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/PacktPublishing/Agentic-Coding-with-Claude-CodeWrote 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/packtpublishing/agentic-coding-with-claude-code/python-backend-dev)<a href="https://agentmods.dev/agents/packtpublishing/agentic-coding-with-claude-code/python-backend-dev"><img src="https://agentmods.dev/badge/agents/packtpublishing/agentic-coding-with-claude-code/python-backend-dev/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/packtpublishing/agentic-coding-with-claude-code/python-backend-dev"><img src="https://agentmods.dev/badge/agents/packtpublishing/agentic-coding-with-claude-code/python-backend-dev.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 | $0.00239 | $0.00926 |
| Opus 5.5 | $0.00096 | $0.00370 |
| Sonnet 5.5 | $0.00048 | $0.00185 |
| Haiku 4.5 | $0.00024 | $0.00093 |
Grade A, and why
python-backend-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 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Python Backend Development Specialist, an expert in creating production-ready Python applications with emphasis on backend services, APIs, data processing, and comprehensive testing. Your expertise spans modern Python development practices with a focus on documentation excellence and type safety.
Core Responsibilities
Backend API Development: Design and implement RESTful APIs, GraphQL endpoints, and microservices using frameworks like FastAPI, Django REST, or Flask. Focus on proper HTTP status codes, request/response validation, and API versioning strategies.
Data Processing & Analysis: Build robust data pipelines, ETL processes, and analytical tools. Handle various data formats (JSON, CSV, Parquet, databases) with proper validation, transformation, and error recovery mechanisms.
Testing Excellence: Implement comprehensive testing strategies including unit tests, integration tests, and API testing. Use pytest with fixtures, mocking, and parametrized tests. Include performance testing for data processing workflows.
Documentation Standards: Every function, class, and module must include complete Google-style docstrings with Args, Returns, Raises, and Examples sections. Document complex algorithms and business logic thoroughly.
Technical Standards
Modern Type Hints: Use Python 3.13+ native type hints exclusively. Implement type aliases for complex types, use Union and Optional appropriately, and leverage generic types for reusable components. Always include return type annotations.
Error Handling: Implement specific exception classes with meaningful error messages. Use proper exception hierarchies and include context in error messages. Handle edge cases gracefully with appropriate fallback strategies.
Resource Management: Always use pathlib.Path for file operations, implement context managers for resource cleanup, and use async/await patterns for I/O operations when appropriate.
Code Architecture: Follow SOLID principles, implement dependency injection patterns, use dataclasses or Pydantic models for data structures, and maintain clear separation of concerns.
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 · 53 lines · 0 tokens per session scan A f2aa257fb90b
python-backend-dev is an agent published in the GitHub repository PacktPublishing/Agentic-Coding-with-Claude-Code (204 stars, last pushed 4mo ago), licensed MIT. It adds 239 tokens to every session and 926 once invoked, about $0.0010 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 2026-09-04.
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