picoclaw-ai-assistant▌
aradotso/trending-skills · updated Apr 8, 2026
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Skill by ara.so — Daily 2026 Skills collection.
PicoClaw AI Assistant
Skill by ara.so — Daily 2026 Skills collection.
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.
Installation
Precompiled Binary
Download from the releases page:
# Linux ARM64 (Raspberry Pi, LicheeRV-Nano, etc.)
wget https://github.com/sipeed/picoclaw/releases/download/v0.1.1/picoclaw-linux-arm64
chmod +x picoclaw-linux-arm64
./picoclaw-linux-arm64 onboard
Build from Source
git clone https://github.com/sipeed/picoclaw.git
cd picoclaw
# Install dependencies
make deps
# Build for current platform
make build
# Build for all platforms
make build-all
# Raspberry Pi Zero 2 W — 32-bit
make build-linux-arm # → build/picoclaw-linux-arm
# Raspberry Pi Zero 2 W — 64-bit
make build-linux-arm64 # → build/picoclaw-linux-arm64
# Build both Pi Zero variants
make build-pi-zero
# Build and install to system PATH
make install
Docker Compose
git clone https://github.com/sipeed/picoclaw.git
cd picoclaw
# First run — generates docker/data/config.json then exits
docker compose -f docker/docker-compose.yml --profile gateway up
# Edit config
vim docker/data/config.json
# Start in background
docker compose -f docker/docker-compose.yml --profile gateway up -d
# View logs
docker compose -f docker/docker-compose.yml logs -f picoclaw-gateway
# Stop
docker 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 question
docker compose -f docker/docker-compose.yml run --rm picoclaw-agent -m "What is 2+2?"
# Interactive session
docker 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.
Termux (Android)
pkg install wget proot
wget https://github.com/sipeed/picoclaw/releases/download/v0.1.1/picoclaw-linux-arm64
chmod +x picoclaw-linux-arm64
termux-chroot ./picoclaw-linux-arm64 onboard
Quick Start
1. Initialize
picoclaw onboard
This creates ~/.picoclaw/config.json with a starter configuration.
2. Configure ~/.picoclaw/config.json
{
"agents": {
"defaults": {
"workspace": "~/.picoclaw/workspace",
"model_name": "gpt-4o",
"max_tokens": 8192,
"temperature": 0.7,
"max_tool_iterations": 20
}
},
"model_list": [
{
"model_name": "gpt-4o",
"model": "openai/gpt-4o",
"api_key": "$OPENAI_API_KEY",
"request_timeout": 300
},
{
"model_name": "claude-sonnet",
"model": "anthropic/claude-sonnet-4-5",
"api_key": "$ANTHROPIC_API_KEY"
},
{
"model_name": "ark-code",
"model": "volcengine/ark-code-latest",
"api_key": "$VOLCENGINE_API_KEY",
"api_base": "https://ark.cn-beijing.volces.com/api/coding/v3"
}
],
"tools": {
"web": {
"brave": {
"enabled": false,
"api_key": "$BRAVE_API_KEY"
},
"tavily": {
"enabled": false,
"api_key": "$TAVILY_API_KEY"
}
}
}
}
Never hard-code API keys. Reference environment variables using
$VAR_NAMEnotation in config, or set them in your shell environment before launch.
3. Run
# Interactive chat
picoclaw
# Single message
picoclaw -m "Summarize the latest Go release notes"
# Use a specific model
picoclaw -model claude-sonnet -m "Refactor this function for clarity"
Key CLI Commands
| Command | Description |
|---|---|
picoclaw onboard |
Initialize config and workspace |
picoclaw |
Start interactive chat session |
picoclaw -m "..." |
Send a single message and exit |
picoclaw -model <name> |
Override the default model |
picoclaw -config <path> |
Use a custom config file |
Configuration Reference
Model Entry Fields
{
"model_name": "my-model", // Alias used in -model flag and agent defaults
"model": "provider/model-id", // Provider-prefixed model identifier
"api_key": "$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
}
}
Web Search Tools
Get free API keys:
- Tavily: https://tavily.com — 1,000 free queries/month
- Brave Search: https://brave.com/search/api — 2,000 free queries/month
"tools": {
"web": {
"tavily": {
"enabled": true,
"api_key": "$TAVILY_API_KEY"
},
"brave": {
"enabled": false,
"api_key": "$BRAVE_API_KEY"
}
}
}
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 releases
wget 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_tokens
cat > ~/.picoclaw/config.json << 'EOF'
{
"agents": {
"defaults": {
how to use picoclaw-ai-assistantHow to use picoclaw-ai-assistant on Cursor
AI-first code editor with Composer
1Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add picoclaw-ai-assistant
2Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/aradotso/trending-skills --skill picoclaw-ai-assistantThe skills CLI fetches picoclaw-ai-assistant from GitHub repository aradotso/trending-skills and configures it for Cursor.
3Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?││ ── Universal (.agents/skills) ── always included ────│ • Amp│ • Antigravity│ • Cline│ • Codex│ ●Cursor(selected)│ • Cursor│ • Windsurf4Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/picoclaw-ai-assistantReload or restart Cursor to activate picoclaw-ai-assistant. Access the skill through slash commands (e.g., /picoclaw-ai-assistant) or your agent's skill management interface.
⚠Security & Verification 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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
List & Monetize Your Skill
Submit your Claude Code skill and start earning
GET_STARTED →Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
✓Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
✓Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
✓Improve work quality by 30-40% with less effort
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
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate 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
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
general reviewsRatings
4.8★★★★★69 reviews- ★★★★★Ganesh Mohane· Dec 20, 2024
picoclaw-ai-assistant reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Aisha Perez· Dec 16, 2024
Useful defaults in picoclaw-ai-assistant — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Tariq Chen· Dec 8, 2024
picoclaw-ai-assistant reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Yusuf Patel· Dec 4, 2024
I recommend picoclaw-ai-assistant for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Tariq Bansal· Dec 4, 2024
picoclaw-ai-assistant fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Arjun Martin· Nov 27, 2024
I recommend picoclaw-ai-assistant for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Arjun Bhatia· Nov 23, 2024
picoclaw-ai-assistant reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Zara Abebe· Nov 23, 2024
picoclaw-ai-assistant is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Advait Kim· Nov 15, 2024
Solid pick for teams standardizing on skills: picoclaw-ai-assistant is focused, and the summary matches what you get after install.
- ★★★★★Rahul Santra· Nov 11, 2024
I recommend picoclaw-ai-assistant for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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