What problem does it solve? Large features often require extensive codebase exploration that consumes most of an AI agent's context window, leaving no room to actually implement. This Skill caches that exploration into a persistent research document so implementation conversations start with full context instead of re-exploring from scratch. ## Core Features & Use Cases - Parallel Sub-Agent Exploration: Decomposes a research topic into 3-6 independent areas and spawns dedicated sub-agents to investigate each one concurrently. - Structured Research Documents: Synthesizes findings into working/research/<topic>.md with sections for summary, architecture, constraints, dependencies, open questions, and recommendations. - Lifecycle Management: Reuses research files under 7 days old, re-validates stale ones, and promotes lasting findings to docs/research/ before cleanup. - Use Case: Before building a new authentication system, run research to map existing auth code paths, external dependencies, and API constraints, then hand the document to the implementation conversation. ## Quick Start Ask the agent to research the authentication flow before building the new login feature.