# neolata-mem

> neolata-mem — jeremiaheth-neolata-mem. Use this tool when you need to efficiently store and retrieve knowledge in a graph-native format, leveraging hybrid vector and keyword search to solve complex information retrieval problems. It enables AI agents to link ideas using Zettelkasten methodology and simulate biological decay for knowledge prioritization. Ideal for applications requiring advanced knowledge management and information organization.

Canonical page: https://skillsregistry.net/skills/jeremiaheth-neolata-mem  
JSON: https://api.skillsregistry.net/v1/skills/jeremiaheth-neolata-mem

## Description

Graph-native memory engine for AI agents — hybrid vector+keyword search, biological decay, Zettelkasten linking.

## Trust

- **Trust score (0–1):** 0.30
- **Verification tier:** unverified
- **Last scanned:** 2026-08-23

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** ai-ml
- **Updated:** 2026-09-14

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/jeremiaheth-neolata-mem)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "jeremiaheth-neolata-mem"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/jeremiaheth-neolata-mem` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/jeremiaheth-neolata-mem/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt