What problem does it solve?
This Skill eliminates the inefficiency of manually aggregating fragmented, single-source research for complex queries, which often leads to missed insights, contradictory information, and incomplete analysis. It automates the end-to-end research workflow from query decomposition to cross-validated synthesis, ensuring comprehensive coverage of all relevant domains without requiring the user to coordinate multiple research tools or agents manually.
Core Features & Use Cases
- Parallel Multi-Domain Orchestration: Automatically decomposes research queries into up to 5 parallel specialized streams covering social media discourse, code/ML ecosystems, academic publications, market intelligence, and general web sources.
- Cross-Domain Signal Validation: Identifies convergent high-confidence findings, unique leading indicators, and explicit contradictions across sources to produce honest, well-supported analysis.
- Structured Output with Quality Scoring: Generates standardized intelligence briefs with executive summaries, key findings, signal matrices, and timelines, plus an automated 0-10 quality score to assess output reliability.
- Persistent Memory Storage: Automatically saves research outputs to MemOS for cross-session recall by other agents or team members.
- Use Case: For a product manager researching "current state of open-source RAG frameworks", this skill coordinates researchers across GitHub, arXiv, Reddit, and industry news to produce a single decision-ready brief in 10-15 minutes instead of hours of manual source aggregation.
Quick Start
Use the research-coordinator skill to produce a comprehensive intelligence brief on the latest developments in open-source RAG frameworks, covering technical ecosystem updates, community sentiment, recent academic research, and industry adoption trends.
