Kilo Python Expert Suite: Advanced Doctor & Skillset

Inspiration

As a Python developer with over 10 years of experience, I’ve seen how AI agents can struggle with the nuances of production-grade code—specifically type safety, linting, and codebase hygiene. I wanted to transform Kilo from a "code writer" into a "Staff Engineer" that autonomously verifies, polishes, and maintains high standards without human intervention.

What it does

I contributed a tripartite enhancement suite to the Kilo Marketplace to create a self-correcting development loop:

1. Advanced Python Doctor Mode: A specialized agent persona that enforces a "Zero-Error" policy. It leverages ruff and mypy to fix its own linting and type errors autonomously before presenting code to the user.
2. Dead Code Cleaner Skill: A high-utility skill that identifies and removes unused imports, variables, and unreachable code, keeping projects lean.
3. Automated Documentation Skill: A tool that ensures every new function and class meets strict docstring requirements (Google or NumPy style). ## How we built it
4. Architecture: I designed the mode logic to utilize a "Self-Correction Loop," where the agent interprets tool output (stderr/stdout) to iterate on its own code.
5. Speed: I integrated uv as the primary execution engine to ensure the "Doctor" diagnostics run in milliseconds, aligning with Kilo's core value of speed.
6. Protocol: Used the Kilo marketplace schema to define custom YAML modes and skill definitions that interface directly with the local shell. ## Challenges we ran into The primary challenge was fine-tuning the agent's autonomy—ensuring it could resolve complex mypy type conflicts without getting stuck in a loop. I solved this by providing clearer system prompts on how to prioritize type-narrowing over simple "any" casts. ## Accomplishments that we're proud of Submitting three distinct, production-ready Pull Requests within 48 hours that directly solve common friction points in the Python development lifecycle. ## What we learned Integrating autonomous agents with strict static analysis tools (like mypy and ruff) requires a delicate balance of persona instructions. I learned that for an agent to truly be "Senior," it must be taught to interpret error logs as actionable feedback rather than just failure states. This hackathon was a great playground to test the Model Context Protocol (MCP) principles in a fast-paced, real-world scenario. ## What's next for Kilo Python Expert Suite: Advanced Doctor & Skillset Integration with uv more deeply: Further optimizing the toolchain to use cached virtual environments for near-instantaneous verification.

Complex Refactoring Modes: Adding a "Design Pattern" mode that can autonomously suggest architectural improvements (like switching to Dependency Injection or Factory patterns) while maintaining the "Doctor" safety checks.

Expanded Marketplace: Encouraging the community to build specialized "Doctors" for other ecosystems like Go or Zig.

Built With

• kilo
• mypy
• python
• ruff
• shell-scripting
• uv
• yaml