| name | multi-model-research |
| description | Orchestrate multiple frontier LLMs (Claude, GPT-5.1, Gemini 3.0 Pro, Perplexity Sonar, Grok 4.1) for comprehensive research using LLM Council pattern with peer review and synthesis |
| triggers | ["deep dive","research council","multi-model research","comprehensive research","council research"] |
| allowed-tools | Bash, Read, mcp__obsidian-vault__create_vault_file |
| version | 0.1.0 |
Multi-Model Research Agent
Implements Karpathy's LLM Council pattern for superior research through parallel queries, peer review, and chairman synthesis.
Architecture
Geoffrey/Claude (Native Council Member):
- Routes simple vs complex queries
- Calls external API orchestrator (
research.py)
- Provides my own research response
- Conducts peer review phase
- Requests GPT-5.1 synthesis (chairman)
- Saves final report to Obsidian
Python External API Orchestrator:
- Fetches responses from GPT-5.1, Gemini 3.0 Pro, Perplexity Sonar, Grok 4.1
- Returns JSON with all external responses
- I handle all orchestration and synthesis
When to Use This Skill
Use multi-model research when:
- Complex analysis needed - Multiple perspectives valuable
- Factual verification critical - Cross-model validation
- Comprehensive coverage required - No single model sufficient
- Current information essential - Perplexity provides web grounding
- Contested topics - Benefit from diverse model perspectives
Simple vs Council Mode
Simple Mode (Perplexity only):
- Factual lookups
- Current events
- Quick research with citations
- Completes in <15 seconds
Council Mode (Full council):
- Comparative analysis
- Deep research
- Multiple perspectives needed
- Strategic questions
- Completes in <90 seconds
Workflow
Simple Query
User: "What are the latest developments in quantum computing?"
↓
I decide: Simple query (factual, current)
↓
I call: uv run scripts/research.py --query "..." --models perplexity
↓
I read: JSON response from Perplexity
↓
I format: Markdown report with citations
↓
I save: To Obsidian Geoffrey/Research folder
↓
I return: Summary to user with Obsidian link
Council Query
User: "Compare the AI strategies of OpenAI, Anthropic, and Google"
↓
I decide: Council query (comparative, complex)
↓
I call: uv run scripts/research.py --query "..." --models gpt,gemini,perplexity,grok
↓
I read: JSON with all external responses
↓
I provide: My own (Claude) research response
↓
I conduct: Peer review (each model ranks others)
↓
I request: GPT-5.1 chairman synthesis
↓
I format: Comprehensive markdown report
↓
I save: To Obsidian Geoffrey/Research folder
↓
I return: Summary with Obsidian link
Output Format
All research reports saved to Obsidian include:
- Executive Summary (2-3 paragraphs)
- Key Findings (organized by theme, inline citations)
- Confidence Assessment (what's certain vs debated)
- References Section (all sources with URLs and dates)
Citations use numeric format: [1], [2], etc.
Technical Details
Python Script:
cd skills/multi-model-research
uv run scripts/research.py --query "Your question" --models perplexity --output /tmp/responses.json
Config:
config.yaml - Model settings, routing rulesprompts/system_prompts.yaml - Per-model system promptsprompts/peer_review.md - Peer review templateprompts/chairman_synthesis.md - GPT-5.1 synthesis template
Dependencies:
- httpx (async HTTP client)
- pyyaml (config parsing)
- python-dotenv (env vars)
- python-frontmatter (Obsidian frontmatter)
API Keys Required:
- OPENAI_API_KEY (GPT-5.1)
- GEMINI_API_KEY (Gemini 3.0 Pro)
- PERPLEXITY_API_KEY (Sonar Pro)
- XAI_API_KEY (Grok 4.1)
All keys configured in ~/.env file.
Examples
Simple Research:
User: "What is RAG in AI?"
I route to: Simple mode (Perplexity)
Output: Concise explanation with current examples and citations
Time: ~10 seconds
Council Research:
User: "Compare serverless vs containers for production ML workloads"
I route to: Council mode (all 4 external + me)
Process:
1. GPT-5.1: Provides comprehensive technical comparison
2. Gemini 3.0: Analyzes cost and performance trade-offs
3. Perplexity: Current industry trends and case studies
4. Grok 4.1: Developer sentiment from X/Twitter
5. Claude (me): Synthesize with nuanced analysis
6. Peer review: Each model ranks others
7. GPT-5.1 (chairman): Final synthesis
Output: Multi-perspective analysis with citations
Time: ~60 seconds
Limitations
- Cost: Council mode uses 4-5 API calls per query
- Latency: Council mode takes 60-90 seconds
- API Limits: Rate limits may throttle parallel requests
- Citation Quality: Non-Perplexity models require URL extraction
Future Enhancements
- Streaming responses during deliberation
- Cost tracking and budget limits
- Query history and versioning
- Custom model weights based on topic
- Integration with Geoffrey's knowledge base
This skill implements Karpathy's LLM Council pattern released November 22, 2025.
