{
  "schemaVersion": "1.0",
  "item": {
    "slug": "swarm",
    "name": "Swarm",
    "source": "tencent",
    "type": "skill",
    "category": "开发工具",
    "sourceUrl": "https://clawhub.ai/Chair4ce/swarm",
    "canonicalUrl": "https://clawhub.ai/Chair4ce/swarm",
    "targetPlatform": "OpenClaw"
  },
  "install": {
    "downloadMode": "redirect",
    "downloadUrl": "/downloads/swarm",
    "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=swarm",
    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "installMethod": "Manual import",
    "extraction": "Extract archive",
    "prerequisites": [
      "OpenClaw"
    ],
    "packageFormat": "ZIP package",
    "includedAssets": [
      "CHANGELOG.md",
      "INSTALL.md",
      "PUBLISHING.md",
      "README.md",
      "ROADMAP.md",
      "SKILL.md"
    ],
    "primaryDoc": "SKILL.md",
    "quickSetup": [
      "Download the package from Yavira.",
      "Extract the archive and review SKILL.md first.",
      "Import or place the package into your OpenClaw setup."
    ],
    "agentAssist": {
      "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
      "steps": [
        "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."
      ],
      "prompts": [
        {
          "label": "New install",
          "body": "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."
        },
        {
          "label": "Upgrade existing",
          "body": "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."
        }
      ]
    },
    "sourceHealth": {
      "source": "tencent",
      "status": "healthy",
      "reason": "direct_download_ok",
      "recommendedAction": "download",
      "checkedAt": "2026-05-07T17:22:31.273Z",
      "expiresAt": "2026-05-14T17:22:31.273Z",
      "httpStatus": 200,
      "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=afrexai-annual-report",
      "contentType": "application/zip",
      "probeMethod": "head",
      "details": {
        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=afrexai-annual-report",
        "contentDisposition": "attachment; filename=\"afrexai-annual-report-1.0.0.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/swarm"
    },
    "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."
      ]
    },
    "downloadPageUrl": "https://openagent3.xyz/downloads/swarm",
    "agentPageUrl": "https://openagent3.xyz/skills/swarm/agent",
    "manifestUrl": "https://openagent3.xyz/skills/swarm/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/swarm/agent.md"
  },
  "agentAssist": {
    "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
    "steps": [
      "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."
    ],
    "prompts": [
      {
        "label": "New install",
        "body": "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."
      },
      {
        "label": "Upgrade existing",
        "body": "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."
      }
    ]
  },
  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Swarm — Cut Your LLM Costs by 200x",
        "body": "Turn your expensive model into an affordable daily driver. Offload the boring stuff to Gemini Flash workers — parallel, batch, research — at a fraction of the cost."
      },
      {
        "title": "At a Glance",
        "body": "30 tasks viaTimeCostOpus (sequential)~30s~$0.50Swarm (parallel)~1s~$0.003"
      },
      {
        "title": "When to Use",
        "body": "Swarm is ideal for:\n\n3+ independent tasks (research, summaries, comparisons)\nComparing or researching multiple subjects\nMultiple URLs to fetch/analyze\nBatch processing (documents, entities, facts)\nComplex analysis needing multiple perspectives → use chain"
      },
      {
        "title": "Quick Reference",
        "body": "# Check daemon (do this every session)\nswarm status\n\n# Start if not running\nswarm start\n\n# Parallel prompts\nswarm parallel \"What is X?\" \"What is Y?\" \"What is Z?\"\n\n# Research multiple subjects\nswarm research \"OpenAI\" \"Anthropic\" \"Mistral\" --topic \"AI safety\"\n\n# Discover capabilities\nswarm capabilities"
      },
      {
        "title": "Parallel (v1.0)",
        "body": "N prompts → N workers simultaneously. Best for independent tasks.\n\nswarm parallel \"prompt1\" \"prompt2\" \"prompt3\""
      },
      {
        "title": "Research (v1.1)",
        "body": "Multi-phase: search → fetch → analyze. Uses Google Search grounding.\n\nswarm research \"Buildertrend\" \"Jobber\" --topic \"pricing 2026\""
      },
      {
        "title": "Chain (v1.3) — Refinement Pipelines",
        "body": "Data flows through multiple stages, each with a different perspective/filter. Stages run in sequence; tasks within a stage run in parallel.\n\nStage modes:\n\nparallel — N inputs → N workers (same perspective)\nsingle — merged input → 1 worker\nfan-out — 1 input → N workers with DIFFERENT perspectives\nreduce — N inputs → 1 synthesized output\n\nAuto-chain — describe what you want, get an optimal pipeline:\n\ncurl -X POST http://localhost:9999/chain/auto \\\n  -d '{\"task\":\"Find business opportunities\",\"data\":\"...market data...\",\"depth\":\"standard\"}'\n\nManual chain:\n\nswarm chain pipeline.json\n# or\necho '{\"stages\":[...]}' | swarm chain --stdin\n\nDepth presets: quick (2 stages), standard (4), deep (6), exhaustive (8)\n\nBuilt-in perspectives: extractor, filter, enricher, analyst, synthesizer, challenger, optimizer, strategist, researcher, critic\n\nPreview without executing:\n\ncurl -X POST http://localhost:9999/chain/preview \\\n  -d '{\"task\":\"...\",\"depth\":\"standard\"}'"
      },
      {
        "title": "Benchmark (v1.3)",
        "body": "Compare single vs parallel vs chain on the same task with LLM-as-judge scoring.\n\ncurl -X POST http://localhost:9999/benchmark \\\n  -d '{\"task\":\"Analyze X\",\"data\":\"...\",\"depth\":\"standard\"}'\n\nScores on 6 FLASK dimensions: accuracy (2x weight), depth (1.5x), completeness, coherence, actionability (1.5x), nuance."
      },
      {
        "title": "Capabilities Discovery (v1.3)",
        "body": "Lets the orchestrator discover what execution modes are available:\n\nswarm capabilities\n# or\ncurl http://localhost:9999/capabilities"
      },
      {
        "title": "Prompt Cache (v1.3.2)",
        "body": "LRU cache for LLM responses. 212x speedup on cache hits (parallel), 514x on chains.\n\nKeyed by hash of instruction + input + perspective\n500 entries max, 1 hour TTL\nSkips web search tasks (need fresh data)\nPersists to disk across daemon restarts\nPer-task bypass: set task.cache = false\n\n# View cache stats\ncurl http://localhost:9999/cache\n\n# Clear cache\ncurl -X DELETE http://localhost:9999/cache\n\nCache stats show in swarm status."
      },
      {
        "title": "Stage Retry (v1.3.2)",
        "body": "If tasks fail within a chain stage, only the failed tasks get retried (not the whole stage). Default: 1 retry. Configurable per-phase via phase.retries or globally via options.stageRetries."
      },
      {
        "title": "Cost Tracking (v1.3.1)",
        "body": "All endpoints return cost data in their complete event:\n\nsession — current daemon session totals\ndaily — persisted across restarts, accumulates all day\n\nswarm status        # Shows session + daily cost\nswarm savings       # Monthly savings report"
      },
      {
        "title": "Web Search (v1.1)",
        "body": "Workers search the live web via Google Search grounding (Gemini only, no extra cost).\n\n# Research uses web search by default\nswarm research \"Subject\" --topic \"angle\"\n\n# Parallel with web search\ncurl -X POST http://localhost:9999/parallel \\\n  -d '{\"prompts\":[\"Current price of X?\"],\"options\":{\"webSearch\":true}}'"
      },
      {
        "title": "JavaScript API",
        "body": "const { parallel, research } = require('~/clawd/skills/node-scaling/lib');\nconst { SwarmClient } = require('~/clawd/skills/node-scaling/lib/client');\n\n// Simple parallel\nconst result = await parallel(['prompt1', 'prompt2', 'prompt3']);\n\n// Client with streaming\nconst client = new SwarmClient();\nfor await (const event of client.parallel(prompts)) { ... }\nfor await (const event of client.research(subjects, topic)) { ... }\n\n// Chain\nconst result = await client.chainSync({ task, data, depth });"
      },
      {
        "title": "Daemon Management",
        "body": "swarm start              # Start daemon (background)\nswarm stop               # Stop daemon\nswarm status             # Status, cost, cache stats\nswarm restart            # Restart daemon\nswarm savings            # Monthly savings report\nswarm logs [N]           # Last N lines of daemon log"
      },
      {
        "title": "Performance (v1.3.2)",
        "body": "ModeTasksTimeNotesParallel (simple)5~700ms142ms/task effectiveParallel (stress)10~1.2s123ms/task effectiveChain (standard)5~14s3-stage multi-perspectiveChain (quick)2~3s2-stage extract+synthesizeCache hitany~3-5ms200-500x speedupResearch (web)2~15sGoogle grounding latency"
      },
      {
        "title": "Config",
        "body": "Location: ~/.config/clawdbot/node-scaling.yaml\n\nnode_scaling:\n  enabled: true\n  limits:\n    max_nodes: 16\n    max_concurrent_api: 16\n  provider:\n    name: gemini\n    model: gemini-2.0-flash\n  web_search:\n    enabled: true\n    parallel_default: false\n  cost:\n    max_daily_spend: 10.00"
      },
      {
        "title": "Troubleshooting",
        "body": "IssueFixDaemon not runningswarm startNo API keySet GEMINI_API_KEY or run npm run setupRate limitedLower max_concurrent_api in configWeb search not workingEnsure provider is gemini + web_search.enabledCache stale resultscurl -X DELETE http://localhost:9999/cacheChain too slowUse depth: \"quick\" or check context size"
      },
      {
        "title": "Structured Output (v1.3.7)",
        "body": "Force JSON output with schema validation — zero parse failures on structured tasks.\n\n# With built-in schema\ncurl -X POST http://localhost:9999/structured \\\n  -d '{\"prompt\":\"Extract entities from: Tim Cook announced iPhone 17\",\"schema\":\"entities\"}'\n\n# With custom schema\ncurl -X POST http://localhost:9999/structured \\\n  -d '{\"prompt\":\"Classify this text\",\"data\":\"...\",\"schema\":{\"type\":\"object\",\"properties\":{\"category\":{\"type\":\"string\"}}}}'\n\n# JSON mode (no schema, just force JSON)\ncurl -X POST http://localhost:9999/structured \\\n  -d '{\"prompt\":\"Return a JSON object with name, age, city for a fictional person\"}'\n\n# List available schemas\ncurl http://localhost:9999/structured/schemas\n\nBuilt-in schemas: entities, summary, comparison, actions, classification, qa\n\nUses Gemini's native response_mime_type: application/json + responseSchema for guaranteed JSON output. Includes schema validation on the response."
      },
      {
        "title": "Majority Voting (v1.3.7)",
        "body": "Same prompt → N parallel executions → pick the best answer. Higher accuracy on factual/analytical tasks.\n\n# Judge strategy (LLM picks best — most reliable)\ncurl -X POST http://localhost:9999/vote \\\n  -d '{\"prompt\":\"What are the key factors in SaaS pricing?\",\"n\":3,\"strategy\":\"judge\"}'\n\n# Similarity strategy (consensus — zero extra cost)\ncurl -X POST http://localhost:9999/vote \\\n  -d '{\"prompt\":\"What year was Python released?\",\"n\":3,\"strategy\":\"similarity\"}'\n\n# Longest strategy (heuristic — zero extra cost)\ncurl -X POST http://localhost:9999/vote \\\n  -d '{\"prompt\":\"Explain recursion\",\"n\":3,\"strategy\":\"longest\"}'\n\nStrategies:\n\njudge — LLM scores all candidates on accuracy/completeness/clarity/actionability, picks winner (N+1 calls)\nsimilarity — Jaccard word-set similarity, picks consensus answer (N calls, zero extra cost)\nlongest — Picks longest response as heuristic for thoroughness (N calls, zero extra cost)\n\nWhen to use: Factual questions, critical decisions, or any task where accuracy > speed.\n\nStrategyCallsExtra CostQualitysimilarityN$0Good (consensus)longestN$0Decent (heuristic)judgeN+1~$0.0001Best (LLM-scored)"
      },
      {
        "title": "Self-Reflection (v1.3.5)",
        "body": "Optional critic pass after chain/skeleton output. Scores 5 dimensions, auto-refines if below threshold.\n\n# Add reflect:true to any chain or skeleton request\ncurl -X POST http://localhost:9999/chain/auto \\\n  -d '{\"task\":\"Analyze the AI chip market\",\"data\":\"...\",\"reflect\":true}'\n\ncurl -X POST http://localhost:9999/skeleton \\\n  -d '{\"task\":\"Write a market analysis\",\"reflect\":true}'\n\nProven: improved weak output from 5.0 → 7.6 avg score. Skeleton + reflect scored 9.4/10."
      },
      {
        "title": "Skeleton-of-Thought (v1.3.6)",
        "body": "Generate outline → expand each section in parallel → merge into coherent document. Best for long-form content.\n\ncurl -X POST http://localhost:9999/skeleton \\\n  -d '{\"task\":\"Write a comprehensive guide to SaaS pricing\",\"maxSections\":6,\"reflect\":true}'\n\nPerformance: 14,478 chars in 21s (675 chars/sec) — 5.1x more content than chain at 2.9x higher throughput.\n\nMetricChainSkeleton-of-ThoughtWinnerOutput size2,856 chars14,478 charsSoT (5.1x)Throughput234 chars/sec675 chars/secSoT (2.9x)Duration12s21sChain (faster)Quality (w/ reflect)~7-8/109.4/10SoT\n\nWhen to use what:\n\nSoT → long-form content, reports, guides, docs (anything with natural sections)\nChain → analysis, research, adversarial review (anything needing multiple perspectives)\nParallel → independent tasks, batch processing\nStructured → entity extraction, classification, any task needing reliable JSON\nVoting → factual accuracy, critical decisions, consensus-building"
      },
      {
        "title": "API Endpoints",
        "body": "MethodPathDescriptionGET/healthHealth checkGET/statusDetailed status + cost + cacheGET/capabilitiesDiscover execution modesPOST/parallelExecute N prompts in parallelPOST/researchMulti-phase web researchPOST/skeletonSkeleton-of-Thought (outline → expand → merge)POST/chainManual chain pipelinePOST/chain/autoAuto-build + execute chainPOST/chain/previewPreview chain without executingPOST/chain/templateExecute pre-built templatePOST/structuredForced JSON with schema validationGET/structured/schemasList built-in schemasPOST/voteMajority voting (best-of-N)POST/benchmarkQuality comparison testGET/templatesList chain templatesGET/cacheCache statisticsDELETE/cacheClear cache"
      },
      {
        "title": "Cost Comparison",
        "body": "ModelCost per 1M tokensRelativeClaude Opus 4~$15 input / $75 output1xGPT-4o~$2.50 input / $10 output~7x cheaperGemini Flash~$0.075 input / $0.30 output200x cheaper\n\nCache hits are essentially free (~3-5ms, no API call)."
      }
    ],
    "body": "Swarm — Cut Your LLM Costs by 200x\n\nTurn your expensive model into an affordable daily driver. Offload the boring stuff to Gemini Flash workers — parallel, batch, research — at a fraction of the cost.\n\nAt a Glance\n30 tasks via\tTime\tCost\nOpus (sequential)\t~30s\t~$0.50\nSwarm (parallel)\t~1s\t~$0.003\nWhen to Use\n\nSwarm is ideal for:\n\n3+ independent tasks (research, summaries, comparisons)\nComparing or researching multiple subjects\nMultiple URLs to fetch/analyze\nBatch processing (documents, entities, facts)\nComplex analysis needing multiple perspectives → use chain\nQuick Reference\n# Check daemon (do this every session)\nswarm status\n\n# Start if not running\nswarm start\n\n# Parallel prompts\nswarm parallel \"What is X?\" \"What is Y?\" \"What is Z?\"\n\n# Research multiple subjects\nswarm research \"OpenAI\" \"Anthropic\" \"Mistral\" --topic \"AI safety\"\n\n# Discover capabilities\nswarm capabilities\n\nExecution Modes\nParallel (v1.0)\n\nN prompts → N workers simultaneously. Best for independent tasks.\n\nswarm parallel \"prompt1\" \"prompt2\" \"prompt3\"\n\nResearch (v1.1)\n\nMulti-phase: search → fetch → analyze. Uses Google Search grounding.\n\nswarm research \"Buildertrend\" \"Jobber\" --topic \"pricing 2026\"\n\nChain (v1.3) — Refinement Pipelines\n\nData flows through multiple stages, each with a different perspective/filter. Stages run in sequence; tasks within a stage run in parallel.\n\nStage modes:\n\nparallel — N inputs → N workers (same perspective)\nsingle — merged input → 1 worker\nfan-out — 1 input → N workers with DIFFERENT perspectives\nreduce — N inputs → 1 synthesized output\n\nAuto-chain — describe what you want, get an optimal pipeline:\n\ncurl -X POST http://localhost:9999/chain/auto \\\n  -d '{\"task\":\"Find business opportunities\",\"data\":\"...market data...\",\"depth\":\"standard\"}'\n\n\nManual chain:\n\nswarm chain pipeline.json\n# or\necho '{\"stages\":[...]}' | swarm chain --stdin\n\n\nDepth presets: quick (2 stages), standard (4), deep (6), exhaustive (8)\n\nBuilt-in perspectives: extractor, filter, enricher, analyst, synthesizer, challenger, optimizer, strategist, researcher, critic\n\nPreview without executing:\n\ncurl -X POST http://localhost:9999/chain/preview \\\n  -d '{\"task\":\"...\",\"depth\":\"standard\"}'\n\nBenchmark (v1.3)\n\nCompare single vs parallel vs chain on the same task with LLM-as-judge scoring.\n\ncurl -X POST http://localhost:9999/benchmark \\\n  -d '{\"task\":\"Analyze X\",\"data\":\"...\",\"depth\":\"standard\"}'\n\n\nScores on 6 FLASK dimensions: accuracy (2x weight), depth (1.5x), completeness, coherence, actionability (1.5x), nuance.\n\nCapabilities Discovery (v1.3)\n\nLets the orchestrator discover what execution modes are available:\n\nswarm capabilities\n# or\ncurl http://localhost:9999/capabilities\n\nPrompt Cache (v1.3.2)\n\nLRU cache for LLM responses. 212x speedup on cache hits (parallel), 514x on chains.\n\nKeyed by hash of instruction + input + perspective\n500 entries max, 1 hour TTL\nSkips web search tasks (need fresh data)\nPersists to disk across daemon restarts\nPer-task bypass: set task.cache = false\n# View cache stats\ncurl http://localhost:9999/cache\n\n# Clear cache\ncurl -X DELETE http://localhost:9999/cache\n\n\nCache stats show in swarm status.\n\nStage Retry (v1.3.2)\n\nIf tasks fail within a chain stage, only the failed tasks get retried (not the whole stage). Default: 1 retry. Configurable per-phase via phase.retries or globally via options.stageRetries.\n\nCost Tracking (v1.3.1)\n\nAll endpoints return cost data in their complete event:\n\nsession — current daemon session totals\ndaily — persisted across restarts, accumulates all day\nswarm status        # Shows session + daily cost\nswarm savings       # Monthly savings report\n\nWeb Search (v1.1)\n\nWorkers search the live web via Google Search grounding (Gemini only, no extra cost).\n\n# Research uses web search by default\nswarm research \"Subject\" --topic \"angle\"\n\n# Parallel with web search\ncurl -X POST http://localhost:9999/parallel \\\n  -d '{\"prompts\":[\"Current price of X?\"],\"options\":{\"webSearch\":true}}'\n\nJavaScript API\nconst { parallel, research } = require('~/clawd/skills/node-scaling/lib');\nconst { SwarmClient } = require('~/clawd/skills/node-scaling/lib/client');\n\n// Simple parallel\nconst result = await parallel(['prompt1', 'prompt2', 'prompt3']);\n\n// Client with streaming\nconst client = new SwarmClient();\nfor await (const event of client.parallel(prompts)) { ... }\nfor await (const event of client.research(subjects, topic)) { ... }\n\n// Chain\nconst result = await client.chainSync({ task, data, depth });\n\nDaemon Management\nswarm start              # Start daemon (background)\nswarm stop               # Stop daemon\nswarm status             # Status, cost, cache stats\nswarm restart            # Restart daemon\nswarm savings            # Monthly savings report\nswarm logs [N]           # Last N lines of daemon log\n\nPerformance (v1.3.2)\nMode\tTasks\tTime\tNotes\nParallel (simple)\t5\t~700ms\t142ms/task effective\nParallel (stress)\t10\t~1.2s\t123ms/task effective\nChain (standard)\t5\t~14s\t3-stage multi-perspective\nChain (quick)\t2\t~3s\t2-stage extract+synthesize\nCache hit\tany\t~3-5ms\t200-500x speedup\nResearch (web)\t2\t~15s\tGoogle grounding latency\nConfig\n\nLocation: ~/.config/clawdbot/node-scaling.yaml\n\nnode_scaling:\n  enabled: true\n  limits:\n    max_nodes: 16\n    max_concurrent_api: 16\n  provider:\n    name: gemini\n    model: gemini-2.0-flash\n  web_search:\n    enabled: true\n    parallel_default: false\n  cost:\n    max_daily_spend: 10.00\n\nTroubleshooting\nIssue\tFix\nDaemon not running\tswarm start\nNo API key\tSet GEMINI_API_KEY or run npm run setup\nRate limited\tLower max_concurrent_api in config\nWeb search not working\tEnsure provider is gemini + web_search.enabled\nCache stale results\tcurl -X DELETE http://localhost:9999/cache\nChain too slow\tUse depth: \"quick\" or check context size\nStructured Output (v1.3.7)\n\nForce JSON output with schema validation — zero parse failures on structured tasks.\n\n# With built-in schema\ncurl -X POST http://localhost:9999/structured \\\n  -d '{\"prompt\":\"Extract entities from: Tim Cook announced iPhone 17\",\"schema\":\"entities\"}'\n\n# With custom schema\ncurl -X POST http://localhost:9999/structured \\\n  -d '{\"prompt\":\"Classify this text\",\"data\":\"...\",\"schema\":{\"type\":\"object\",\"properties\":{\"category\":{\"type\":\"string\"}}}}'\n\n# JSON mode (no schema, just force JSON)\ncurl -X POST http://localhost:9999/structured \\\n  -d '{\"prompt\":\"Return a JSON object with name, age, city for a fictional person\"}'\n\n# List available schemas\ncurl http://localhost:9999/structured/schemas\n\n\nBuilt-in schemas: entities, summary, comparison, actions, classification, qa\n\nUses Gemini's native response_mime_type: application/json + responseSchema for guaranteed JSON output. Includes schema validation on the response.\n\nMajority Voting (v1.3.7)\n\nSame prompt → N parallel executions → pick the best answer. Higher accuracy on factual/analytical tasks.\n\n# Judge strategy (LLM picks best — most reliable)\ncurl -X POST http://localhost:9999/vote \\\n  -d '{\"prompt\":\"What are the key factors in SaaS pricing?\",\"n\":3,\"strategy\":\"judge\"}'\n\n# Similarity strategy (consensus — zero extra cost)\ncurl -X POST http://localhost:9999/vote \\\n  -d '{\"prompt\":\"What year was Python released?\",\"n\":3,\"strategy\":\"similarity\"}'\n\n# Longest strategy (heuristic — zero extra cost)\ncurl -X POST http://localhost:9999/vote \\\n  -d '{\"prompt\":\"Explain recursion\",\"n\":3,\"strategy\":\"longest\"}'\n\n\nStrategies:\n\njudge — LLM scores all candidates on accuracy/completeness/clarity/actionability, picks winner (N+1 calls)\nsimilarity — Jaccard word-set similarity, picks consensus answer (N calls, zero extra cost)\nlongest — Picks longest response as heuristic for thoroughness (N calls, zero extra cost)\n\nWhen to use: Factual questions, critical decisions, or any task where accuracy > speed.\n\nStrategy\tCalls\tExtra Cost\tQuality\nsimilarity\tN\t$0\tGood (consensus)\nlongest\tN\t$0\tDecent (heuristic)\njudge\tN+1\t~$0.0001\tBest (LLM-scored)\nSelf-Reflection (v1.3.5)\n\nOptional critic pass after chain/skeleton output. Scores 5 dimensions, auto-refines if below threshold.\n\n# Add reflect:true to any chain or skeleton request\ncurl -X POST http://localhost:9999/chain/auto \\\n  -d '{\"task\":\"Analyze the AI chip market\",\"data\":\"...\",\"reflect\":true}'\n\ncurl -X POST http://localhost:9999/skeleton \\\n  -d '{\"task\":\"Write a market analysis\",\"reflect\":true}'\n\n\nProven: improved weak output from 5.0 → 7.6 avg score. Skeleton + reflect scored 9.4/10.\n\nSkeleton-of-Thought (v1.3.6)\n\nGenerate outline → expand each section in parallel → merge into coherent document. Best for long-form content.\n\ncurl -X POST http://localhost:9999/skeleton \\\n  -d '{\"task\":\"Write a comprehensive guide to SaaS pricing\",\"maxSections\":6,\"reflect\":true}'\n\n\nPerformance: 14,478 chars in 21s (675 chars/sec) — 5.1x more content than chain at 2.9x higher throughput.\n\nMetric\tChain\tSkeleton-of-Thought\tWinner\nOutput size\t2,856 chars\t14,478 chars\tSoT (5.1x)\nThroughput\t234 chars/sec\t675 chars/sec\tSoT (2.9x)\nDuration\t12s\t21s\tChain (faster)\nQuality (w/ reflect)\t~7-8/10\t9.4/10\tSoT\n\nWhen to use what:\n\nSoT → long-form content, reports, guides, docs (anything with natural sections)\nChain → analysis, research, adversarial review (anything needing multiple perspectives)\nParallel → independent tasks, batch processing\nStructured → entity extraction, classification, any task needing reliable JSON\nVoting → factual accuracy, critical decisions, consensus-building\nAPI Endpoints\nMethod\tPath\tDescription\nGET\t/health\tHealth check\nGET\t/status\tDetailed status + cost + cache\nGET\t/capabilities\tDiscover execution modes\nPOST\t/parallel\tExecute N prompts in parallel\nPOST\t/research\tMulti-phase web research\nPOST\t/skeleton\tSkeleton-of-Thought (outline → expand → merge)\nPOST\t/chain\tManual chain pipeline\nPOST\t/chain/auto\tAuto-build + execute chain\nPOST\t/chain/preview\tPreview chain without executing\nPOST\t/chain/template\tExecute pre-built template\nPOST\t/structured\tForced JSON with schema validation\nGET\t/structured/schemas\tList built-in schemas\nPOST\t/vote\tMajority voting (best-of-N)\nPOST\t/benchmark\tQuality comparison test\nGET\t/templates\tList chain templates\nGET\t/cache\tCache statistics\nDELETE\t/cache\tClear cache\nCost Comparison\nModel\tCost per 1M tokens\tRelative\nClaude Opus 4\t~$15 input / $75 output\t1x\nGPT-4o\t~$2.50 input / $10 output\t~7x cheaper\nGemini Flash\t~$0.075 input / $0.30 output\t200x cheaper\n\nCache hits are essentially free (~3-5ms, no API call)."
  },
  "trust": {
    "sourceLabel": "tencent",
    "provenanceUrl": "https://clawhub.ai/Chair4ce/swarm",
    "publisherUrl": "https://clawhub.ai/Chair4ce/swarm",
    "owner": "Chair4ce",
    "version": "1.3.7",
    "license": null,
    "verificationStatus": "Indexed source record"
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/swarm",
    "downloadUrl": "https://openagent3.xyz/downloads/swarm",
    "agentUrl": "https://openagent3.xyz/skills/swarm/agent",
    "manifestUrl": "https://openagent3.xyz/skills/swarm/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/swarm/agent.md"
  }
}