# data-feeder

> data-feeder — ptroger-data-feeder. Use this tool when you need to streamline data ingestion for AI agents, solving problems of data consistency and availability. It takes YAML configuration as input and outputs data feeds to an MCP server, featuring caching, scheduling, and authentication. Ideal for use cases requiring automated and secure data provisioning, such as machine learning model training and AI system deployment.

Canonical page: https://skillsregistry.net/skills/ptroger-data-feeder  
JSON: https://api.skillsregistry.net/v1/skills/ptroger-data-feeder

## Description

Declarative data feeds for AI agents. YAML config → MCP server with caching, scheduling, and auth.

## Trust

- **Trust score (0–1):** 0.89
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/Ptroger/data-feeder)

## 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": "ptroger-data-feeder"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/ptroger-data-feeder` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/ptroger-data-feeder/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