# tensory

> tensory — kryptogrib-tensory. Use this tool when you need to efficiently manage and store memory for large language models, such as Claude and Cursor, to enable seamless claim extraction and graph-based data retrieval. Tensory provides a zero-setup, persistent memory solution that integrates with MCP clients, taking in LLM claims and outputting graph, vector, and SQLite formats. Ideal for applications requiring fast and reliable data storage and retrieval, Tensory simplifies the process of working with complex data structures.

Canonical page: https://skillsregistry.net/skills/kryptogrib-tensory  
JSON: https://api.skillsregistry.net/v1/skills/kryptogrib-tensory

## Description

Tensory — Claim-native MCP Memory Server. LLM claim extraction → Graph + Vector + SQLite. Zero-setup persistent memory for Claude, Cursor & any MCP client.

## Trust

- **Trust score (0–1):** 0.31
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/kryptogrib/tensory)

## 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": "kryptogrib-tensory"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/kryptogrib-tensory` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/kryptogrib-tensory/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