Agent skill
openmemory
Persistent long-term agent memory for storing and querying past work, patterns, and learnings.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/openmemory-5dlabs-cto
SKILL.md
OpenMemory (Persistent Agent Memory)
You have access to OpenMemory for persistent long-term memory across sessions.
Memory Tools
| Tool | Purpose |
|---|---|
openmemory_query |
Semantic search across memories by similarity |
openmemory_store |
Store new memories with sector classification |
openmemory_list |
List recent memories for a user/agent |
openmemory_get |
Retrieve specific memory by ID |
openmemory_reinforce |
Boost salience of important memories |
Memory Sectors
Memories are classified into sectors:
| Sector | Use Case | Example |
|---|---|---|
| episodic | Events, task history | "Implemented auth flow for project X" |
| semantic | Facts, learned patterns | "Always add Context7 lookup before Rust implementation" |
| procedural | How-to knowledge | "Steps to deploy with ArgoCD" |
Usage Patterns
Before starting a task:
openmemory_query({ query: "similar implementations", sector: "episodic" })
After completing a task:
openmemory_store({
content: "Implemented OAuth2 with PKCE for React app using Effect",
sector: "episodic",
tags: ["auth", "react", "effect"]
})
For important learnings:
openmemory_reinforce({ memory_id: "mem_xyz", boost: 1.5 })
Best Practices
- Query before implementing - Check for similar past work
- Store after completing - Save successful patterns and solutions
- Reinforce important memories - Boost salience of critical learnings
- Tag memories well - Include relevant technologies and patterns
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agent-ops-spec
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agent-ops-state
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