Index knowledge by topic, date, and utility for rapid retrieval.
npx skills add https://github.com/hiyenwong/ai_collection --skill indexed-memory
Please help me install this Agent Skill. Skill: indexed-memory Source: https://github.com/hiyenwong/ai_collection/tree/main/collection/skills/indexed-memory Command: npx skills add https://github.com/hiyenwong/ai_collection --skill indexed-memory
知识库规模不断扩大时，查找相关信息变得低效，重复学习成本上升，历史知识的复用性下降。Indexed Memory 旨在通过对知识进行主题、日期和效用的索引，将检索效率提升到新的水平，帮助用户快速定位、复用和回顾信息。
请让 AI 将你的笔记按主题和日期建立索引，以便快速检索相关记忆。
High-intent search queries and answers about installing and using this skill.
To index and retrieve personal knowledge by topic, you structure your notes with frontmatter fields like name, description, and model, organized into knowledge/, arxiv/, and daily/ subpaths. This enables rapid topic-based search and recall across large knowledge bases.
Searching large research notes by date and utility requires a structured index that groups knowledge entries by timeline and usefulness. This approach reduces repeated learning and accelerates recall for academic research notes and project histories.
Yes, topic-based memory retrieval requires frontmatter fields including name, description, model, and tools. These fields ensure consistent discovery and context loading across the knowledge/, arxiv/, and daily/ subpaths within your structured index.
Yes, indexed memory works for project histories and daily logs by organizing entries under the daily/ subpath. It indexes knowledge by topic and timeline, allowing quick recall of project milestones and historical decisions without repeated learning.
Knowledge indexing reduces repeated learning costs by tagging entries with topic, date, and utility metadata. This allows rapid retrieval of existing knowledge fragments, eliminating the need to re-research previously documented information in large personal knowledge bases.
Topic-based memory retrieval requires a predefined structured index with specific subpaths and frontmatter fields. Without consistent metadata organization across knowledge/, arxiv/, and daily/ directories, discovery and context loading may fail to locate relevant knowledge fragments.
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