---
name: agent-scaffold
description: Scaffolds OpenClaw/Hermes agent workspaces from m2-memory. Query memory for context, generate PRD.md + SOUL.md for a new agent, and optionally wire into the fleet.
---

# agent-scaffold

Generate a complete agent workspace (PRD + SOUL + MEMORY seed) from context in m2-memory.

## Usage

```
/agent-scaffold <agent-name> "<what this agent does>"
```

Examples:
```
/agent-scaffold gesy-rates-fetcher "Fetches and monitors GESY reimbursement rate updates for GMI Clinic"
/agent-scaffold platform-unifier "Maps and unifies the 3-5 GMI Clinic software platforms into Machine.Machine"
/agent-scaffold nasr-research-runner "Runs parallel deep research tasks for Nasr's healthcare domain work"
```

## What it produces

For each agent, the skill:

1. **Queries m2-memory** for relevant context (entity/project history, related agents, decisions)
2. **Generates `PRD.md`** — problem, personas, functional requirements, success metrics, open items
3. **Generates `SOUL.md`** — agent identity, values, communication style, scope boundaries
4. **Generates `MEMORY.md`** — seed memories pre-loaded from m2-memory search results
5. **Prints a deploy snippet** — `docker run` or Coolify env vars to spawn the agent

## Output location

`~/agents/<agent-name>/` — commit to `git.machinemachine.ai/machine.machine/specs/` when ready.

## Design principle

The goal is reproducibility: if an agent's context is lost (like Nasr's Apr 8 research agents), 
this skill can reconstruct the workspace from the vector store and hand it to OpenClaw or Hermes 
to re-execute without starting from scratch.