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Found 8 Skills
Use when long-term knowledge retention is needed (weeks to months), studying for exams or certifications, learning new job skills or technology, mastering substantial material that requires systematic review, combating forgetting through spaced repetition and retrieval practice, or when user mentions studying, memorizing, learning plans, spaced repetition, flashcards, active recall, or durable learning.
Search your coding memory. Use when user asks about past work, previous sessions, how something was implemented, what they worked on before, or wants to recall information from earlier sessions.
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory Use when: conversation memory, remember, memory persistence, long-term memory, chat history.
Recall relevant long-term memories extracted by OpenViking Session memory. Use when the user asks about past decisions, prior fixes, historical context, or what was done in earlier sessions.
Semantic search over global agent memory. Use to retrieve previously learned patterns, decisions, gotchas, and workarounds. Prevents stale-context errors across long sessions and multi-agent pipelines.
Use Neo4j memory MCP for creating/updating linked memories (entities, relations), de-duplication (DRY), and retrieval queries for project continuity. Use when saving global learnings or querying graph relationships.
Query the memory system for relevant learnings from past sessions