swarm

What This Skill Does

Swarm is a high-performance orchestration engine designed to drastically reduce LLM operational costs while increasing throughput. By offloading parallel, batch, and multi-step research tasks to Gemini Flash workers, Swarm effectively bypasses the high latency and expense of calling primary models like Claude 3.5 Sonnet or GPT-4 for trivial or concurrent sub-tasks. It acts as a middleware orchestrator that transforms a single complex request into a distributed pipeline, enabling users to achieve results up to 200x more cost-effectively compared to sequential execution.

Installation

To integrate Swarm into your environment, utilize the OpenClaw skill registry:

1. Ensure your daemon is ready by running swarm status.
2. Execute the install command: clawhub install openclaw/skills/skills/chair4ce/node-scaling.
3. Verify the installation via the CLI by running swarm capabilities to list the currently enabled execution modes.

Use Cases

Swarm excels in scenarios requiring heavy lifting where single-model chains would be bottlenecked by time and cost. Ideal use cases include:

• Large-scale Research: Simultaneously fetching and synthesizing information from multiple URLs or data sources.
• Batch Processing: Running standardized analysis over hundreds of documents, entity extractions, or sentiment benchmarks.
• Complex Refinement Pipelines: Using the 'Chain' feature to pass data through varied perspectives (e.g., extractor -> analyst -> critic) to ensure high-fidelity outputs.
• Market Analysis: Comparing multiple subjects against specific criteria to generate comprehensive reports.

Example Prompts

1. "Swarm, research the pricing models of the top 5 CRM platforms for 2026 and summarize the findings in a comparison table."
2. "Use swarm parallel to extract the key action items from these 10 meeting transcripts simultaneously."
3. "Execute a standard depth chain to analyze this market dataset: identify the top 3 growth opportunities and provide a critique of the risks."

Tips & Limitations

• Depth Presets: Start with 'quick' or 'standard' depth for most tasks. Only use 'exhaustive' for critical, high-stakes decision-making to avoid unnecessary token usage.
• Monitoring: Always use swarm status before starting a massive batch job to ensure the local daemon is stable.
• Limitations: Swarm is optimized for independent tasks. Tasks requiring strict temporal dependency (where step 2 requires the full output context of step 1) should be handled via the Chain module, which has a higher overhead than simple parallel execution.