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 agentmods add rules/maccman/agent-playground/pythongit clone --depth 1 https://github.com/maccman/agent-playgroundWhat 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 | $0.01325 | $0.01325 |
| Opus 5 | $0.00662 | $0.00662 |
| Sonnet 5 | $0.00265 | $0.00265 |
| Haiku 4.5 | $0.00133 | $0.00133 |
Grade A, and why
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 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.
This is a copy
100% identical to python — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python
Python is available under the python3 command, but use uv for all Python project management.
Project Setup with UV:
- Create projects:
uv init project-namefor new projects - Virtual environments:
uv venv(80x faster than python -m venv) - Install packages:
uv add package-name(10-100x faster than pip) - Install from requirements:
uv pip install -r requirements.txt - Run scripts:
uv run script.py(automatically manages dependencies) - Python versions:
uv python install 3.11to install specific versions
Environment & Dependencies:
- Env vars should be loaded from the
.envfile - UV automatically creates and manages virtual environments
- Use
uv syncto install all project dependencies from lockfile - Use
uv lockto generate platform-independent lockfiles
Suggested packages:
- PDF extraction: `PdfReader`
- Data analysis: `pandas`
- Peeking or data analysis of CSVs: `pandas`
- Data visualization: `matplotlib` with the `SVG` backend
- OCR: OCR is currently not supported.
- Table/markdown formatting: `tabulate`
- Excel: `openpyxl`
- HTML parsing: `html5lib`
Plotting:
- Use matplotlib unless the user requests seaborn or something else.
- Default to using the `SVG` backend.
- Don't style charts with specific colors unless requested.
- Each plot should be in its own figure (no subplots unless asked).
- Label axes, set a clear title, and format values for readability (e.g. ".0%" for percentages).
- Use tight_layout() to avoid clipped labels.
- Generate big charts, they can be scaled down later.
Working with pandas & counts / aggregations:
- `value_counts()` and `groupby(...).size()` return a Series.
If you need a DataFrame with a named column of counts, convert with
`.`reset_index(name="count")` (or another explicit name). - Do not try to access a `'count'` column on the raw Series that comes back from `value_counts()`—create the column first as shown above.
- When dividing or comparing aggregated results, make sure the Series / DataFrames share the same index (use `.`align()`), or merge first: `df_a.merge(df_b, left_on=..., right_on=...)`.
Best Practices:
- Use assert statements for sanity checks when appropriate.
- Use context managers (with blocks) for file handling.
- Catch exceptions narrowly (except ValueError, not a blanket except).
Avoid:
- Wildcard imports (e.g., from x import *)
- Overuse of lambda — define named functions if reused or non-trivial.
- Nested list comprehensions (use loops for clarity)
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 First seen · 132 lines · 1,325 tokens per session scan A 76d96912a784
python is a cursor rule published in the GitHub repository maccman/agent-playground (2 stars, last pushed 6mo ago), licensed MIT. It adds 1,325 tokens to every session, about $0.0066 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to python, differing in 0 lines, and is treated as a copy.
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