Agent Memory Systems Agent Skill
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
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/Dokhacgiakhoa/Agent-Skills-4-Vibe-Coding-CLI --skill agent-memory-systemsWhat comes with it
964 bytes besides the instructionThe instruction itself
10 sections, as written by the authorAgent 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
🧠 Knowledge Modules (Fractal Skills)
1. Memory Type Architecture
2. Vector Store Selection Pattern
3. Chunking Strategy Pattern
4. ❌ Store Everything Forever
5. ❌ Chunk Without Testing Retrieval
6. ❌ Single Memory Type for All Data
Other skills for the same job
different authors, same section of the catalogue
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Take dokhacgiakhoa/agent-memory-systems from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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