Skill

geepers-dream-swarm

Launch parallel multi-domain search workflows using the Dream Swarm orchestrator. Use when (1) searching across multiple data sources simultaneously, (2) aggregating information from APIs, databases, and web sources, (3) comparing findings across domains, (4) rapid parallel data gathering. Requires the MCP server to be running.

From geepers-mcp
Install
1
Run in your terminal
$
npx claudepluginhub lukeslp/geepers-mcp --plugin geepers-mcp
Tool Access

This skill uses the workspace's default tool permissions.

Supporting Assets
View in Repository
scripts/swarm-search.py
Skill Content

Dream Swarm Search Skill

Launch parallel multi-agent search workflows across multiple domains.

Architecture: Parallel Domain Agents

              ┌────────────────────────┐
              │     SEARCH QUERY       │
              │  "AI safety research"  │
              └───────────┬────────────┘
                          │
        ┌─────────────────┼─────────────────┐
        │                 │                 │
   ┌────▼────┐       ┌────▼────┐       ┌────▼────┐
   │  arXiv  │       │ GitHub  │       │  News   │
   │  Agent  │       │  Agent  │       │  Agent  │
   └────┬────┘       └────┬────┘       └────┬────┘
        │                 │                 │
        └─────────────────┼─────────────────┘
                          │
              ┌───────────▼───────────┐
              │    AGGREGATOR         │
              │  Dedupe & Synthesize  │
              └───────────────────────┘

When to Use

  • Multi-source search across different APIs
  • Comparative analysis from diverse sources
  • Rapid information gathering with parallelization
  • Fact-checking against multiple sources
  • Research aggregation from academic + news + code

Available Domains (17 sources)

DomainDescriptionExample Query
arxivAcademic papers"quantum computing"
githubCode repositories"react components"
newsRecent articles"AI regulation 2024"
wikipediaEncyclopedia"machine learning"
pubmedMedical literature"CRISPR therapy"
semantic_scholarAcademic index"transformer architectures"
censusUS demographics"population density"
nasaSpace/Earth data"mars rover"
youtubeVideo content"tutorial python"
weatherForecasts"Seattle forecast"
openlibraryBook metadata"science fiction"
fecCampaign finance"2024 donations"
judiciaryCourt records"antitrust case"
archiveWayback Machine"historical websites"
financeMarket data"AAPL stock"
malAnime database"studio ghibli"
wolframComputation"integral sin(x)"

Scripts

Start Search Workflow

scripts/swarm-search.py "AI safety research" --domains arxiv github news
scripts/swarm-search.py "climate change" --agents 10 --parallel 5
scripts/swarm-search.py "quantum computing" --all-domains

Check Search Status

scripts/swarm-status.py workflow-xyz789
scripts/swarm-status.py workflow-xyz789 --results

Configuration Options

ParameterDefaultDescription
--domainsarxiv,newsComma-separated domain list
--agents5Number of parallel agents (1-20)
--parallel3Max concurrent domain searches (1-10)
--providerxaiLLM provider for synthesis
--max-results10Results per domain
--streamfalseEnable SSE streaming
--outputstdoutOutput format (stdout, json, markdown)

Example Workflows

Academic + Code Search

scripts/swarm-search.py "WebAssembly optimization" \
  --domains arxiv,github,semantic_scholar \
  --agents 6 --parallel 3

News + Social Monitoring

scripts/swarm-search.py "AI regulation policy" \
  --domains news,wikipedia,youtube \
  --agents 5 --output markdown

Comprehensive Research

scripts/swarm-search.py "CRISPR gene therapy" \
  --domains arxiv,pubmed,news,wikipedia \
  --agents 8 --parallel 4 --stream

Output Structure

{
  "task_id": "swarm-xyz789",
  "status": "completed",
  "results": {
    "arxiv": [
      {"title": "...", "url": "...", "abstract": "..."}
    ],
    "github": [
      {"name": "...", "url": "...", "description": "..."}
    ],
    "news": [
      {"title": "...", "url": "...", "source": "..."}
    ]
  },
  "synthesis": {
    "summary": "...",
    "key_themes": [...],
    "cross_domain_insights": [...]
  },
  "metadata": {
    "domains_searched": 3,
    "total_results": 24,
    "execution_time": 12.5
  }
}

Advantages over Dream Cascade

AspectDream SwarmDream Cascade
SpeedFaster (parallel)Slower (hierarchical)
DepthBroad coverageDeep analysis
Use caseData gatheringResearch synthesis
Agent countMany simple agentsFewer complex agents
CostLowerHigher

Prerequisites

  • MCP server running on port 5060
  • Valid API keys for data sources
  • Python 3.10+ with requests library

Troubleshooting

"Domain not available": Check API key configured for that domain

"Rate limited": Reduce --parallel count

"No results": Try broader query or different domains

Related Skills

  • dream-cascade - Hierarchical deep research
  • data-fetch - Single domain direct access
  • code-quality - Code-specific searches
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