Review Python and FastAPI code with linters and specialized subagents.
npx skills add https://github.com/arthrod/conejo-skills --skill review-python-arthrod
Please help me install this Agent Skill. Skill: review-python Source: https://github.com/arthrod/conejo-skills/tree/main/skills/review-python Command: npx skills add https://github.com/arthrod/conejo-skills --skill review-python-arthrod
This skill addresses the inconsistency and oversight common in manual code reviews by providing a structured, multi-agent verification process that enforces project-specific coding doctrines and framework best practices.
Run the review-python skill on the current directory to perform a comprehensive code review of all recent changes.
High-intent search queries and answers about installing and using this skill.
Automating Python and FastAPI code reviews involves orchestrating specialized subagents to verify logic, type safety, and framework-specific patterns. This skill enforces red-green TDD and architectural doctrines by dispatching domain-specific verification tasks across multiple technology stacks.
Automated code review detects technologies like Pydantic-AI, SQLAlchemy, or Postgres and spawns domain-specific subagents for deep analysis. This multi-agent verification process enforces project-specific coding doctrines and framework best practices during pull request workflows.
Yes, automated Python code reviews require integration with project-level linters like Ruff and Mypy. These tools ensure style and type safety are verified before the multi-agent system performs deeper human-level logic review on your FastAPI changes.
The best way to enforce red-green TDD principles is using a structured, multi-agent verification process that targets pull request workflows. This approach automatically verifies adherence to testing doctrines and architectural standards without missing subtle logic errors.
Yes, you can run this automated code review skill on the current directory to perform a comprehensive analysis of all recent changes. It orchestrates specialized subagents to evaluate logic, type safety, and framework-specific patterns.
The multi-agent approach for Python static analysis focuses on pull request workflows and requires project-level linters to be integrated. It relies on detecting specific technologies like SQLAlchemy or Postgres to dispatch domain-specific verification tasks effectively.
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