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Swarm

Cut your LLM costs by 200x. Offload parallel, batch, and research work to Gemini Flash workers instead of burning your expensive primary model.

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High Signal

Cut your LLM costs by 200x. Offload parallel, batch, and research work to Gemini Flash workers instead of burning your expensive primary model.

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Download the package from Yavira.
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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: CHANGELOG.md, INSTALL.md, PUBLISHING.md, README.md, ROADMAP.md, SKILL.md

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.3.7

Provenance

Publisher: Chair4ce
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 24 sections Open source page

Swarm — Cut Your LLM Costs by 200x

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.

At a Glance

30 tasks viaTimeCostOpus (sequential)~30s~$0.50Swarm (parallel)~1s~$0.003

When to Use

Swarm is ideal for: 3+ independent tasks (research, summaries, comparisons) Comparing or researching multiple subjects Multiple URLs to fetch/analyze Batch processing (documents, entities, facts) Complex analysis needing multiple perspectives → use chain

Quick Reference

# Check daemon (do this every session) swarm status # Start if not running swarm start # Parallel prompts swarm parallel "What is X?" "What is Y?" "What is Z?" # Research multiple subjects swarm research "OpenAI" "Anthropic" "Mistral" --topic "AI safety" # Discover capabilities swarm capabilities

Parallel (v1.0)

N prompts → N workers simultaneously. Best for independent tasks. swarm parallel "prompt1" "prompt2" "prompt3"

Research (v1.1)

Multi-phase: search → fetch → analyze. Uses Google Search grounding. swarm research "Buildertrend" "Jobber" --topic "pricing 2026"

Chain (v1.3) — Refinement Pipelines

Data flows through multiple stages, each with a different perspective/filter. Stages run in sequence; tasks within a stage run in parallel. Stage modes: parallel — N inputs → N workers (same perspective) single — merged input → 1 worker fan-out — 1 input → N workers with DIFFERENT perspectives reduce — N inputs → 1 synthesized output Auto-chain — describe what you want, get an optimal pipeline: curl -X POST http://localhost:9999/chain/auto \ -d '{"task":"Find business opportunities","data":"...market data...","depth":"standard"}' Manual chain: swarm chain pipeline.json # or echo '{"stages":[...]}' | swarm chain --stdin Depth presets: quick (2 stages), standard (4), deep (6), exhaustive (8) Built-in perspectives: extractor, filter, enricher, analyst, synthesizer, challenger, optimizer, strategist, researcher, critic Preview without executing: curl -X POST http://localhost:9999/chain/preview \ -d '{"task":"...","depth":"standard"}'

Benchmark (v1.3)

Compare single vs parallel vs chain on the same task with LLM-as-judge scoring. curl -X POST http://localhost:9999/benchmark \ -d '{"task":"Analyze X","data":"...","depth":"standard"}' Scores on 6 FLASK dimensions: accuracy (2x weight), depth (1.5x), completeness, coherence, actionability (1.5x), nuance.

Capabilities Discovery (v1.3)

Lets the orchestrator discover what execution modes are available: swarm capabilities # or curl http://localhost:9999/capabilities

Prompt Cache (v1.3.2)

LRU cache for LLM responses. 212x speedup on cache hits (parallel), 514x on chains. Keyed by hash of instruction + input + perspective 500 entries max, 1 hour TTL Skips web search tasks (need fresh data) Persists to disk across daemon restarts Per-task bypass: set task.cache = false # View cache stats curl http://localhost:9999/cache # Clear cache curl -X DELETE http://localhost:9999/cache Cache stats show in swarm status.

Stage Retry (v1.3.2)

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.

Cost Tracking (v1.3.1)

All endpoints return cost data in their complete event: session — current daemon session totals daily — persisted across restarts, accumulates all day swarm status # Shows session + daily cost swarm savings # Monthly savings report

Web Search (v1.1)

Workers search the live web via Google Search grounding (Gemini only, no extra cost). # Research uses web search by default swarm research "Subject" --topic "angle" # Parallel with web search curl -X POST http://localhost:9999/parallel \ -d '{"prompts":["Current price of X?"],"options":{"webSearch":true}}'

JavaScript API

const { parallel, research } = require('~/clawd/skills/node-scaling/lib'); const { SwarmClient } = require('~/clawd/skills/node-scaling/lib/client'); // Simple parallel const result = await parallel(['prompt1', 'prompt2', 'prompt3']); // Client with streaming const client = new SwarmClient(); for await (const event of client.parallel(prompts)) { ... } for await (const event of client.research(subjects, topic)) { ... } // Chain const result = await client.chainSync({ task, data, depth });

Daemon Management

swarm start # Start daemon (background) swarm stop # Stop daemon swarm status # Status, cost, cache stats swarm restart # Restart daemon swarm savings # Monthly savings report swarm logs [N] # Last N lines of daemon log

Performance (v1.3.2)

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

Config

Location: ~/.config/clawdbot/node-scaling.yaml node_scaling: enabled: true limits: max_nodes: 16 max_concurrent_api: 16 provider: name: gemini model: gemini-2.0-flash web_search: enabled: true parallel_default: false cost: max_daily_spend: 10.00

Troubleshooting

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

Structured Output (v1.3.7)

Force JSON output with schema validation — zero parse failures on structured tasks. # With built-in schema curl -X POST http://localhost:9999/structured \ -d '{"prompt":"Extract entities from: Tim Cook announced iPhone 17","schema":"entities"}' # With custom schema curl -X POST http://localhost:9999/structured \ -d '{"prompt":"Classify this text","data":"...","schema":{"type":"object","properties":{"category":{"type":"string"}}}}' # JSON mode (no schema, just force JSON) curl -X POST http://localhost:9999/structured \ -d '{"prompt":"Return a JSON object with name, age, city for a fictional person"}' # List available schemas curl http://localhost:9999/structured/schemas Built-in schemas: entities, summary, comparison, actions, classification, qa Uses Gemini's native response_mime_type: application/json + responseSchema for guaranteed JSON output. Includes schema validation on the response.

Majority Voting (v1.3.7)

Same prompt → N parallel executions → pick the best answer. Higher accuracy on factual/analytical tasks. # Judge strategy (LLM picks best — most reliable) curl -X POST http://localhost:9999/vote \ -d '{"prompt":"What are the key factors in SaaS pricing?","n":3,"strategy":"judge"}' # Similarity strategy (consensus — zero extra cost) curl -X POST http://localhost:9999/vote \ -d '{"prompt":"What year was Python released?","n":3,"strategy":"similarity"}' # Longest strategy (heuristic — zero extra cost) curl -X POST http://localhost:9999/vote \ -d '{"prompt":"Explain recursion","n":3,"strategy":"longest"}' Strategies: judge — LLM scores all candidates on accuracy/completeness/clarity/actionability, picks winner (N+1 calls) similarity — Jaccard word-set similarity, picks consensus answer (N calls, zero extra cost) longest — Picks longest response as heuristic for thoroughness (N calls, zero extra cost) When to use: Factual questions, critical decisions, or any task where accuracy > speed. StrategyCallsExtra CostQualitysimilarityN$0Good (consensus)longestN$0Decent (heuristic)judgeN+1~$0.0001Best (LLM-scored)

Self-Reflection (v1.3.5)

Optional critic pass after chain/skeleton output. Scores 5 dimensions, auto-refines if below threshold. # Add reflect:true to any chain or skeleton request curl -X POST http://localhost:9999/chain/auto \ -d '{"task":"Analyze the AI chip market","data":"...","reflect":true}' curl -X POST http://localhost:9999/skeleton \ -d '{"task":"Write a market analysis","reflect":true}' Proven: improved weak output from 5.0 → 7.6 avg score. Skeleton + reflect scored 9.4/10.

Skeleton-of-Thought (v1.3.6)

Generate outline → expand each section in parallel → merge into coherent document. Best for long-form content. curl -X POST http://localhost:9999/skeleton \ -d '{"task":"Write a comprehensive guide to SaaS pricing","maxSections":6,"reflect":true}' Performance: 14,478 chars in 21s (675 chars/sec) — 5.1x more content than chain at 2.9x higher throughput. MetricChainSkeleton-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 When to use what: SoT → long-form content, reports, guides, docs (anything with natural sections) Chain → analysis, research, adversarial review (anything needing multiple perspectives) Parallel → independent tasks, batch processing Structured → entity extraction, classification, any task needing reliable JSON Voting → factual accuracy, critical decisions, consensus-building

API Endpoints

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

Cost Comparison

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 Cache hits are essentially free (~3-5ms, no API call).

Category context

Code helpers, APIs, CLIs, browser automation, testing, and developer operations.

Source: Tencent SkillHub

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Package contents

Included in package

6 Docs

SKILL.md Primary doc
CHANGELOG.md Docs
INSTALL.md Docs
PUBLISHING.md Docs
README.md Docs
ROADMAP.md Docs