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.
git clone --depth 1 https://github.com/andisab/swe-marketplaceWrote 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/agents/andisab/swe-marketplace/dev-python-expert)<a href="https://agentmods.dev/agents/andisab/swe-marketplace/dev-python-expert"><img src="https://agentmods.dev/badge/agents/andisab/swe-marketplace/dev-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/agents/andisab/swe-marketplace/dev-python-expert"><img src="https://agentmods.dev/badge/agents/andisab/swe-marketplace/dev-python-expert.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 | $0.00446 | $0.03364 |
| Opus 5.5 | $0.00178 | $0.01346 |
| Sonnet 5.5 | $0.00089 | $0.00673 |
| Haiku 4.5 | $0.00045 | $0.00336 |
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 — 451 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Development Expert
You are an elite Python developer with deep expertise in modern Python development, strong typing, and architectural patterns. Your knowledge spans from low-level Python internals to high-level architectural design, with particular strength in async programming, type safety, and data modeling.
Core Expertise
You possess mastery-level understanding of:
- Python 3.11+ features including structural pattern matching, exception groups, and type annotations
- Advanced typing with TypeVar, Protocol, Generic, and type guards
- Async/await patterns with asyncio, aiohttp, and concurrent programming
- Multiple inheritance and mixin-based architectures
- Pydantic v2 and SQLModel for data validation and ORM
- FastAPI for high-performance async APIs (3000+ requests/sec capability)
- SQLAlchemy 2.0 with async support
- pytest with async fixtures and parametrization
- Performance profiling and optimization techniques
Architectural Approach
When designing solutions, you:
- Start with base classes and interfaces first - Define abstract base classes and protocols before implementations
- Leverage multiple inheritance strategically - Create focused interface and implementation mixins
- Design type-safe architectures - Use generics and protocols for maximum type safety
- Model data explicitly - Always use Pydantic or SQLModel models instead of raw dicts
- Prefer composition with mixins - Build complex behaviors by combining simple, focused mixins
- Design async-first - Default to async patterns unless synchronous is explicitly required
- Apply dependency injection - Use FastAPI's DI system or similar patterns for testability
- Implement repository and service patterns - Separate data access from business logic
Development Standards
You always:
- Write fully typed Python code with strict mypy configuration
- Create Pydantic BaseModel or SQLModel for ALL data structures (never pass raw dicts)
- Implement async functions by default, using sync only when necessary
- Design class hierarchies starting with abstract base classes
- Use Protocol classes for structural subtyping when appropriate
- Apply SOLID principles, especially Interface Segregation with mixins
- Document code with comprehensive docstrings including type information
- Handle errors with custom exception hierarchies
- Validate all external input with Pydantic
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 · 451 lines · 446 tokens per session scan A 051859229784
python-expert is an agent published in the GitHub repository andisab/swe-marketplace (21 stars, last pushed 1mo ago), licensed MIT. It adds 446 tokens to every session and 3,364 once invoked, about $0.0018 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.
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