
Memory systems are where agent architectures get real. This skill breaks down the three-layer approach: semantic memory for facts and preferences, episodic for timestamped experiences, and procedural for learned patterns. The tooling coverage is practical, comparing Pinecone's enterprise scale against ChromaDB for prototyping, with honest latency numbers and cost tradeoffs. The core insight holds up: retrieval quality matters more than storage quantity, which is why the chunking and embedding strategy sections matter. Built on the CoALA cognitive framework, so the terminology is consistent even if the broader field isn't. If you're building agents that need to remember across sessions, this covers the architecture decisions you'll actually face.
$ npx -y skills add sickn33/antigravity-awesome-skills --skill agent-memory-systems --agent claude-codeInstalls into .claude/skills of the current project.
Choose semantic records for facts/preferences, episodic records for events, and procedural records for reusable methods. Keep retrieval quality separate from storage volume and bind persistent access to verified identity.
Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.
Request: “Our support agent forgets preferences between sessions. Design memory for two customer tenants without sharing their history.”
Output: Load the tenant-scoped contract and background-extraction sections. Propose a semantic namespace per verified tenant/user, records with source IDs and revisions, and bounded retrieval. Retain conflicting preferences until the owner resolves them; include tests that tenant B cannot retrieve or consolidate tenant A records. Identify the existing persistent adapter and consent/retention evidence before any write.
Sponsors