openhanako-personal-ai-agent▌
aradotso/trending-skills · updated May 13, 2026
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Skill by ara.so — Daily 2026 Skills collection.
OpenHanako Personal AI Agent
Skill by ara.so — Daily 2026 Skills collection.
OpenHanako is a desktop AI agent platform built on Electron that gives each agent persistent memory, a distinct personality, and the ability to autonomously operate your computer — read/write files, run terminal commands, browse the web, execute JavaScript, and manage schedules. Multiple agents can collaborate via channel group chats or task delegation.
Installation
Download & Run
# macOS Apple Silicon — download from releases page
# https://github.com/liliMozi/openhanako/releases
# Mount the .dmg and drag to Applications
# First launch — bypass Gatekeeper (one-time):
# Right-click app → Open → Open
# Windows — run the .exe installer from releases
# SmartScreen warning: click "More info" → "Run anyway"
Build from Source
git clone https://github.com/liliMozi/openhanako.git
cd openhanako
npm install
# Development mode
npm run dev
# Build for production
npm run build
# Run tests
npm test
First-Run Onboarding
On first launch, the wizard asks for:
- Language — UI language preference
- Your name — used by agents when addressing you
- Model provider — any OpenAI-compatible endpoint
- Three models:
chat model— main conversation (e.g.gpt-4o,deepseek-chat)utility model— lightweight tasks, summarization (e.g.gpt-4o-mini)utility large model— memory compilation, deep analysis (e.g.gpt-4o)
Provider Configuration Examples
// OpenAI
{
"baseURL": "https://api.openai.com/v1",
"apiKey": "process.env.OPENAI_API_KEY"
}
// DeepSeek
{
"baseURL": "https://api.deepseek.com/v1",
"apiKey": "process.env.DEEPSEEK_API_KEY"
}
// Local Ollama
{
"baseURL": "http://localhost:11434/v1",
"apiKey": "ollama"
}
// Qwen (Alibaba Cloud)
{
"baseURL": "https://dashscope.aliyuncs.com/compatible-mode/v1",
"apiKey": "process.env.DASHSCOPE_API_KEY"
}
Project Architecture
openhanako/
├── core/ # Engine orchestration + Managers (Agent, Session, Model, Preferences, Skill)
├── lib/ # Core libraries
│ ├── memory/ # Custom memory system (recency decay)
│ ├── tools/ # Built-in tools (files, terminal, browser, screenshot, canvas)
│ ├── sandbox/ # PathGuard + OS-level isolation (Seatbelt/Bubblewrap)
│ └── bridge/ # Multi-platform adapters (Telegram, Feishu, QQ)
├── server/ # Fastify 5 HTTP + WebSocket server
├── hub/ # Scheduler, ChannelRouter, EventBus
├── desktop/ # Electron 38 main process + React 19 frontend
├── tests/ # Vitest test suite
└── skills2set/ # Built-in skill definitions
Key Managers (via unified engine facade)
| Manager | Responsibility |
|---|---|
AgentManager |
Create, load, delete agents |
SessionManager |
Conversation sessions per agent |
ModelManager |
Route requests to configured providers |
PreferencesManager |
User/global settings |
SkillManager |
Install, enable, disable, sandbox skills |
Agent Configuration
Each agent is a self-contained folder you can back up:
~/.openhanako/agents/<agent-id>/
├── personality.md # Personality template (free-form prose or structured)
├── memory/
│ ├── working.db # Recent events (SQLite WAL)
│ └── compiled.md # Long-term compiled memory
├── desk/ # Agent's file workspace
│ └── notes/ # Jian notes
└── skills/ # Agent-local installed skills
Personality Template Example
# Hanako
You are Hanako, a calm and thoughtful assistant who prefers directness over verbosity.
You remember past conversations and refer to them naturally.
You ask clarifying questions before starting large tasks.
When writing code, you always add brief inline comments.
## Tone
- Warm but professional
- Uses occasional dry humor
- Never uses hollow affirmations ("Great question!")
## Constraints
- Always confirm before deleting files
- Summarize long terminal output rather than dumping it raw
Skills System
Skills extend agent capabilities. They live in skills2set/ (built-in) or are installed per-agent.
Install a Skill from GitHub
// Via the Skills UI in the app, or programmatically:
const { skillManager } = engine;
await skillManager.installFromGitHub({
repo: 'some-user/hanako-skill-weather',
agentId: 'agent-abc123',
safetyReview: true // strict review enabled by default
});
Skill Definition Format (SKILL.md → skills2set)
---
name: web-scraper
version: 1.0.0
description: Scrape structured data from web pages
tools:
- browser
- javascript
permissions:
- network
---
## Instructions for Agent
When asked to scrape a page:
1. Use the `browser` tool to navigate to the URL
2. Use `executeJavaScript` to extract structured data
3. Save results to the desk as JSON
Writing a Custom Skill (JavaScript)
// skills/my-skill/index.js
export default {
name: 'my-skill',
version: '1.0.0',
description: 'Does something useful',
// Tools this skill adds to the agent
tools: [
{
name: 'fetch_weather',
description: 'Fetch current weather for a city',
parameters: {
type: 'object',
properties: {
city: { type: 'string', description: 'City name' }
},
required: ['city']
},
async execute({ city }) {
const res = await fetch(
`https://wttr.in/${encodeURIComponent(city)}?format=j1`
);
const data = await res.json();
return {
temp_c: data.current_condition[0].temp_C,
description: data.current_condition[0].weatherDesc[0].value
};
}
}
]
};
Memory System
OpenHanako uses a recency-decay memory model: recent events stay sharp, older ones fade.
// Accessing memory programmatically (core/lib/memory)
import { MemoryManager } from './lib/memory/index.js';
const memory = new MemoryManager({ agentId: 'agent-abc123' });
// Store a memory event
await memory.store({
type: 'conversation',
contentHow to use openhanako-personal-ai-agent on Cursor
AI-first code editor with Composer
Prerequisites
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 openhanako-personal-ai-agent
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches openhanako-personal-ai-agent from GitHub repository aradotso/trending-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate openhanako-personal-ai-agent. Access the skill through slash commands (e.g., /openhanako-personal-ai-agent) 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.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
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.
Ratings
4.5★★★★★51 reviews- ★★★★★Meera Torres· Dec 24, 2024
openhanako-personal-ai-agent is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Ava Mehta· Dec 24, 2024
Useful defaults in openhanako-personal-ai-agent — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Lucas Torres· Dec 16, 2024
I recommend openhanako-personal-ai-agent for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Shikha Mishra· Dec 8, 2024
openhanako-personal-ai-agent reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Yash Thakker· Nov 27, 2024
I recommend openhanako-personal-ai-agent for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Evelyn Flores· Nov 19, 2024
openhanako-personal-ai-agent fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Hiroshi Kapoor· Nov 15, 2024
Registry listing for openhanako-personal-ai-agent matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Mia Bhatia· Nov 7, 2024
openhanako-personal-ai-agent reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Mia Reddy· Oct 26, 2024
Registry listing for openhanako-personal-ai-agent matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Dhruvi Jain· Oct 18, 2024
Useful defaults in openhanako-personal-ai-agent — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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