# seedforge

> seedforge — cognis-digital-seedforge. Use this tool when you need to generate synthetic test data with referential integrity, solving problems of data scarcity and privacy concerns in development and testing environments. It takes input parameters such as data schema and output requirements, and generates synthetic data as output, compatible with git for version control. Ideal for use cases where realistic test data is required, but sensitive information must be protected.

Canonical page: https://skillsregistry.net/skills/cognis-digital-seedforge  
JSON: https://api.skillsregistry.net/v1/skills/cognis-digital-seedforge

## Description

Synthetic test-data generator with referential integrity

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Updated:** 2026-09-21

## Source

- **Source listing:** [GitHub](https://github.com/cognis-digital/seedforge)

## 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": "cognis-digital-seedforge"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/cognis-digital-seedforge` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/cognis-digital-seedforge/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