What problem does it solve?
Manually collecting and verifying research from scattered internal codebases, documentation, stakeholder transcripts, and external web sources is slow, inconsistent, and often misses cross-source contradictions or unsubstantiated claims. This Skill automates the end-to-end research gathering workflow to deliver raw, cited findings with confidence scoring and structured verification.
Core Features & Use Cases
- Automatic research type classification: Detects if a question requires internal, external, or mixed research to trigger the correct source gathering strategy.
- Parallel subagent delegation: Launches specialized gatherer agents for different source categories (codebase, docs, web, transcripts) to collect evidence in parallel.
- Cross-source verification: Identifies contradictions between sources, tags declarative conclusions from stakeholder inputs, and produces confidence-scoped findings.
- Structured output generation: Creates raw findings, rejected information logs, cross-source verification reports, and actor-tailored views for different stakeholders.
- Use case: A product team investigating checkout flow issues can use this Skill to collect internal code, ticket, and user transcript data alongside external competitor and industry standard information, then verify consistency across all sources.
Quick Start
Use the research-gatherer skill to collect and cross-verify raw research findings for your question about internal system issues, competitor practices, or industry standards.
