What problem does it solve?
Large collections of Feishu (Lark) meeting notes and community minutes become dormant knowledge: newcomers do not know where to start, valuable content is rarely revisited, and mentors spend hours re-reading materials to prepare. Study Reviver converts scattered docs into structured knowledge cards and curated learning paths so community knowledge becomes discoverable, reusable, and easy to share.
Core Features & Use Cases
- Interactive quiz intake: a 3–4 question conversational quiz learns the user's goal, level, time budget and style before searching.
- Feishu-native search and assembly: runs lark-cli docs +search (with fallback to wiki node traversal) to find, rank, and group articles by difficulty and recency, then writes a formatted Feishu document with original links.
- Community scenarios: onboarding learning paths for new members, mentor prep outlines, weekly quiz generation and daily card broadcasts, and cross-period topic synthesis (e.g., RAG evolution).
- Operational guarantees: search fallback, result cleaning, and an automatic document creation step to persist and share the curated path.
Quick Start
Tell your local Agent: Help me find WaytoAGI/Feishu documents about "共学" or "读书会", extract key points into Feishu Base, and generate a beginner→advanced learning path document containing original links.
