Skill: variable190/agent-orchestrator-molter (version 1.0.5)

--- name: agent-orchestrator version: 1.0.5 author: molter-white description: Multi-agent orchestration with 5 proven patterns - Work Crew, Supervisor, Pipeline, Council, and Auto-Routing license: MIT tags: multi-agent,orchestration,automation,productivity,ai-workflow compatibility: OpenClaw 0.8+ --- # 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 | Pattern | Use When | Avoid When | |---------|----------|------------| | **crew** | Same task from multiple angles, verification, research breadth | Results cannot be easily compared/merged | | **supervise** | Dynamic decomposition needed, complex planning | Fixed workflow, simple delegation | | **pipeline** | Well-defined sequential stages, content creation | Path needs runtime adaptation | | **council** | Cross-domain expertise, risk assessment, policy review | Single-domain task, need fast consensus | | **route** | Mixed workload types, automatic classification | Task type is already known | ## Auto-Routing Pattern The route command analyzes tasks and automatically classifies them by type, then routes to the appropriate specialist: ```bash # 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 ```bash # 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** ```bash # 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** ```bash # 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** ```bash # 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** ```bash # 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** - [x] Supervisor pattern implemented - dynamic task decomposition and worker delegation - [x] Pipeline pattern implemented - sequential staged processing with validation gates - [x] Council pattern implemented - multi-expert deliberation with convergence methods - [x] Route pattern implemented - intelligent task classification and specialist routing ## References - Anthropic Multi-Agent Research System - LangGraph Supervisor Pattern - CrewAI Framework - AutoGen Conversational Agents


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)