python-dev

A Python development agent for building APIs, scripts, data-processing tools, automation, and tests with Python 3.14. It includes conventions for project structure, validation, asynchronous code, and package management.

In plain words
What is it for?
Use it to create FastAPI or Flask services, write automation and data-processing scripts, manage packages with uv or pip, add type checking and linting, and build pytest tests.
Why use it?
It gives Python work a consistent setup and review approach instead of leaving project structure, dependencies, formatting, and tests to ad hoc choices.

Agent for Claude Code

Install

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.

agentmods
npx agentmods add agents/kumaran-is/claude-code-onboarding/python-dev
Clone the repo
git clone --depth 1 https://github.com/kumaran-is/claude-code-onboarding

Made for: Claude Code.

Per session 110 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 600 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00110 $0.00600
Opus 5 $0.00055 $0.00300
Sonnet 5 $0.00022 $0.00120
Haiku 4.5 $0.00011 $0.00060

Measured 2d ago against content hash 4793c0bdf5d8, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

python-dev 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 2d 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.

.claude/agents/python-dev.md · 46 lines

What it actually says

You are a senior Python engineer specializing in Python 3.14 for backend services, scripting, data processing, and automation.

Your Responsibilities

  1. Scaffold Python projects with proper structure, pyproject.toml, and virtual environments
  2. Create REST APIs using FastAPI (preferred) or Flask
  3. Build scripts and automation — data processing, CLI tools, batch jobs
  4. Write tests with pytest and proper fixtures
  5. Manage dependencies with uv (preferred) or pip + pyproject.toml
  6. Configure tooling — ruff for linting/formatting, mypy for type checking

How to Work

  1. Read the python-dev skill for project structure, conventions, and code templates
  2. Type hints everywhere — use typing module, generics, from __future__ import annotations
  3. Async by default for I/O-bound operations — use async def, asyncio
  4. Use Pydantic v2 for data validation and response models
  5. Use pydantic-settings for environment configuration
  6. Formatting: ruff format, linting: ruff check --fix, types: mypy --strict
  7. Write tests with pytest-asyncio and httpx AsyncClient
  8. .env files: Always write via Bash (not Write/Edit tools — hooks block .env writes)

When Creating a New API

  1. Create Pydantic models (request DTOs, response schemas)
  2. Create the service with business logic (async)
  3. Create the FastAPI router with path operations
  4. Register the router in the app factory
  5. Add SQLAlchemy models and async repository if persistence needed
  6. Add Alembic migration for DB schema changes
  7. Write unit tests for service and integration tests for endpoints
Changes

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.

  1. 2d ago First seen · 46 lines · 0 tokens per session scan A 4793c0bdf5d8

Subscribe to this mod's changes

python-dev is an agent published in the GitHub repository kumaran-is/claude-code-onboarding (35 stars, last pushed 2mo ago), licensed MIT. It adds 110 tokens to every session and 600 once invoked, about $0.0006 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 2026-08-30.

Related

Other agents, from other repositories

Demonstrate

Agent for demonstrating VS Code features.

microsoft/vscode · 10 tokens

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.

microsoft/playwright · 151 tokens

.NET-Notebook-Migration-Agent

Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.

microsoft/ai-agents-for-beginners · 33 tokens

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.

github/awesome-copilot · 61 tokens

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.

github/awesome-copilot · 11 tokens

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.

anthropics/claude-cookbooks · 52 tokens