# Hecatoncheire MCP

> Use this tool when you need to streamline multi-agent development and orchestrate large language models (LLMs) locally, solving problems of complexity and scalability in continuous development environments. It accepts various inputs, such as development workflows and LLM configurations, and outputs optimized development pipelines. Ideal for use cases requiring efficient, localized management of multiple agents and LLMs in complex development projects.

Canonical page: https://skillsregistry.net/skills/srose69-hecatoncheire  
JSON: https://api.skillsregistry.net/v1/skills/srose69-hecatoncheire

## Description

Multi-agent continuous development system with local LLM orchestration.

## Trust

- **Trust score (0–1):** 0.69
- **Verification tier:** scanned
- **Last scanned:** 2026-09-03

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-03

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/hbafpecek3)
- **Repository:** <https://github.com/srose69/hecatoncheire>

## 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": "srose69-hecatoncheire"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/srose69-hecatoncheire` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/srose69-hecatoncheire/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