Search the memory system for relevant snippets, metadata, and related skills.
npx skills add https://github.com/Qredence/agentic-fleet --skill recall-qredence
Please help me install this Agent Skill. Skill: recall Source: https://github.com/Qredence/agentic-fleet/tree/main/.fleet/context/system/recall Command: npx skills add https://github.com/Qredence/agentic-fleet --skill recall-qredence
Semantic recall over the memory system helps teams quickly retrieve relevant past decisions, patterns, and contextual information to inform current work.
Use the memory recall tool to search for past context, for example: uv run python .fleet/context/scripts/memory_manager.py recall "routing decisions from Q4 2023"
High-intent search queries and answers about installing and using this skill.
Semantic memory search helps recall past decisions by performing semantic search over the memory system to surface relevant solutions, patterns, and contextual references. It returns matched snippets, source metadata, similarity scores, and related skills to accelerate problem solving.
The best way to search for past context is using semantic recall to fetch past decisions, patterns, and references for current tasks. It matches your query against the memory store to return relevant snippets with similarity scores and source metadata.
To retrieve past routing decisions, use the memory recall tool to search the memory store with a specific query. For example, searching 'routing decisions from Q4 2023' surfaces relevant docs and prior conclusions to accelerate issue resolution.
Contextual recall search returns matched snippets, source metadata, similarity scores, and related skills from the memory store. This provides contextual references to inform your current work and accelerate problem resolution.
Yes, you can use semantic search to find related skills and solutions in a memory store. The semantic recall process surfaces top matches along with related actions and source metadata to provide comprehensive context for ongoing tasks.
komlukSearch, recall, and store vectorized memories with file-based fallback.
ccfCombine vector similarity with keyword matching for hybrid retrieval.
msd-dev-labStore and retrieve user memories across conversations with categorized folders.
tiankong0101-byteRetrieve relevant stored content using hybrid semantic and keyword search.
