# SSTorytime

> Use this tool when you need to query complex relationship data using natural language, or visualize entity connections in a structured and intuitive way. It solves problems related to data exploration, relationship analysis, and knowledge graph navigation, accepting natural language queries as input and returning structured semantic orbit data and SVG visualizations as output. Ideal for use cases where insights into interconnected data are crucial, such as research, investigation, or data-driven decision making.

Canonical page: https://skillsregistry.net/skills/markburgess-sstorytime  
JSON: https://api.skillsregistry.net/v1/skills/markburgess-sstorytime

## Description

Proxies language model requests to an SSTorytime semantic knowledge graph, enabling natural language queries over relationship data stored in a local instance. Returns structured semantic orbit data and can generate SVG visualizations of entity relationships. Requires a running SSTorytime service and self-signed certificate trust.

## Trust

- **Trust score (0–1):** 0.92
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** other
- **Updated:** 2026-09-28

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/markburgess-sstorytime)
- **Repository:** <https://github.com/markburgess/mcp-sst>

## 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": "markburgess-sstorytime"
    }
  }
}
```

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