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 joao-aires/agent-skills-public --skill python-automation-engineeringgit clone --depth 1 https://github.com/joao-aires/agent-skills-publicWrote 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/joao-aires/agent-skills-public/python-automation-engineering)<a href="https://agentmods.dev/skills/joao-aires/agent-skills-public/python-automation-engineering"><img src="https://agentmods.dev/badge/skills/joao-aires/agent-skills-public/python-automation-engineering/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/joao-aires/agent-skills-public/python-automation-engineering"><img src="https://agentmods.dev/badge/skills/joao-aires/agent-skills-public/python-automation-engineering.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 | $0.00044 | $0.00904 |
| Opus 5.5 | $0.00018 | $0.00362 |
| Sonnet 5.5 | $0.00009 | $0.00181 |
| Haiku 4.5 | $0.00004 | $0.00090 |
Grade A, and why
python-automation-engineering 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 yesterday.
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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python automation engineering
Build a tool whose result remains understandable when no person watches it run. Establish what a complete result means, how the tool selects its targets, and what its caller should do after a partial failure. The command's exit status, structured output and human summary should agree about that state.
For API collection, track what was requested, what was actually visited and which part failed. An empty complete collection and an incomplete collection need different results. For mutations, separate the intended operation from individual attempts so a resumed run can identify completed work, recheck preconditions and avoid repeating effects. Automating a command is worthwhile when it reduces toil or improves consistency without concealing partial failure.
Modes and discovery
- Interactive/setup: identify the user's operational decision/problem, consumers, frequency, inputs/outputs, API privileges and success criteria; implement the simplest maintainable tool.
- Feature/maintenance: preserve CLI/output contracts, local project manager and runtime, improve the affected boundaries, tests and documentation.
- Recurring review: inspect changed code/dependencies and prior run evidence, identify reproducible correctness/usability gaps, prepare bounded fixes and fixtures, and maintain stable findings. Run against live systems only within the configured read/mutate policy.
Discover the nearest README/pyproject/lock/entrypoint in multi-tool repositories. Preserve its Python version and dependency manager unless migration is in scope. Consult the automation guide for deeper implementation details when needed.
Design the tool around its operational result
Define the intended value, target selection, source completeness, user/machine output, exit codes, mutation/recovery model and owner. Choose an interface that lets a person inspect the result and an unattended caller determine whether it can continue.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday Changed · +15 lines 413cb808781d
- 7d ago First seen · 41 lines · 44 tokens per session scan A 6a5417fa42c1
python-automation-engineering is a skill published in the GitHub repository joao-aires/agent-skills-public (2 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 904 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 2026-10-15.
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