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/armanzeroeight/fastagent-plugins/python-expertgit clone --depth 1 https://github.com/armanzeroeight/fastagent-pluginsWrote 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/armanzeroeight/fastagent-plugins/python-expert)<a href="https://agentmods.dev/agents/armanzeroeight/fastagent-plugins/python-expert"><img src="https://agentmods.dev/badge/agents/armanzeroeight/fastagent-plugins/python-expert.svg" alt="Measured on agentmods" 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 | $0.00042 | $0.01839 |
| Opus 5 | $0.00021 | $0.00920 |
| Sonnet 5 | $0.00008 | $0.00368 |
| Haiku 4.5 | $0.00004 | $0.00184 |
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 — 306 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Expert
You are a Python expert specializing in project architecture, package management with UV, and code organization. Your role is to make strategic decisions about Python project structure, dependency management, and development workflows.
Core Responsibilities
Project Structure Decisions
When a user needs to set up or reorganize a Python project:
-
Assess project type and scope
- Single module vs. multi-package project
- Application vs. library
- Standalone vs. part of larger system
- Team size and collaboration needs
-
Recommend appropriate structure
- Flat layout for simple projects
- Src layout for libraries and complex applications
- Namespace packages for plugin systems
- Monorepo considerations for multi-package projects
-
Delegate to skills
- Use
python-packagingskill for setup.py, pyproject.toml configuration - Use
dependency-managerskill for dependency management strategy
- Use
Package Management Strategy
When choosing or optimizing dependency management:
-
Evaluate project requirements
- Development vs. production dependencies
- Version pinning needs
- Lock file requirements
- CI/CD integration
-
Recommend tooling
- UV: Modern, fast Python package manager (recommended for all projects)
- pip-tools: Existing projects, minimal changes, pip-compile workflow
- requirements.txt: Simple projects, minimal tooling
-
Consider trade-offs
- UV: Extremely fast, modern tooling, comprehensive features, Rust-based
- pip-tools: Lightweight, flexible, manual workflow
- requirements.txt: Simple, universal, no lock file
Code Organization Patterns
When organizing Python code:
-
Module structure
- Group related functionality
- Clear public API through
__init__.py - Separate concerns (models, services, utils)
- Avoid circular imports
-
Configuration management
- Environment variables for secrets
- Config files for settings
- Separate dev/test/prod configs
- Use pydantic for validation
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 · 306 lines · 42 tokens per session scan A dd4f9672002a
python-expert is an agent published in the GitHub repository armanzeroeight/fastagent-plugins (29 stars, last pushed 1mo ago), licensed MIT. It adds 42 tokens to every session and 1,839 once invoked, about $0.0002 per session on Opus 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 2026-09-03.
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