Skill
xskill-dev
Use when developing xskill itself — editing agent tool docstrings, changing the generate agent tool surface, or verifying what…
@skillnerdsInstall SkillAgent Memory
Skill
/using-xskill
Use when installing, configuring, or operating xskill (the `xskill` CLI / `pip install xskill`) — starting the daemon, registering trajectory dirs, joining a team server, understanding how trajectories become Skills, or rebuilding/re-distilling the skill library after a model
Install
$ npx -y skills add SkillNerds/xskill --skill using-xskill --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/using-xskill
Context preview
The summary Claude sees to decide when to auto-load this skill.
Use when installing, configuring, or operating xskill (the `xskill` CLI / `pip install xskill`) — starting the daemon, registering trajectory dirs, joining a team server, understanding how trajectories become Skills, or rebuilding/re-distilling the skill library after a model
SKILL.md
using-xskill.SKILL.mdname: using-xskill
description: Use when installing, configuring, or operating xskill (the `xskill` CLI / `pip install xskill`) — starting the daemon, registering trajectory dirs, joining a team server, understanding how trajectories become Skills, or rebuilding/re-distilling the skill library after a model change.
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 <t>` | Join a team server as a thin client | | `xskill registry add <path>` | Backfill / watch an extra trajectory directory | | `xskill traj search <query>` / `xskill search <query>` | Search trajectories or skills | | `xskill read <path> --eco <eco>` | Batch-ingest db trajectories (ngagent/opencode) | | `xskill rebuild [--force]` | Re-distill from existing raw trajectories (see reference) | | `xskill stats` | Token usage & estimated cost |
The daemon is the engine: most commands only change state in the DB; nothing is distilled unless `xskill serve` (or the team server) is running.
Progressive Disclosure — read on demand
- **Install & configure** (config.yaml fields, per-agent collect/install paths, team
client setup): `references/installation.md`
- **How it works** (TaskAgent → TaskClusterAgent → SkillEditAgent, atoms, canary/UX
scoring, standalone vs team mode): `references/mechanisms.md`
- **Rebuild the skill library** (a ready-to-run prompt that walks a model through
re-distilling correctly): `references/rebuilding-skill-library.md`
Common Mistakes
- **Running `rebuild` with no daemon up.** `rebuild` only resets DB state; the watcher
in `serve` does the actual re-split/re-cluster every 30s. No daemon = nothing happens.
- **Deleting raw `~/.xskill/*_sessions/*.md`.** Those are the *input* to distillation —
delete them and you can no longer rebuild.
- **Expecting DeepSeek to do embeddings.** DeepSeek has no embedding endpoint; point the
`embedding:` block at DashScope / OpenAI / Ollama.
- **Putting tokens in public places.** Team join tokens must never land in a public repo
or chat log.
Read more
name: using-xskill description: Use when installing, configuring, or operating xskill (the `xskill` CLI / `pip install xskill`) — starting the daemon, registering trajectory dirs, joining a team server, understanding how trajectories become Skills, or rebuilding/re-distilling the skill library after a model change.
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 <t>` | Join a team server as a thin client | | `xskill registry add <path>` | Backfill / watch an extra trajectory directory | | `xskill traj search <query>` / `xskill search <query>` | Search trajectories or skills | | `xskill read <path> --eco <eco>` | Batch-ingest db trajectories (ngagent/opencode) | | `xskill rebuild [--force]` | Re-distill from existing raw trajectories (see reference) | | `xskill stats` | Token usage & estimated cost |
The daemon is the engine: most commands only change state in the DB; nothing is distilled unless `xskill serve` (or the team server) is running.
Progressive Disclosure — read on demand
- **Install & configure** (config.yaml fields, per-agent collect/install paths, team
client setup): `references/installation.md`
- **How it works** (TaskAgent → TaskClusterAgent → SkillEditAgent, atoms, canary/UX
scoring, standalone vs team mode): `references/mechanisms.md`
- **Rebuild the skill library** (a ready-to-run prompt that walks a model through
re-distilling correctly): `references/rebuilding-skill-library.md`
Common Mistakes
- **Running `rebuild` with no daemon up.** `rebuild` only resets DB state; the watcher
in `serve` does the actual re-split/re-cluster every 30s. No daemon = nothing happens.
- **Deleting raw `~/.xskill/*_sessions/*.md`.** Those are the *input* to distillation —
delete them and you can no longer rebuild.
- **Expecting DeepSeek to do embeddings.** DeepSeek has no embedding endpoint; point the
`embedding:` block at DashScope / OpenAI / Ollama.
- **Putting tokens in public places.** Team join tokens must never land in a public repo
or chat log.
Other skills on xskill.
@skillnerdsInstall Skill