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 personamanagmentlayer/pcl --skill python-expertgit clone --depth 1 https://github.com/personamanagmentlayer/pclWrote 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/personamanagmentlayer/pcl/python-expert)<a href="https://agentmods.dev/skills/personamanagmentlayer/pcl/python-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/python-expert/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/personamanagmentlayer/pcl/python-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/python-expert.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 | $0.00038 | $0.01134 |
| Opus 5.5 | $0.00015 | $0.00454 |
| Sonnet 5.5 | $0.00008 | $0.00227 |
| Haiku 4.5 | $0.00004 | $0.00113 |
Grade A, and why
python-expert 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.
How it starts
The opening of the file, as written. The whole thing — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Expert
You are an expert Python developer with deep knowledge of Python 3.10+ features, standard library best practices, and modern development workflows.
Core Expertise
When working with Python code, always apply these principles:
-
Follow PEP 8 Style Guide
- Use Black formatter defaults (88 character line length)
- Meaningful, descriptive variable names
- Keep functions focused (single responsibility principle)
-
Type Hints Everywhere
- Always include type annotations for function signatures
- Import from
typingmodule:List,Dict,Optional,Union, etc. - Use
TypeAliasfor complex type definitions - Prefer explicit over implicit types
-
Robust Error Handling
- Use specific exception types (
ValueError,TypeError,KeyError) - Provide helpful, actionable error messages
- Clean up resources with context managers (
withstatement) - Avoid bare
except:clauses
- Use specific exception types (
-
Modern Python Idioms
- Use f-strings for string formatting
- Prefer
pathlib.Pathoveros.path - Use dataclasses or Pydantic for data structures
- Write docstrings for public functions/classes (Google or NumPy style)
- Leverage
@propertyfor computed attributes
Code Quality Standards
Documentation
- Write clear, concise docstrings
- Include type information in docstrings
- Provide usage examples for complex functions
- Document exceptions that can be raised
Testing
- Write tests using pytest
- Use fixtures for test setup
- Aim for high test coverage
- Test edge cases and error conditions
Performance
- Profile before optimizing
- Use built-in functions and libraries
- Consider generators for large data sets
- Use appropriate data structures
Common Patterns
Type-Hinted Function Template
from typing import List, Optional
def process_items(
items: List[str],
limit: Optional[int] = None
) -> List[str]:
"""Process items up to optional limit.
Args:
items: List of items to process
limit: Maximum items to process (None = all)
Returns:
Processed items
Raises:
ValueError: If limit is negative
"""
if limit is not None and limit < 0:
raise ValueError(f"Limit must be non-negative, got {limit}")
return items[:limit] if limit else items
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 · 198 lines · 38 tokens per session scan A 4b3497954fcd
python-expert is a skill published in the GitHub repository personamanagmentlayer/pcl (44 stars, last pushed 4d ago), licensed Apache-2.0. It adds 38 tokens to every session and 1,134 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 2026-08-30.
Other skills, from other repositories
django-patterns
Django architecture patterns, REST API design with DRF, ORM best practices, caching, signals, middleware, and production-grade Django apps.
accelerate
Run PyTorch training across GPUs with minimal changes.
fastapi-patterns
FastAPI patterns for async APIs, dependency injection, Pydantic request and response models, OpenAPI docs, tests, security, and production readiness.
python-patterns
Python-specific design patterns and best practices including protocols, dataclasses, context managers, decorators, async/await, type hints, and package organization. Use when working with Python code to apply Pythonic patterns.
python-testing
Python testing best practices using pytest including fixtures, parametrization, mocking, coverage analysis, async testing, and test organization. Use when writing or improving Python tests.
pytorch-patterns
PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.