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Skills/AI & Agent Building
sickn33 avatar

Agent Memory Systems

sickn33/antigravity-awesome-skills

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.

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last updated
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Install

$ npx -y skills add sickn33/antigravity-awesome-skills --skill agent-memory-systems --agent claude-code

Installs into .claude/skills of the current project.

Files

Agent Memory Systems

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.

Procedure

  1. Establish the actual task, authenticated tenant/user, permission to retain content, retention limits and existing store/model versions.
  2. Read the guide's tenant-scoped contract and relevant storage/chunking sections. Select an adapter supported by the installed SDK; the guide's historical sketches are not deployable integrations.
  3. Specify record IDs, source evidence, timestamps, namespace, retrieval limits and conflict/version policy. Preserve originals and propose consolidation without deletion.
  4. Test cross-identity rejection, stale/conflicting records, bounded retrieval and failed writes with isolated fixtures. Benchmark retrieval against held-out questions before expanding storage.
  5. Deliver the architecture and evidence-backed change plan, listing unresolved adapters and hosted cost/data destinations. Apply only the persistence or provider operations actually authorized.

Detailed Guide

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.

When to Use

  • Design memory namespaces and retention for an agent serving distinct tenants or users.
  • Diagnose retrieval of stale, conflicting or unauthorized conversation records.
  • Plan an embedding/store migration while preserving source records and verifying isolation.

Example

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.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
SKILL.md on GitHub

Sponsors

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Details

Repository
github.com/sickn33/antigravity-awesome-skills
Category
AI & Agent Building

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