# Send Agent Orchestrator to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - 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. ## Suggested prompts ### New install ```text 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 ```text 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. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "agent-orchestrator-molter-102", "name": "Agent Orchestrator", "source": "tencent", "type": "skill", "category": "AI 智能", "sourceUrl": "https://clawhub.ai/variable190/agent-orchestrator-molter-102", "canonicalUrl": "https://clawhub.ai/variable190/agent-orchestrator-molter-102", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/agent-orchestrator-molter-102", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=agent-orchestrator-molter-102", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "CHANGELOG.md", "CONVENTION.md", "PUBLISHING.md", "README.md", "SECURITY.md", "SKILL.md" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-23T16:43:11.935Z", "expiresAt": "2026-04-30T16:43:11.935Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/agent-orchestrator-molter-102" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/agent-orchestrator-molter-102", "downloadUrl": "https://openagent3.xyz/downloads/agent-orchestrator-molter-102", "agentUrl": "https://openagent3.xyz/skills/agent-orchestrator-molter-102/agent", "manifestUrl": "https://openagent3.xyz/skills/agent-orchestrator-molter-102/agent.json", "briefUrl": "https://openagent3.xyz/skills/agent-orchestrator-molter-102/agent.md" } } ``` ## Documentation ### agent-orchestrator Multi-agent orchestration for OpenClaw. Implements 5 proven patterns for coordinating multiple AI agents: Work Crew, Supervisor, Pipeline, Expert Council, and Auto-Routing. USE WHEN: A task can be parallelized for speed or redundancy (Work Crew) Complex tasks need dynamic planning and delegation (Supervisor) Work follows a predictable sequence of stages (Pipeline) Cross-domain input is needed from multiple specialists (Expert Council) Mixed task types need automatic routing to appropriate specialists (Auto-Routing) Research tasks require breadth-first exploration of multiple angles High-stakes decisions need confidence through multiple perspectives DON'T USE WHEN: Simple tasks that fit in one agent's context window (use main session instead) Sequential tasks with no parallelization opportunity (use regular tool calls) One-shot deterministic tasks (use single agent) Tasks requiring real-time inter-agent conversation (this uses async spawning) Tasks where 15x token cost cannot be justified Quick/simple tasks where coordination overhead exceeds benefit Outputs: Aggregated results from multiple parallel agents Synthesized consensus recommendations Routing decisions to appropriate specialists Structured output from staged processing ### Decision Matrix PatternUse WhenAvoid WhencrewSame task from multiple angles, verification, research breadthResults cannot be easily compared/mergedsuperviseDynamic decomposition needed, complex planningFixed workflow, simple delegationpipelineWell-defined sequential stages, content creationPath needs runtime adaptationcouncilCross-domain expertise, risk assessment, policy reviewSingle-domain task, need fast consensusrouteMixed workload types, automatic classificationTask type is already known ### Auto-Routing Pattern The route command analyzes tasks and automatically classifies them by type, then routes to the appropriate specialist: # Basic routing claw agent-orchestrator route --task "Write Python parser" # With custom specialist pool claw agent-orchestrator route \\ --task "Analyze data and create report" \\ --specialists "analyst,data,writer" # Force specific specialist claw agent-orchestrator route \\ --task "Something complex" \\ --force coder ### Confidence Thresholds High confidence (>0.85): Auto-route immediately Good confidence (0.7-0.85): Propose with confirmation option Moderate confidence (0.5-0.7): Show top alternatives Low confidence (<0.5): Request clarification Available specialists: coder, researcher, writer, analyst, planner, reviewer, creative, data, devops, support ### Common Workflows # Parallel research with consensus claw agent-orchestrator crew \\ --task "Research Bitcoin Lightning 2026 adoption" \\ --agents 4 \\ --perspectives technical,business,security,competitors \\ --converge consensus # Best-of redundancy for critical analysis claw agent-orchestrator crew \\ --task "Audit this smart contract for vulnerabilities" \\ --agents 3 \\ --converge best-of # Supervisor-managed code review claw agent-orchestrator supervise \\ --task "Refactor authentication module" \\ --workers coder,reviewer,tester \\ --strategy adaptive # Staged content pipeline claw agent-orchestrator pipeline \\ --stages research,draft,review,finalize \\ --input "topic: AI agent adoption trends" # Expert council for decision claw agent-orchestrator council \\ --question "Should we publish this blog post about unreleased features?" \\ --experts skeptic,ethicist,strategist \\ --converge consensus \\ --rounds 2 # Auto-route mixed tasks claw agent-orchestrator route \\ --task "Write Python function to analyze CSV data" \\ --specialists coder,researcher,writer,analyst # Force route to specific specialist claw agent-orchestrator route \\ --task "Debug authentication error" \\ --force coder \\ --confidence-threshold 0.9 # Route and output as JSON for scripting claw agent-orchestrator route \\ --task $TASK \\ --format json \\ --specialists "coder,data,analyst" ### Negative Examples DON'T: Use crew for simple single-answer questions # WRONG: Wasteful for simple facts claw agent-orchestrator crew --task "What is 2+2?" --agents 3 # RIGHT: Use main session directly What is 2+2? DON'T: Use supervise when pipeline suffices # WRONG: Over-engineering fixed workflows claw agent-orchestrator supervise --task "Draft, edit, publish" # RIGHT: Use pipeline for fixed sequences claw agent-orchestrator pipeline --stages draft,edit,publish DON'T: Route when task type is obvious # WRONG: Unnecessary classification overhead claw agent-orchestrator route --task "Write Python code" # RIGHT: Direct to appropriate specialist claw agent-orchestrator crew --pattern code --task "Write Python code" DON'T: Use multi-agent for very small context tasks # WRONG: Coordination overhead exceeds value claw agent-orchestrator crew --task "Fix typo" --agents 2 # RIGHT: Single agent or direct edit edit file.py "typo" "correct" ### Token Cost Warning Multi-agent patterns use approximately 15x more tokens than single-agent interactions. Use only for high-value tasks where quality improvement justifies the cost. See Anthropic research: token usage explains 80% of performance variance in complex tasks. ### Dependencies Python 3.8+ OpenClaw sessions_spawn capability OpenClaw sessions_list capability OpenClaw sessions_history capability ### Files __main__.py - CLI entry point crew.py - Work Crew pattern implementation supervise.py - Supervisor pattern (Phase 2) council.py - Expert Council pattern (Phase 2) pipeline.py - Pipeline pattern (Phase 2) route.py - Auto-Routing pattern (Phase 2) utils.py - Shared utilities for session management ### Status MVP: Work Crew pattern implemented Phase 2: 100% Complete Supervisor pattern implemented - dynamic task decomposition and worker delegation Pipeline pattern implemented - sequential staged processing with validation gates Council pattern implemented - multi-expert deliberation with convergence methods Route pattern implemented - intelligent task classification and specialist routing ### References Anthropic Multi-Agent Research System LangGraph Supervisor Pattern CrewAI Framework AutoGen Conversational Agents ## Trust - Source: tencent - Verification: Indexed source record - Publisher: variable190 - Version: 1.0.2 ## Source health - Status: healthy - Source download looks usable. - Yavira can redirect you to the upstream package for this source. - Health scope: source - Reason: direct_download_ok - Checked at: 2026-04-23T16:43:11.935Z - Expires at: 2026-04-30T16:43:11.935Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/agent-orchestrator-molter-102) - [Send to Agent page](https://openagent3.xyz/skills/agent-orchestrator-molter-102/agent) - [JSON manifest](https://openagent3.xyz/skills/agent-orchestrator-molter-102/agent.json) - [Markdown brief](https://openagent3.xyz/skills/agent-orchestrator-molter-102/agent.md) - [Download page](https://openagent3.xyz/downloads/agent-orchestrator-molter-102)