Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
Agent Orchestrator
Meta-agent skill for orchestrating complex tasks through autonomous sub-agents. Decomposes macro tasks into subtasks, spawns specialized sub-agents with dynamically generated SKILL.md files, coordinates file-based communication, consolidates results, and dissolves agents upon completion. MANDATORY TRIGGERS: orchestrate, multi-agent, decompose task, spawn agents, sub-agents, parallel agents, agent coordination, task breakdown, meta-agent, agent factory, delegate tasks
skill openclawclawhub Free
Meta-agent skill for orchestrating complex tasks through autonomous sub-agents. Decomposes macro tasks into subtasks, spawns specialized sub-agents with dynamically generated SKILL.md files, coordinates file-based communication, consolidates results, and dissolves agents upon completion. MANDATORY TRIGGERS: orchestrate, multi-agent, decompose task, spawn agents, sub-agents, parallel agents, agent coordination, task breakdown, meta-agent, agent factory, delegate tasks
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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
- SKILL.md, references/communication-protocol.md, references/sub-agent-templates.md
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. 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. 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
- aatmaan1
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Agent Orchestrator
Orchestrate complex tasks by decomposing them into subtasks, spawning autonomous sub-agents, and consolidating their work.
Phase 1: Task Decomposition
Analyze the macro task and break it into independent, parallelizable subtasks: 1. Identify the end goal and success criteria 2. List all major components/deliverables required 3. Determine dependencies between components 4. Group independent work into parallel subtasks 5. Create a dependency graph for sequential work Decomposition Principles: Each subtask should be completable in isolation Minimize inter-agent dependencies Prefer broader, autonomous tasks over narrow, interdependent ones Include clear success criteria for each subtask
Phase 2: Agent Generation
For each subtask, create a sub-agent workspace: python3 scripts/create_agent.py <agent-name> --workspace <path> This creates: <workspace>/<agent-name>/ âââ SKILL.md # Generated skill file for the agent âââ inbox/ # Receives input files and instructions âââ outbox/ # Delivers completed work âââ workspace/ # Agent's working area âââ status.json # Agent state tracking Generate SKILL.md dynamically with: Agent's specific role and objective Tools and capabilities needed Input/output specifications Success criteria Communication protocol See references/sub-agent-templates.md for pre-built templates.
Phase 3: Agent Dispatch
Initialize each agent by: Writing task instructions to inbox/instructions.md Copying required input files to inbox/ Setting status.json to {"state": "pending", "started": null} Spawning the agent using the Task tool: # Spawn agent with its generated skill Task( description=f"{agent_name}: {brief_description}", prompt=f""" Read the skill at {agent_path}/SKILL.md and follow its instructions. Your workspace is {agent_path}/workspace/ Read your task from {agent_path}/inbox/instructions.md Write all outputs to {agent_path}/outbox/ Update {agent_path}/status.json when complete. """, subagent_type="general-purpose" )
Phase 4: Monitoring (Checkpoint-based)
For fully autonomous agents, minimal monitoring is needed: # Check agent completion def check_agent_status(agent_path): status = read_json(f"{agent_path}/status.json") return status.get("state") == "completed" Periodically check status.json for each agent. Agents update this file upon completion.
Phase 5: Consolidation
Once all agents complete: Collect outputs from each agent's outbox/ Validate deliverables against success criteria Merge/integrate outputs as needed Resolve conflicts if multiple agents touched shared concerns Generate summary of all work completed # Consolidation pattern for agent in agents: outputs = glob(f"{agent.path}/outbox/*") validate_outputs(outputs, agent.success_criteria) consolidated_results.extend(outputs)
Phase 6: Dissolution & Summary
After consolidation: Archive agent workspaces (optional) Clean up temporary files Generate final summary: What was accomplished per agent Any issues encountered Final deliverables location Time/resource metrics python3 scripts/dissolve_agents.py --workspace <path> --archive
File-Based Communication Protocol
See references/communication-protocol.md for detailed specs. Quick Reference: inbox/ - Read-only for agent, written by orchestrator outbox/ - Write-only for agent, read by orchestrator status.json - Agent updates state: pending â running â completed | failed
Example: Research Report Task
Macro Task: "Create a comprehensive market analysis report" Decomposition: âââ Agent: data-collector â âââ Gather market data, competitor info, trends âââ Agent: analyst â âââ Analyze collected data, identify patterns âââ Agent: writer â âââ Draft report sections from analysis âââ Agent: reviewer âââ Review, edit, and finalize report Dependency: data-collector â analyst â writer â reviewer
Sub-Agent Templates
Pre-built templates for common agent types in references/sub-agent-templates.md: Research Agent - Web search, data gathering Code Agent - Implementation, testing Analysis Agent - Data processing, pattern finding Writer Agent - Content creation, documentation Review Agent - Quality assurance, editing Integration Agent - Merging outputs, conflict resolution
Best Practices
Start small - Begin with 2-3 agents, scale as patterns emerge Clear boundaries - Each agent owns specific deliverables Explicit handoffs - Use structured files for agent communication Fail gracefully - Agents report failures; orchestrator handles recovery Log everything - Status files track progress for debugging
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
3 Docs
- SKILL.md
- references/communication-protocol.md
- references/sub-agent-templates.md