Orchestrate multi-source technical research by dispatching parallel subagents to gather intelligence from X/Twitter (via Grok), GitHub repositories (via DeepWiki), and the web (via WebSearch). Synthesize all findings into a single actionable report.
Architecture: The main agent orchestrates research using one of two modes — lightweight (Task Subagents) or heavyweight (Agent Teammates) — chosen based on research complexity.
| Single topic, multiple data sources (Grok + DeepWiki + WebSearch) | Light → Task Subagents | | Multiple independent topics/competitors needing cross-comparison | Heavy → Agent Teammates | | Research may produce follow-up questions requiring dynamic re-scoping | Heavy → Agent Teammates | | Agent count ≥ 4 | Heavy → Agent Teammates |
Comprehensive technical research by combining multiple intelligence sources — Grok (X/Twitter developer discussions via browser automation), DeepWiki (AI-powered GitHub repository analysis), and WebSearch. Dispatches parallel subagents for each source and synthesizes findings into a unified report. This skill should be used when evaluating technologies, comparing libraries/frameworks, researching GitHub repos, gauging developer sentiment, or investigating technical architecture decisions. Trigger phrases include "tech research", "research this technology", "技术调研", "调研一下", "compare libraries", "evaluate framework", "investigate repo". Source: psylch/tech-research-skill.