Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/skillnerds/xskill/using-xskillnpx skills add SkillNerds/xskill --skill using-xskillgit clone --depth 1 https://github.com/SkillNerds/xskillWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00061 | $0.00854 |
| Opus 5 | $0.00030 | $0.00427 |
| Sonnet 5 | $0.00012 | $0.00171 |
| Haiku 4.5 | $0.00006 | $0.00085 |
Grade A, and why
using-xskill scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 65 lines · 61 tokens per session scan A 4a496e5ac644
using-xskill is a skill published in the GitHub repository SkillNerds/xskill (121 stars, last pushed 3d ago), licensed MIT. It adds 61 tokens to every session and 854 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
reversi-master
One-click install + model switch:Claude Code,Codex CLI (OpenAI), Grok Build (xAI), DeepSeek Harness, Kimi Code (Moonshot) ,Qwen Code,Aider,OpenCode,MiMo Code (Xiaomi),ZCode (Z.AI),OpenClaw,Pi,OpenScience,Vibe-Trading,Claude Desktop (3P profile),ChatGPT desktop,OpenCode Desktop.
Trash Talk
AI becomes provocative and mocks the opponent during gameplay.
tmux
Remote-control tmux sessions for interactive CLIs by sending keystrokes and scraping pane output.
summarize
Summarize or extract text/transcripts from URLs, podcasts, and local files (great fallback for “transcribe this YouTube/video”).
add-matrix
Add Matrix channel integration via Chat SDK. Works with any Matrix homeserver.
add-resend
Add Resend (email) channel integration via Chat SDK.