SKILL.md
$27
- Client Proxy — Local API proxy intercepting
/v1/chat/completionsand/v1/messages, syncing skills
- Workflow Evolve Server (
evolve_server) — Fixed 3-stage pipeline: Summarize → Aggregate → Execute
- Agent Evolve Server (
agent_evolve_server) — Autonomous OpenClaw agent that reads sessions and writes evolved skills
Installation
Client / Local Development
git clone <repo-url> SkillClaw && cd SkillClaw
bash scripts/install_skillclaw.sh
source .venv/bin/activateServer Deployment
bash scripts/install_skillclaw_server.sh
source .venv-server/bin/activate
# Required only for the agent evolve server
npm install -g openclawEnvironment Configuration
Copy and populate credentials — never hardcode secrets:
# From example_env.sh
export OPENAI_BASE_URL="https://your-api-gateway/v1"
export OPENAI_API_KEY="$OPENAI_API_KEY"
# For OSS storage backend
export EVOLVE_STORAGE_ENDPOINT="$OSS_ENDPOINT"
export EVOLVE_STORAGE_BUCKET="$OSS_BUCKET"
export OSS_ACCESS_KEY_ID="$OSS_ACCESS_KEY_ID"
export OSS_ACCESS_KEY_SECRET="$OSS_ACCESS_KEY_SECRET"Config file lives at ~/.skillclaw/config.yaml. Inspect and modify:
skillclaw config show
skillclaw config <key> <value>CLI Reference
Client Proxy Setup
skillclaw setup # Initialize config and directories
skillclaw start # Start the local proxy server
skillclaw stop # Stop the proxy server
skillclaw status # Show proxy status and config summary
skillclaw config show # Dump full resolved configSkill Management
skillclaw skills pull # Download shared skills from cloud storage
skillclaw skills push # Upload local skills to cloud storage
skillclaw skills sync # Bidirectional sync (pull + push)
skillclaw skills list-remote # Browse skills available in shared storageBenchmarking
skillclaw benchmark --help # List all benchmark subcommandsStarting the Evolve Servers
Workflow Evolve Server (Summarize → Aggregate → Execute)
skillclaw-evolve-server \
--port 8787 \
--interval 300 \
--storage-backend oss \
--oss-endpoint "$EVOLVE_STORAGE_ENDPOINT" \
--oss-bucket "$EVOLVE_STORAGE_BUCKET" \
--group-id my-groupAgent Evolve Server (Autonomous OpenClaw agent)
skillclaw-agent-evolve-server \
--port 8787 \
--interval 300 \
--no-fresh \
--storage-backend oss \
--oss-endpoint "$EVOLVE_STORAGE_ENDPOINT" \
--oss-bucket "$EVOLVE_STORAGE_BUCKET" \
--group-id my-groupUse --no-fresh to continue from existing evolved skills rather than starting from scratch each run.
Local Filesystem Backend (for development)
skillclaw-evolve-server \
--port 8787 \
--interval 60 \
--storage-backend local \
--local-storage-path ./skill_storage \
--group-id dev-groupKey Configuration Options
Option
Description
Default
--port
Server port
8787
--interval
Seconds between evolution cycles
300
--storage-backend
oss, s3, or local
local
--group-id
Identifier for your agent cluster
required
--no-fresh
Resume from existing skills
flag
--oss-endpoint
OSS endpoint URL
env var
--oss-bucket
OSS bucket name
env var
Skill Format (SKILL.md)
Skills are Markdown files with YAML frontmatter. The evolve server reads session data and writes or updates these files:
---
name: my-skill-name
description: What this skill does
version: 1.0.0
tags: [web, scraping]
---
# Skill Name
## When to Use
...
## Instructions
Step-by-step instructions the agent follows.
## Examples
\`\`\`python
# working code example
\`\`\`WildClawBench Experiment
Run the main iterative evolution experiment:
python scripts/run_wildclawbench_iterative_evolve_agent.py \
--group-id wildclawbench-test \
--storage-backend local \
--local-storage-path ./wb_storage \
--num-iterations 3This evaluates skill evolution on real-world agent tasks from WildClawBench.
Python API Usage
Programmatic Skill Sync
from skillclaw.skill_manager import SkillManager
from skillclaw.skill_hub import SkillHub
# Initialize with local backend
manager = SkillManager(storage_backend="local", local_path="./skills")
# Pull skills from shared storage
manager.pull()
# List available skills
skills = manager.list_local()
for skill in skills:
print(f"{skill.name}: {skill.description}")
# Push a new skill
manager.push("path/to/SKILL.md")Launching the Proxy Programmatically
from skillclaw.launcher import SkillClawLauncher
from skillclaw.config import SkillClawConfig
config = SkillClawConfig(
upstream_base_url="https://api.openai.com/v1",
upstream_api_key="$OPENAI_API_KEY", # loaded from env at runtime
proxy_port=8080,
storage_backend="local",
local_storage_path="./skillclaw_data",
group_id="my-agents",
)
launcher = SkillClawLauncher(config)
launcher.start()
# Agents now point to http://localhost:8080/v1Using the Evolve Server API
import httpx
# Trigger an immediate evolution cycle
response = httpx.post("http://localhost:8787/evolve")
print(response.json()) # {"status": "ok", "skills_evolved": 3}
# Check server status
status = httpx.get("http://localhost:8787/status")
print(status.json())Evolve Server Config ( .env.example )
# evolve_server/.env.example
OPENAI_BASE_URL="https://your-api-gateway/v1"
OPENAI_API_KEY="$OPENAI_API_KEY"
STORAGE_BACKEND=oss
OSS_ENDPOINT="$EVOLVE_STORAGE_ENDPOINT"
OSS_BUCKET="$EVOLVE_STORAGE_BUCKET"
OSS_ACCESS_KEY_ID="$OSS_ACCESS_KEY_ID"
OSS_ACCESS_KEY_SECRET="$OSS_ACCESS_KEY_SECRET"
GROUP_ID=production-cluster
EVOLVE_INTERVAL=300Supported Frameworks
SkillClaw natively integrates with these OpenClaw-compatible frameworks:
- CoPaw — collaborative agent framework
- IronClaw — robust task execution
- PicoClaw — lightweight agents
- ZeroClaw — zero-shot agents
- NanoClaw — minimal footprint
- NemoClaw — NVIDIA NeMo-based agents
Point any framework's OpenAI-compatible API calls at the SkillClaw proxy to start recording sessions.
Deployment Pattern: Multi-User Cluster
User A → Agent (port 8080) ─┐
User B → Agent (port 8081) ─┼──→ Shared OSS Bucket ←── Evolve Server
User C → Agent (port 8082) ─┘ ↑
Skills sync'd
back to all agents# Each user's machine runs:
skillclaw start --group-id production-cluster --port 8080
# One central server runs:
skillclaw-evolve-server \
--storage-backend oss \
--oss-bucket "$SHARED_BUCKET" \
--group-id production-cluster \
--interval 300Troubleshooting
Proxy won't start:
skillclaw status # Check if already running
skillclaw stop && skillclaw start # Restart
skillclaw config show # Verify OPENAI_BASE_URL is setSkills not syncing:
skillclaw skills list-remote # Verify storage connection
skillclaw config show # Check storage backend config
# Confirm env vars are exported: echo $OSS_ACCESS_KEY_IDEvolve server not processing sessions:
# Check server logs for cycle output
# Verify --group-id matches the client proxy group-id
# Try --storage-backend local for debugging
skillclaw-evolve-server --storage-backend local --local-storage-path ./debug_storage --group-id debugAgent evolve server fails to start:
which openclaw # Must be in PATH
npm install -g openclaw # Install if missing
# Verify OPENAI_BASE_URL and OPENAI_API_KEY are set for the agent's LLMPort already in use:
skillclaw stop
lsof -i :8787 | grep LISTEN # Find conflicting process
skillclaw-evolve-server --port 8788 ...Project Structure Reference
SkillClaw/
├── skillclaw/ # Client proxy, CLI, config
│ ├── cli.py # All `skillclaw` CLI commands
│ ├── api_server.py # Proxy server implementation
│ ├── launcher.py # Process management
│ ├── skill_manager.py # Local skill CRUD
│ ├── skill_hub.py # Cloud sync logic
│ └── experiments/ # Benchmark runners
├── evolve_server/ # Workflow evolve server
│ ├── summarizer.py # Stage 1: session → summary
│ ├── aggregation.py # Stage 2: summaries → patterns
│ ├── execution.py # Stage 3: patterns → SKILL.md
│ └── skill_registry.py # Skill dedup and versioning
├── agent_evolve_server/ # OpenClaw-based evolve server
│ ├── workspace.py # Session/skill file workspace
│ ├── openclaw_runner.py # Agent execution harness
│ └── EVOLVE_AGENTS.md # Agent prompt and tool config
└── scripts/
├── install_skillclaw.sh
├── install_skillclaw_server.sh
└── run_wildclawbench_iterative_evolve_agent.py