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
5.7
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AI & LLM
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The skill addresses an important and novel domain (agent memory systems) that would require significant tokens for a CLI agent to handle independently. However, it is severely incomplete. The description is truncated mid-sentence, and the SKILL.md itself appears unfinished with incomplete sentences, missing pattern details, empty anti-pattern descriptions, and a malformed Sharp Edges table where 'Issue' is repeated rather than actual issues being listed. While the structure and taxonomy (memory types, patterns, anti-patterns) are sound, the lack of concrete implementation details, retrieval strategies, code examples, or actionable guidance significantly limits taskKnowledge. The skill shows promise but needs substantial completion to be useful for an AI agent to execute memory system tasks.
davila7
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