Borrowing it
Nothing to install: this file belongs to roryeckel/wyoming_openai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/roryeckel/wyoming_openai/main/.agents/skills/lint/SKILL.mdgit clone --depth 1 https://github.com/roryeckel/wyoming_openaiWrote 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/roryeckel/wyoming_openai/lint)<a href="https://agentmods.dev/skills/roryeckel/wyoming_openai/lint"><img src="https://agentmods.dev/badge/skills/roryeckel/wyoming_openai/lint/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/roryeckel/wyoming_openai/lint"><img src="https://agentmods.dev/badge/skills/roryeckel/wyoming_openai/lint.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 | $0.00025 | $0.00721 |
| Opus 5 | $0.00013 | $0.00360 |
| Sonnet 5 | $0.00005 | $0.00144 |
| Haiku 4.5 | $0.00003 | $0.00072 |
Grade A, and why
lint 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Current State
- Branch: !
git branch --show-current - Dirty files: !
git diff --name-only
Run all project linters and resolve every issue. Loop until the codebase passes cleanly.
Python environment
Use the repository's selected or activated virtual environment for every linter invocation. Do
not assume a particular environment name or location. Resolve the interpreter from the active
environment (for example, with python -c "import sys; print(sys.executable)") and refer to it
below as <venv-python>.
Run Ruff and Pyright as modules through <venv-python> rather than using system-level commands.
Pass the same interpreter to Pyright with --pythonpath so its import resolution uses the
environment containing the project's dependencies. If no project environment is selected, stop
and report that linting requires the development environment to be activated or selected.
Linter pipeline
Run the linters in this order each iteration:
- Ruff auto-fix —
<venv-python> -m ruff check . --fix(safe, auto-fixable issues) - Ruff check —
<venv-python> -m ruff check .(remaining issues that need manual fixes) - Pyright —
<venv-python> -m pyright --pythonpath <venv-python>(type-checking)
Resolution loop
while linters report errors:
1. Run <venv-python> -m ruff check . --fix (let ruff auto-fix what it can)
2. Run <venv-python> -m ruff check .
- If errors remain, read the offending files and fix them
3. Run <venv-python> -m pyright --pythonpath <venv-python>
- If errors remain, read the offending files and fix them
4. If no errors in steps 2 and 3, break
Rules
- Max 5 iterations. If issues persist after 5 loops, stop and report the remaining errors to the user.
- Ruff first, pyright second. Ruff fixes (especially import sorting) can eliminate pyright false positives, so always run ruff before pyright.
- Use
ruff check . --fixbefore manual fixes. Never manually fix what ruff can auto-fix. - Minimal changes. Only touch lines the linters flag. Do not refactor, add docstrings, or "improve" surrounding code.
- Preserve behavior. Fixes must not change runtime behavior. If a fix requires a judgment call (e.g., an
Anytype that needs a real annotation), prefer the narrowest correct type. - Report clearly. After each iteration, briefly state which linter you ran and how many errors remain. When done, summarize what was fixed.
- If
$ARGUMENTScontains a file path or glob, pass it to both linters to scope the run (e.g.,<venv-python> -m ruff check $ARGUMENTSand<venv-python> -m pyright --pythonpath <venv-python> $ARGUMENTS).
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 Changed · +12 lines 74b320dbc4a6
- 10d ago First seen · 42 lines · 25 tokens per session scan A 9382221986b3
lint is a skill published in the GitHub repository roryeckel/wyoming_openai (209 stars, last pushed 2d ago), licensed Apache-2.0. It adds 25 tokens to every session and 721 once invoked, about $0.0001 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 2026-08-30.
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