# edgehdf5-memory

> edgehdf5-memory — osobh-edgehdf5-memory. Use this tool when you need to store and retrieve large amounts of cognitive data for AI agents, solving problems of data persistence and scalability in machine learning models. It provides a persistent memory interface with HDF5-backed storage, accepting input data from AI agents and outputting retrieved information. Ideal for use cases requiring long-term data retention and recall in AI systems.

Canonical page: https://skillsregistry.net/skills/osobh-edgehdf5-memory  
JSON: https://api.skillsregistry.net/v1/skills/osobh-edgehdf5-memory

## Description

HDF5-backed persistent cognitive memory for AI agents.

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** scanned
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** ai-ml
- **Updated:** 2026-09-19

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/osobh-edgehdf5-memory)

## 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": "osobh-edgehdf5-memory"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/osobh-edgehdf5-memory` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/osobh-edgehdf5-memory/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