Fetches picoclaw-ai-assistant from aradotso/trending-skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/picoclaw-ai-assistant
Restart Cursor to activate picoclaw-ai-assistant. Access via /picoclaw-ai-assistant in your agent's command palette.
⚠
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
PicoClaw is an ultra-lightweight personal AI assistant written in Go. It runs on $10 hardware with under 10MB RAM and boots in under 1 second. It supports multiple LLM providers (OpenAI-compatible, Anthropic, Volcengine), optional web search tools, and deploys as a single self-contained binary on x86_64, ARM64, MIPS, and RISC-V Linux devices.
git clone https://github.com/sipeed/picoclaw.git
cd picoclaw
# Install dependenciesmake deps
# Build for current platformmake build
# Build for all platformsmake build-all
# Raspberry Pi Zero 2 W — 32-bitmake build-linux-arm # → build/picoclaw-linux-arm# Raspberry Pi Zero 2 W — 64-bitmake build-linux-arm64 # → build/picoclaw-linux-arm64# Build both Pi Zero variantsmake build-pi-zero
# Build and install to system PATHmakeinstall
Docker Compose
git clone https://github.com/sipeed/picoclaw.git
cd picoclaw
# First run — generates docker/data/config.json then exitsdocker compose -f docker/docker-compose.yml --profile gateway up
# Edit configvim docker/data/config.json
# Start in backgrounddocker compose -f docker/docker-compose.yml --profile gateway up -d# View logsdocker compose -f docker/docker-compose.yml logs -f picoclaw-gateway
# Stopdocker compose -f docker/docker-compose.yml --profile gateway down
Docker: Web Console (Launcher Mode)
docker compose -f docker/docker-compose.yml --profile launcher up -d# Open http://localhost:18800
Docker: One-shot Agent Mode
# Single questiondocker compose -f docker/docker-compose.yml run --rm picoclaw-agent -m"What is 2+2?"# Interactive sessiondocker compose -f docker/docker-compose.yml run --rm picoclaw-agent
Docker: Expose Gateway to Host
If the gateway needs to be reachable from the host, set:
PICOCLAW_GATEWAY_HOST=0.0.0.0 docker compose -f docker/docker-compose.yml --profile gateway up -d
Or set PICOCLAW_GATEWAY_HOST=0.0.0.0 in docker/data/config.json.
Never hard-code API keys. Reference environment variables using $VAR_NAME notation in config, or set them in your shell environment before launch.
3. Run
# Interactive chatpicoclaw
# Single messagepicoclaw -m"Summarize the latest Go release notes"# Use a specific modelpicoclaw -model claude-sonnet -m"Refactor this function for clarity"
Key CLI Commands
Command
Description
picoclaw onboard
Initialize config and workspace
picoclaw
Start interactive chat session
picoclaw -m "..."
Send a single message and exit
picoclaw -model <name>
Override the default model
picoclaw -config <path>
Use a custom config file
Configuration Reference
Model Entry Fields
{"model_name":"my-model",// Alias used in -model flag and agent defaults"model":"provider/model-id",// Provider-prefixed model identifier"api_key":"$ENV_VAR",// API key — use env var reference"api_base":"https://...",// Optional: override base URL (for self-hosted or regional endpoints)"request_timeout":300// Optional: seconds before timeout}
Supported Provider Prefixes
Prefix
Provider
openai/
OpenAI and OpenAI-compatible APIs
anthropic/
Anthropic Claude
volcengine/
Volcengine (Ark)
Agent Defaults
"agents":{"defaults":{"workspace":"~/.picoclaw/workspace",// Working directory for file operations"model_name":"gpt-4o",// Default model alias"max_tokens":8192,// Max response tokens"temperature":0.7,// Sampling temperature"max_tool_iterations":20// Max agentic tool-call loop iterations}}
Only enable one search provider at a time unless you want fallback behavior.
Common Patterns
Pattern: Minimal $10 Device Setup
For a LicheeRV-Nano or similar ultra-low-resource board:
# Download the RISC-V or ARM binary from releaseswget https://github.com/sipeed/picoclaw/releases/download/v0.1.1/picoclaw-linux-riscv64
chmod +x picoclaw-linux-riscv64
# Initialize./picoclaw-linux-riscv64 onboard
# Edit config — use a lightweight model, low max_tokenscat> ~/.picoclaw/config.json <<'EOF'
{
"agents": {
"defaults": {
Implementation Guide
Prerequisites
›Claude Desktop or compatible AI client with skill support
›Clear understanding of task or problem to solve
›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
1Install skill using provided installation command
2Test with simple use case relevant to your work
3Evaluate output quality and relevance
4Iterate on prompts to improve results
5Integrate into regular workflow if valuable
Common Pitfalls
⚠Expecting perfect results without iteration
⚠Not providing enough context in prompts
⚠Using skill for tasks outside its intended scope
⚠Accepting outputs without review and validation
Best Practices
✓ Do
+Start with clear, specific prompts
+Provide relevant context and constraints
+Review and refine all outputs before using
+Iterate to improve output quality
+Document successful prompt patterns
✗ Don't
−Don't use without understanding skill limitations
−Don't skip validation of outputs
−Don't share sensitive information in prompts
−Don't expect skill to replace human judgment
💡 Pro Tips
★Be specific about desired format and style
★Ask for multiple options to choose from
★Request explanations to understand reasoning
★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
1Familiarize yourself with skill capabilities and limitations
2Start with low-risk, non-critical tasks
3Progress to more complex and valuable use cases
4Build expertise through regular use and experimentation
