Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Agent Os
Persistent agent operating system for OpenClaw. Agents remember across sessions, learn from experience, coordinate on complex projects without duplicate work.
skill openclawclawhub Free
Persistent agent operating system for OpenClaw. Agents remember across sessions, learn from experience, coordinate on complex projects without duplicate work.
β¬ 0 downloads β
0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- ARCHITECTURE.md, BUILD_SUMMARY.md, README.md, SKILL.md, core/agent.js, core/executor.js
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
Provenance
- Publisher
- cryptocana
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Agent OS β Persistent Agent Operating System
Agents that remember. Learn. Coordinate.
What It Does
Agent OS enables multi-agent project execution with persistent memory: Agent Memory β Each agent remembers past tasks, lessons learned, success rates Task Decomposition β Break high-level goals into executable task sequences Smart Routing β Assign tasks to agents based on capability fit Execution Tracking β Live progress board showing what every agent is doing State Persistence β Project state survives restarts (resume mid-project)
Installation
clawhub install nova/agent-os
Basic Usage
const { AgentOS } = require('agent-os'); const os = new AgentOS('my-project'); // Register agents with capabilities os.registerAgent('research', 'π Research', ['research', 'planning']); os.registerAgent('design', 'π¨ Design', ['design', 'planning']); os.registerAgent('dev', 'π» Development', ['development']); os.initialize(); // Run a project const result = await os.runProject('Build a feature', [ 'planning', 'design', 'development', ]); console.log(result.progress); // 100
Agent
Persistent worker with: Memory β Past tasks, lessons learned, success rates State β Current task, progress, blockers Capabilities β What it's good at (research, design, development, etc.)
TaskRouter
Decomposes goals into executable tasks: Breaks "Build a feature" into: plan β design β develop β test Matches tasks to agents based on capability fit Tracks dependencies (task A must finish before task B)
Executor
Runs tasks sequentially: Assigns tasks to agents Tracks progress in real-time Persists state so projects survive restarts Handles blockers and errors
AgentOS
Orchestrates everything: Register agents Initialize system Run projects Get status
Architecture
AgentOS (top-level orchestration) βββ Agent (persistent worker) β βββ Memory (lessons, capabilities, history) β βββ State (current task, progress) βββ TaskRouter (goal decomposition) β βββ Templates (planning, design, development, etc.) β βββ Matcher (task β agent assignment) βββ Executor (task execution) βββ Sequential runner βββ Progress tracking βββ State persistence
State Persistence
All state is saved to the data/ directory: [agent-id]-memory.json β Agent knowledge base [agent-id]-state.json β Current agent status [project-id]-project.json β Project task list + status This means: β Projects survive restarts β Agents remember past work β Resume mid-project seamlessly
File Structure
agent-os/ βββ core/ β βββ agent.js # Agent class β βββ task-router.js # Task decomposition β βββ executor.js # Execution scheduler β βββ index.js # AgentOS class βββ ui/ β βββ dashboard.html # Live progress UI β βββ dashboard.js # Dashboard logic β βββ style.css # Styling βββ examples/ β βββ research-project.js # Full working example βββ data/ # Auto-created (persistent state) βββ package.json
AgentOS
new AgentOS(projectId?) registerAgent(id, name, capabilities) initialize() runProject(goal, taskTypes) getStatus() getAgentStatus(agentId) toJSON()
Agent
startTask(task) updateProgress(percentage, message) completeTask(output) setBlocker(message) recordError(error) learnLesson(category, lesson) reset() getStatus()
TaskRouter
decompose(goal, taskTypes) matchAgent(taskType) getTasksForAgent(agentId, tasks) canExecuteTask(task, allTasks) getNextTask(tasks) completeTask(taskId, tasks, output) getProjectStatus(tasks)
Executor
initializeProject(goal, taskTypes) execute() executeTask(task) getStatus()
Example: Research + Design + Development
See examples/research-project.js for the canonical example: npm start This demonstrates: β 3 agents with different capabilities β 12 tasks across 3 phases (planning, design, development) β Sequential execution with progress tracking β State persistence to disk β Final status report Expected output: β Registered 3 agents π Task Plan: 12 tasks π Starting execution... β [Task 1] Complete β [Task 2] Complete ... π PROJECT COMPLETE - 100% progress
What's Coming (v0.2+)
HTTP server + live dashboard Parallel task execution (DAG solver) Capability learning system (auto-score agents) Smart agent routing (match to best agent) Failure recovery + retry logic Cost tracking (token usage per agent) Human checkpoints (review high-risk outputs)
Philosophy
Agents should remember what they learn. Most agent frameworks are stateless. Agent OS keeps persistent memory so agents: Remember β No redundant context resets Learn β Capability scores improve over time Coordinate β Shared state prevents duplication Cost less β Less context = cheaper API calls
License
MIT Built with β€οΈ by Nova for OpenClaw See README.md and ARCHITECTURE.md for complete documentation.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Docs2 Scripts
- SKILL.md
- ARCHITECTURE.md
- BUILD_SUMMARY.md
- README.md
- core/agent.js
- core/executor.js