# vault-echo

> vault-echo — ibarra25-vault-echo. Use this tool when you need to retain and manage local AI memory, eliminating knowledge loss and amnesia. Vault-echo solves the problem of forgotten AI learnings by storing and retrieving information, accepting inputs from various sources and outputting relevant data for future reference. Ideal for use cases where AI continuity and knowledge persistence are crucial, such as iterative machine learning projects or collaborative AI development.

Canonical page: https://skillsregistry.net/skills/ibarra25-vault-echo  
JSON: https://api.skillsregistry.net/v1/skills/ibarra25-vault-echo

## Description

Your Ultimate Local AI Memory Hub 2026 – Eliminate AI Amnesia Today

## Trust

- **Trust score (0–1):** 0.55
- **Verification tier:** scanned
- **Last scanned:** 2026-09-01

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** other
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/Ibarra25/vault-echo)

## 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": "ibarra25-vault-echo"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/ibarra25-vault-echo` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/ibarra25-vault-echo/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