41 lines
1.6 KiB
Markdown
41 lines
1.6 KiB
Markdown
---
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name: agent-scaffold
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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.
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---
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# agent-scaffold
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Generate a complete agent workspace (PRD + SOUL + MEMORY seed) from context in m2-memory.
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## Usage
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```
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/agent-scaffold <agent-name> "<what this agent does>"
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```
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Examples:
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```
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/agent-scaffold gesy-rates-fetcher "Fetches and monitors GESY reimbursement rate updates for GMI Clinic"
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/agent-scaffold platform-unifier "Maps and unifies the 3-5 GMI Clinic software platforms into Machine.Machine"
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/agent-scaffold nasr-research-runner "Runs parallel deep research tasks for Nasr's healthcare domain work"
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```
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## What it produces
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For each agent, the skill:
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1. **Queries m2-memory** for relevant context (entity/project history, related agents, decisions)
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2. **Generates `PRD.md`** — problem, personas, functional requirements, success metrics, open items
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3. **Generates `SOUL.md`** — agent identity, values, communication style, scope boundaries
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4. **Generates `MEMORY.md`** — seed memories pre-loaded from m2-memory search results
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5. **Prints a deploy snippet** — `docker run` or Coolify env vars to spawn the agent
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## Output location
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`~/agents/<agent-name>/` — commit to `git.machinemachine.ai/machine.machine/specs/` when ready.
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## Design principle
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The goal is reproducibility: if an agent's context is lost (like Nasr's Apr 8 research agents),
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this skill can reconstruct the workspace from the vector store and hand it to OpenClaw or Hermes
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to re-execute without starting from scratch.
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