SkillClaw: Collective Skill Evolution for LLM Agents Skill by ara.so — Daily 2026 Skills collection. SkillClaw is a framework that makes LLM agents progressively smarter by evolving reusable skills from real session data and sharing them across a group of agents. It intercepts OpenAI-compatible API calls via a local proxy, records session artifacts, and runs an evolve server that distills experience into SKILL.md files synced via cloud storage (OSS/S3/local). Architecture User → OpenClaw Agent → SkillClaw Client Proxy → Upstream LLM API ↓ records sessions Shared Storage (OSS/S3/local) ↑ reads sessions, writes skills Evolve Server (workflow or agent) Three components share the same storage layer and skill format: Client Proxy — Local API proxy intercepting /v1/chat/completions and /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/activate Server Deployment bash scripts/install_skillclaw_server.sh source .venv-server/bin/activate

Required only for the agent evolve server

npm install -g openclaw Environment 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 config

Skill 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 storage

Benchmarking skillclaw benchmark --help

List all benchmark subcommands

Starting 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-group Agent 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-group Use --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-group Key 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 3 This 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/v1

Using 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 = 300 Supported 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 300 Troubleshooting Proxy won't start: skillclaw status

Check if already running

skillclaw stop && skillclaw start

Restart

skillclaw config show

Verify OPENAI_BASE_URL is set

Skills 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_ID

Evolve 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 debug Agent 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 LLM

Port 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