🎯

agent-memory-systems

🎯Skill

from sebas-aikon-intelligence/antigravity-awesome-skills

What it does

Designs intelligent agent memory architectures with advanced retrieval strategies, optimizing how agents store, chunk, and recall contextual information across different memory types.

📦

Part of

sebas-aikon-intelligence/antigravity-awesome-skills(171 items)

agent-memory-systems

Installation

git cloneClone repository

git clone https://github.com/sickn33/antigravity-awesome-skills.git .agent/skills

git cloneClone repository

git clone https://github.com/sickn33/antigravity-awesome-skills.git .claude/skills

git cloneClone repository

git clone https://github.com/sickn33/antigravity-awesome-skills.git .gemini/skills

git cloneClone repository

git clone https://github.com/sickn33/antigravity-awesome-skills.git .cursor/skills

📖 Extracted from docs: sebas-aikon-intelligence/antigravity-awesome-skills

Need more details? View full documentation on GitHub →

1Installs

AddedFeb 4, 2026

View on GitHub Back to Skills

Skill Details

SKILL.md

"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"

Overview

# 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

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