Index and search markdown and text files using SQLite FTS5.
npx skills add https://github.com/mrap/hexagon-base --skill memory-mrap
Please help me install this Agent Skill. Skill: memory Source: https://github.com/mrap/hexagon-base/tree/main/dot-claude/skills/memory Command: npx skills add https://github.com/mrap/hexagon-base --skill memory-mrap
This Skill provides a persistent, searchable memory for the AI agent, allowing it to recall past conversations, decisions, and project details, ensuring context is never lost.
Use the memory skill to search for information about the last project meeting.
High-intent search queries and answers about installing and using this skill.
To give an AI agent searchable memory, you can index all markdown and text files within its directory using a local SQLite FTS5 database. This enables full-text search queries to recall past context, decisions, and project history.
Full-text search indexing works by processing markdown and text files through Python scripts into a local SQLite FTS5 database. This allows the agent to quickly retrieve specific past interactions and project details through keyword queries.
Yes, you can incrementally update the context index without reindexing everything. The Python scripts support incremental updates for new information, while also offering a full reindexing option when a complete database rebuild is needed.
The best way to retrieve project history and people details is by querying the local SQLite FTS5 database. The agent searches its indexed markdown and text files to find and recall relevant past context and specific decisions.
Yes, you need Python to set up this local SQLite full-text search for agent recall. The memory skill relies on Python scripts to handle both the indexing of markdown files and the execution of search queries.
An agent loses context and forgets past decisions because it lacks persistent memory. Indexing markdown files into a local SQLite database solves this by providing a searchable history of past interactions, decisions, and project details.
IOchairIndexes source code into SQLite and extracts snippets via SQL+FTS5 searches.
AbildtoftSearch a local SQLite learnings database using BM25 full-text queries.
JamesFincherRead, list, search, and create Markdown notes in an Obsidian vault.
BEKO2210Search knowledge bases semantically with vector embeddings and keyword fallback.
