mismatch errors during pipeline initialization.
When NOT to use
- When working with a pre-built container (Docker, Singularity) where the environment is already sealed and dependencies are guaranteed; verification becomes redundant unless the container is known to be damaged or incompletely built.
- When the pipeline has already been run successfully in the current environment and you have no reason to suspect version drift or breakage.
- When dependency resolution is handled by a higher-level workflow orchestration system (e.g., Snakemake, CWL) that manages environment isolation automatically.
Inputs
- conda environment specification file (environment.yml)
- activated conda environment (from
conda activate) - Python interpreter (>3.7) within that environment
Outputs
- verified list of installed Python packages and their versions
- test report confirming all imports succeed
- summary document recording resolved paths and versions
How to apply
After activating a conda environment created from an environment.yml file, test the importability and version of each Python library by executing import statements and version checks in an interactive Python session or test script. For HiC-Pro specifically, verify that Python is >3.7, then check that pysam (>=0.15.4), bx-python (>=0.8.8), numpy (>=1.18.1), and scipy (>=1.4.1) all import successfully and report versions meeting or exceeding the documented thresholds. If any import fails or a version is below the minimum, reinstall or upgrade that package via conda (preferred, as it resolves non-Python C/Fortran dependencies) or pip, then re-verify. Document the final resolved versions and paths in a summary report for reproducibility and troubleshooting.
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 · 117 lines · 68 tokens per session scan A f90a95ac52d9
python-dependency-version-resolution is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed 4d ago), licensed Apache-2.0. It adds 68 tokens to every session and 2,177 once invoked, about $0.0003 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 2026-09-06.
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