How a Skill Evolves

N users, one Skill, continuous evolution. Every conversation compounds. Every user contributes.

News

• 2026/04/14 — WeChat discussion group is live! Join the group to chat with us.
• 2026/04/14 — Seamless integration with Hermes is now available.
• 2026/04/12 — Active discussion with Deer-Flow on cross-framework skill sharing.
• 2026/04/11 — SkillClaw ranked #2 Paper of the Day on Hugging Face Daily Papers!
• 2026/04/10 — SkillClaw is now open source! Code released on GitHub.

Overview

SkillClaw makes LLM agents progressively better by evolving reusable skills from real session data and sharing them across a group of agents.

The system has two components:

1. Client Proxy — A local API proxy (/v1/chat/completions, /v1/messages) that intercepts agent requests, records session artifacts, and syncs skills with shared storage.

2. Evolve Server (evolve_server) — A single evolve service that reads session data from shared storage, evolves or creates skills, and writes them back. It supports two engines:

   • workflow: fixed 3-stage LLM pipeline (Summarize → Aggregate → Execute)
   • agent: OpenClaw-driven agent workspace with direct skill editing

Both components share the same storage layer (Alibaba OSS / S3 / local filesystem) and skill format (SKILL.md).

Deployment Model

Think of SkillClaw as "per-user client, per-group evolver":

1. Every user runs a local skillclaw client proxy on their own machine.
2. One shared group usually runs one skillclaw-evolve-server.
3. The client side and server side only meet through shared storage (local, oss, or s3).

This separation is the key beginner mental model:

• If you only want to use SkillClaw yourself, install the client first. You can add an evolve server later.
• If you want to join an existing team, you still install only the client. You do not run the evolve server unless you are operating the shared group.
• The evolve server can run on the same laptop, a remote VM, or any machine that can access the shared storage and upstream LLM endpoint.

User Guide

If this is your first time, start with Path A. It proves the client-side install and usage first, without mixing in shared deployment concerns.

Prerequisites

• macOS, Linux, or Windows
• Python >= 3.10
• A provider account that exposes an OpenAI-compatible API, or AWS Bedrock
• Install openclaw only if you intentionally choose the openclaw CLI integration or the server agent engine

The beginner path below is locally smoke-tested on macOS.

Path A: Run SkillClaw for yourself on one machine

1. Install SkillClaw from this repository. If you already have the source checkout, skip git clone.

macOS / Linux:

git clone https://github.com/AMAP-ML/SkillClaw.git && cd SkillClaw
bash scripts/install_skillclaw.sh
source .venv/bin/activate

Windows PowerShell (manual install because the repository does not currently ship a native .ps1 installer):

git clone https://github.com/AMAP-ML/SkillClaw.git
Set-Location SkillClaw
python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install -U pip
python -m pip install -e ".[evolve,sharing,server]"

2. Generate a local config.

skillclaw setup

The setup wizard prompts for the provider, model, local skills directory, PRM settings, optional CLI agent integration, and optional shared storage.

For a minimal first run:

• choose none for the CLI agent if you do not want SkillClaw to auto-configure an external agent yet
• local skills at ~/.skillclaw/skills
• disable shared storage if you only want to use the local proxy first
• enable local shared storage only if you want to add the evolve server later on the same machine, and use a dedicated root such as ~/.skillclaw/local-share
• disable PRM if you want the cheapest first pass

3. Start the client proxy and verify that it is healthy.

skillclaw start --daemon
skillclaw status
PROXY_PORT="$(skillclaw config proxy.port | awk '{print $2}')"
curl "http://127.0.0.1:${PROXY_PORT}/healthz"

The default proxy port is 30000, but the health check should follow your configured proxy.port. Use skillclaw config show to inspect the active upstream model, proxy port, and sharing target.

At this point SkillClaw is already usable as a single-user local proxy. You do not need to run an evolve server just to use the client.

If you later want automatic skill evolution for yourself, keep the same client install and continue with Server Guide.

Hermes Integration

If you already use Hermes, the client-side path is:

1. Install Hermes first.
2. Run skillclaw setup and choose hermes for CLI agent to configure.
3. Keep Proxy model name exposed to agents as skillclaw-model unless you have a specific reason to change it.
4. Start SkillClaw. On startup, SkillClaw rewrites ~/.hermes/config.yaml to point Hermes at the local proxy.

Minimal verification:

skillclaw start --daemon
hermes chat -Q -m skillclaw-model -q "Reply with exactly HERMES_SKILLCLAW_OK and nothing else."

Path B: Join an existing shared group

Install the same client as in Path A, then point your local client at the group's shared storage. The easiest beginner route is to rerun skillclaw setup, enable shared storage, and fill in the values your server operator gives you.

You can also set the keys directly. Example for OSS:

skillclaw config sharing.enabled true
skillclaw config sharing.backend oss
skillclaw config sharing.endpoint https://oss-cn-hangzhou.aliyuncs.com
skillclaw config sharing.bucket my-skillclaw-bucke