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 JSchOBL/agentic-ai-learning-journey --skill format-pythongit clone --depth 1 https://github.com/JSchOBL/agentic-ai-learning-journeyWrote 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/jschobl/agentic-ai-learning-journey/format-python)<a href="https://agentmods.dev/skills/jschobl/agentic-ai-learning-journey/format-python"><img src="https://agentmods.dev/badge/skills/jschobl/agentic-ai-learning-journey/format-python/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/jschobl/agentic-ai-learning-journey/format-python"><img src="https://agentmods.dev/badge/skills/jschobl/agentic-ai-learning-journey/format-python.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00041 | $0.00407 |
| Opus 5.5 | $0.00016 | $0.00163 |
| Sonnet 5.5 | $0.00008 | $0.00081 |
| Haiku 4.5 | $0.00004 | $0.00041 |
Grade A, and why
format-python 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.
What it actually says
Format Python Code
Format Python files to the project's style: Black with line length 88, isort using the Black profile.
Steps
-
Determine scope. If the user named files, use those. If they said "the project" or gave no scope, use
Globwith**/*.py, excluding.venv/,build/, anddist/. -
Check the tools exist before doing anything else:
black --version && isort --versionIf either is missing, tell the user to run
pip install black isortand stop. Do not format by hand-editing files — that defeats the purpose and produces inconsistent results. -
Sort imports first, then format. Order matters: Black reformats what isort produces, not the reverse.
isort <paths> --profile black --line-length 88 black <paths> --line-length 88 -
Report what changed. Black prints a summary; pass it through rather than paraphrasing it.
-
If the user asked about code quality rather than just formatting, add a lint pass:
ruff check <paths>Report findings. Do not auto-fix lint issues unless asked — formatting is mechanical and safe, lint fixes are semantic and are not.
Constraints
- Never format files outside the project directory.
- Never format a file with unstaged changes without telling the user first. Black rewrites in place and there is no undo.
- If Black fails on a file it is almost always a syntax error. Report the parse error rather than retrying.
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 · 45 lines · 41 tokens per session scan A 9589df475d24
format-python is a skill published in the GitHub repository JSchOBL/agentic-ai-learning-journey (3 stars, last pushed 2mo ago), licensed MIT. It adds 41 tokens to every session and 407 once invoked, about $0.0002 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 2026-08-31.
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