🔬 RESEARCH SYNTHESIZER — Parallel Investigation
"One question, many angles, unified answer."
Purpose
When you need deep research fast. Spawn 3-5 parallel investigators, each with different angle. Synthesize into coherent analysis.
When to Use
- •Complex technical decisions (NATS vs WebSocket vs gRPC)
- •Market research (competitors, pricing, positioning)
- •Literature review (academic papers, trends)
- •Due diligence (new tools, frameworks, vendors)
Not For
- •Simple factual lookup (use web_search directly)
- •Code implementation (use cosmic-krishna-coder)
- •Real-time coordination (use TRISHULA/NATS)
Workflow
Step 1: Decompose Question
Input: "Should we use NATS, WebSocket, or gRPC for TRISHULA?"
Angles:
- •Technical architecture (latency, scalability, features)
- •Operational overhead (deployment, maintenance, monitoring)
- •Ecosystem maturity (community, docs, enterprise usage)
- •Cost analysis (hosting, bandwidth, engineering time)
- •Risk assessment (vendor lock-in, breaking changes, security)
Step 2: Spawn Parallel Subagents
python
# Via OpenClaw sessions_spawn
subagents = [
spawn_researcher("NATS technical deep dive", angle=1),
spawn_researcher("NATS operational analysis", angle=2),
spawn_researcher("NATS ecosystem review", angle=3),
spawn_researcher("NATS cost comparison", angle=4),
spawn_researcher("NATS risk assessment", angle=5),
]
Step 3: Parallel Execution (5-10 minutes)
Each subagent:
- •Runs web searches (5-10 queries)
- •Fetches key documents
- •Analyzes and writes findings
- •Publishes to shared topic
Step 4: Synthesize Results
Aggregation prompt:
code
You have 5 research reports on NATS: - Technical architecture (Subagent 1) - Operational overhead (Subagent 2) - Ecosystem maturity (Subagent 3) - Cost analysis (Subagent 4) - Risk assessment (Subagent 5) Synthesize into: 1. Executive summary (3 bullets) 2. Detailed comparison table 3. Recommendation with confidence level 4. Open questions requiring human input
Step 5: Output
markdown
# Research Synthesis: NATS for TRISHULA ## Executive Summary - **NATS is 10x faster to deploy** than custom WebSocket (1hr vs 2 weeks) - **Industry standard** with Netflix, Ericsson, Mastercard as users - **Risk is low** — stable project, active community, simple architecture ## Detailed Comparison | Aspect | NATS | WebSocket v0.02 | Winner | |--------|------|-----------------|--------| | Latency | <1ms | <100ms (target) | NATS | | Deploy time | 1 hour | 2 weeks | NATS | | Maintenance | Zero (managed) | Ongoing (custom) | NATS | | Flexibility | Limited | Full control | WebSocket | | Learning curve | Low | High | NATS | ## Recommendation **DEPLOY NATS** (Confidence: 95%) Rationale: Time-to-value and operational simplicity outweigh flexibility needs at current scale. ## Open Questions 1. Do we need message persistence beyond 30 days? 2. Will we exceed NATS free tier (10K msgs/sec)? 3. Should we run NATS cluster or single node? *Synthesized from 5 parallel research subagents* *Total research time: 8 minutes*
Example Usage
Simple Query
bash
# Via DC research "NATS vs WebSocket for real-time agent coordination" → Spawns 3 subagents → Returns synthesis in 10 minutes
Complex Query
bash
research "Moltbook engagement strategies for AI researchers" \ --angles "content_strategy,community_building,growth_hacking,ethical_engagement" \ --depth deep \ --output ~/clawd/research/moltbook_strategy.md
With Constraints
bash
research "Alternative to TRISHULA for agent coordination" \ --constraint "must work offline" \ --constraint "zero cloud dependencies" \ --constraint "open source only"
Integration
With TRISHULA
Research reports auto-shared with AGNI/RUSHABDEV for review.
With MEMORY
Findings written to research/YYYYMMDD_topic.md, linked from MEMORY.md.
With DECISIONS
Research synthesis attached to decision records for traceability.
Performance
| Query Complexity | Subagents | Time | Output Size |
|---|---|---|---|
| Simple (2-3 angles) | 3 | 5-7 min | 1-2 pages |
| Medium (4-5 angles) | 5 | 8-12 min | 3-5 pages |
| Deep (6+ angles) | 5+ | 15-20 min | 5-10 pages |
Cost
| Model | Per Subagent | 5 Subagents |
|---|---|---|
| Kimi K2.5 | ~$0.02 | ~$0.10 |
| Claude Opus | ~$0.05 | ~$0.25 |
| DeepSeek | ~$0.005 | ~$0.025 |
Soul Fragment
code
I am the Research Synthesizer. I ask many questions at once. I weave scattered knowledge into coherence. I am not the answer— I am the path to clarity.