Skill router and context picker for Claude Code, Cursor, and Aider. Auto-picks the right skill for every prompt, with prompt-injection filtering for MCP.
Picky about what he loads into context, including what he refuses to load.
https://github.com/user-attachments/assets/8750946b-36be-4c48-bf73-79513451d1f5
PickySteve is a lightweight orchestration layer. A cheap model figures out which skill a request actually needs, retrieves that one skill, and hands a small, focused, untrusted-data-boundaried context bundle to a capable model. It does not dump every tool and document you own into context on every request.
This repo is Phase 1 (MVP), built to an architecture spec. Phase 2 work (tracing platform, standing eval harness, credential vault, sandbox) is not built yet. Each piece gets added only when a real Phase 1 failure justifies it.
Bring your own model. python -m pickysteve.setup asks which model to use and saves it. Runs on anything that speaks the OpenAI-compatible API: local Ollama (offline, no key), OpenAI, Claude, Gemini, Llama, etc. via OpenRouter / LiteLLM / their native compat endpoints. The published benchmarks were measured on local qwen3:8b; a different model just needs a eval/calibrate.py re-run.
Note: this is currently a uv / git clone install. There is no PyPI package yet, so uvx pickysteve and pipx install pickysteve do not exist. If that changes, this section gets a one-liner. For now, the fastest path to a real coding agent is the connector installer below.
Supports Claude Code, Codex, Cursor, Windsurf, Cline, Roo Code, Gemini CLI, Qwen Code, Goose, OpenHands, GitHub Copilot, Kimi Code, OpenCode, ZeroClaw via MCP stdio, and Aider, Hermes, OpenClaw, NanoClaw via an OpenAI-compatible proxy on :8077/v1.
From the project README.
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