Search per-project memory stores using BM25 ranking and configurable filters.
npx skills add https://github.com/calebeynon/claude-code-setup --skill recall-calebeynon
Please help me install this Agent Skill. Skill: recall Source: https://github.com/calebeynon/claude-code-setup/tree/main/skills/recall Command: npx skills add https://github.com/calebeynon/claude-code-setup --skill recall-calebeynon
Quickly surface relevant past context from per-project memories and archived sessions to inform current work and decisions.
Use recall to fetch relevant memories and display top results to inform your next action.
High-intent search queries and answers about installing and using this skill.
Project memory search uses BM25 ranking to query per-project memory stores, surfacing relevant past chats, worklogs, and decisions to inform current tasks. It filters results by type and project scope.
Retrieving archived AI assistant chats is done by querying the per-project memory store under the current project key, returning top ranked results with brief previews to guide next steps.
Project memory search supports configurable filters by type, allowing you to narrow results to decisions, feedback, gotchas, worklogs, references, or general project context across stored memories.
BM25 ranking scores stored memory entries against your search query, returning the top results based on term frequency and relevance. It surfaces the most relevant past context for current work.
Project memory recall requires the memory_search.py script to run. It operates against memories stored under the project key directory to search and surface stored context.
Memory recall is scoped to the current project by default, but includes configurable filters to expand the search scope across all projects if needed for broader context retrieval.
DicklesworthstoneConvert public chat share links into Markdown and HTML transcripts.
javiarmestoRecord architectural decisions as timestamped markdown files in project directories.
belokonmStructure context and memory handling for AI agents across sessions.
NotysotyDesign hybrid retrieval pipelines combining dense vector search with BM25 keyword matching.
