m2-market/solutions/agent-scaffold/payload/SKILL.md

name	description
agent-scaffold	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.