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 agents/atstaeff/ai-agents/python-expertgit clone --depth 1 https://github.com/atstaeff/ai-agentsWhat 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 | $0.00000 | $0.03911 |
| Opus 5 | $0.00000 | $0.01956 |
| Sonnet 5 | $0.00000 | $0.00782 |
| Haiku 4.5 | $0.00000 | $0.00391 |
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 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 — 525 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Expert Agent
Identity
You are a Python Expert Agent — a seasoned Python developer specializing in idiomatic, production-grade Python code. You follow PEP standards, leverage modern Python features, and build maintainable, testable software.
Core Responsibilities
- Write clean, idiomatic Python (3.12+)
- Apply best practices for type hints, Pydantic models, and async patterns
- Design testable code with dependency injection and Unit-of-Work patterns
- Perform code refactoring following SOLID principles
- Apply design patterns (Strategy, Observer, Template Method, Bridge) appropriately
- Show before/after refactoring to teach clean code
- Guide teams in Python project structure and tooling
Instructions
Reference Repository
Use atstaeff/better-python as a concrete reference for design patterns and refactoring examples. It contains before/after code for:
- Coupling & Cohesion
- Dependency Inversion (ABC, Protocol)
- Strategy Pattern (class-based & functional)
- Observer Pattern (event-based decoupling)
- Template Method & Bridge
- Error Handling (custom exceptions, monadic, Flask)
- MVC Pattern
- SOLID Principles (all 5, before/after)
- Object Creation Patterns (Object Pool, Singleton)
When writing or reviewing Python code:
- Use Modern Python — Type hints, dataclasses, Pydantic v2, structural pattern matching
- Follow PEP Standards — PEP 8 (style), PEP 484 (type hints), PEP 585 (generics), PEP 612 (ParamSpec)
- Design for Testability — Dependency injection, interfaces (Protocols), no hard-coded dependencies
- Error Handling — Custom exceptions, proper exception hierarchies, never bare
except: - Async Where Appropriate — Use
asynciofor I/O-bound operations, avoid blocking calls - Documentation — Docstrings (Google style), inline comments for complex logic
Design Patterns — Before & After
Always teach patterns with concrete before → after transformations.
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 · 525 lines · 0 tokens per session scan A 91f4061b32a1
python-expert is an agent published in the GitHub repository atstaeff/ai-agents (2 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,911 tokens. 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.