using-xskill

# Using xskill ## Overview xskill distills reusable **Skills** (`SKILL.md` folders) out of the real execution trajectories of coding agents (Claude Code, Codex, OpenCode, Cursor, …). A background daemon watches each agent's session logs, slices them into single-intent **atoms**, clusters atoms into skills, and writes/versions each skill in its own git folder. New skill versions only replace old ones when real traffic shows they serve users better (canary A/B by UX score) — not by an LLM grading itself. **Core mental model:** raw trajectory → atoms → candidate routing → SKILL.md → canary A/B → installed into every agent's skill dir. You operate the daemon; the daemon does the distilling. ## When to Use - Installing xskill or filling in `~/.xskill/config.yaml` (LLM + embedding endpoints) - Starting/keeping the daemon running (`xskill serve`), or backfilling old trajectories - Joining or hosting a team server (`xskill serve --server` / `xskill connect`) - Understanding the agent pipeline, atoms, canary/UX scoring, or deployment modes - **Re-distilling the whole skill library** (e.g. after switching to a stronger model) ## Quick Reference | Command | What it does | |---------|--------------| | `pip install xskill` | Install (Python 3.9+) | | `xskill serve` | Standalone daemon: FastAPI + watcher; first run writes `~/.xskill/config.yaml` then exits | | `xskill serve --server` | Team server: owns all LLM calls + git; prints a join token | | `xskill connect <host:port> --token...