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 pep8-code-style-compliance-verificationgit 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/pep8-code-style-compliance-verification)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/pep8-code-style-compliance-verification"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/pep8-code-style-compliance-verification/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/pep8-code-style-compliance-verification"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/pep8-code-style-compliance-verification.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 | $0.00035 | $0.01523 |
| Opus 5 | $0.00017 | $0.00762 |
| Sonnet 5 | $0.00007 | $0.00305 |
| Haiku 4.5 | $0.00003 | $0.00152 |
Grade A, and why
pep8-code-style-compliance-verification 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 4d 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PEP8 code style compliance verification
Summary
Automated verification that Python code adheres to PEP 8 style conventions using linting tools (flake8) and optional code formatters (black, autopep8). This skill ensures consistent code style across a scientific Python package, which improves maintainability and code review efficiency.
When to use
Apply this skill when developing or reviewing Python code for a scientific package (e.g., cooltools) that targets collaborative development with multiple contributors. Use it as part of continuous integration (CI) workflows or before submitting pull requests to enforce uniform style and catch formatting issues automatically rather than during manual code review.
When NOT to use
- Input is legacy Python code without existing style baseline and refactoring is out of scope—linting will flag numerous violations that may be impractical to fix retroactively without dedicated effort.
- Project uses a custom or non-standard Python style guide incompatible with PEP 8—flake8 enforces PEP 8 conventions and cannot be easily configured to override them.
- Style compliance is not a project priority and code review does not enforce uniform formatting—applying this skill without team buy-in will create friction without benefit.
Inputs
- Python source files (.py)
- Python package repository with setup.py or pyproject.toml
- Pytest configuration file (pytest.ini or pyproject.toml)
Outputs
- Flake8 linting report (stdout/log with line-by-line violations or clean status)
- Formatted Python source files (if using black or autopep8 with in-place option)
- CI/CD test pass/fail status based on style compliance
How to apply
Integrate flake8 linting into your pytest-based testing framework using the pytest-flake8 extension to check code style alongside unit tests. Run flake8 from the repository root to identify style violations against PEP 8 standards. Optionally use black or autopep8 as an automated code formatter to repair formatting issues; black is recommended for deterministic, uncompromising formatting. Execute the linter as part of the standard test suite (e.g., pytest) so that style violations fail the build. Configure project-level rules in pyproject.toml or setup.cfg to specify which violations to enforce. Evaluate success by confirming zero style warnings in CI logs and that all code diffs show consistent formatting across the codebase.
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.
- 4d ago First seen · 103 lines · 35 tokens per session scan A 9b431b690c4e
pep8-code-style-compliance-verification is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed 5d ago), licensed Apache-2.0. It adds 35 tokens to every session and 1,523 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 2026-09-06.
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