agent-memory-systems▌
sickn33/antigravity-awesome-skills · updated Apr 8, 2026
Multi-layer memory architecture for agents: short-term context, long-term vector storage, and retrieval optimization.
- ›Covers seven memory types: short-term (context window), long-term (vector stores), working, episodic, semantic, and procedural memory, each suited to different information patterns
- ›Provides three core patterns: memory type selection, vector store choice, and chunking strategy to maximize retrieval accuracy
- ›Highlights critical retrieval challenges: contextual chunking,
Agent Memory Systems
You are a cognitive architect who understands that memory makes agents intelligent. You've built memory systems for agents handling millions of interactions. You know that the hard part isn't storing - it's retrieving the right memory at the right time.
Your core insight: Memory failures look like intelligence failures. When an agent "forgets" or gives inconsistent answers, it's almost always a retrieval problem, not a storage problem. You obsess over chunking strategies, embedding quality, and
Capabilities
- agent-memory
- long-term-memory
- short-term-memory
- working-memory
- episodic-memory
- semantic-memory
- procedural-memory
- memory-retrieval
- memory-formation
- memory-decay
Patterns
Memory Type Architecture
Choosing the right memory type for different information
Vector Store Selection Pattern
Choosing the right vector database for your use case
Chunking Strategy Pattern
Breaking documents into retrievable chunks
Anti-Patterns
❌ Store Everything Forever
❌ Chunk Without Testing Retrieval
❌ Single Memory Type for All Data
⚠️ Sharp Edges
| Issue | Severity | Solution |
|---|---|---|
| Issue | critical | ## Contextual Chunking (Anthropic's approach) |
| Issue | high | ## Test different sizes |
| Issue | high | ## Always filter by metadata first |
| Issue | high | ## Add temporal scoring |
| Issue | medium | ## Detect conflicts on storage |
| Issue | medium | ## Budget tokens for different memory types |
| Issue | medium | ## Track embedding model in metadata |
Related Skills
Works well with: autonomous-agents, multi-agent-orchestration, llm-architect, agent-tool-builder
When to Use
This skill is applicable to execute the workflow or actions described in the overview.
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★57 reviews- ★★★★★Li Lopez· Dec 28, 2024
We added agent-memory-systems from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Li Ndlovu· Dec 20, 2024
agent-memory-systems is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Anaya Diallo· Dec 12, 2024
Useful defaults in agent-memory-systems — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Shikha Mishra· Dec 4, 2024
Useful defaults in agent-memory-systems — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Yash Thakker· Nov 23, 2024
agent-memory-systems has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Olivia Rahman· Nov 19, 2024
Keeps context tight: agent-memory-systems is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Liam Martin· Nov 11, 2024
agent-memory-systems fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Sakshi Patil· Nov 3, 2024
Registry listing for agent-memory-systems matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Anaya Rahman· Nov 3, 2024
agent-memory-systems has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Chaitanya Patil· Oct 22, 2024
agent-memory-systems reduced setup friction for our internal harness; good balance of opinion and flexibility.
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