agi-farm

Files (45)

• README.md
• SKILL.md
• _meta.json
• auto-dispatch.py
• README.md · dashboard-react/
• index-CN6oMpnV.js · dashboard-react/dist/assets/
• index-DpgkYyr0.css · dashboard-react/dist/assets/
• index.html · dashboard-react/dist/
• vite.svg · dashboard-react/dist/
• eslint.config.js · dashboard-react/
• index.html · dashboard-react/
• package-lock.json · dashboard-react/
• package.json · dashboard-react/
• vite.svg · dashboard-react/public/
• App.css · dashboard-react/src/
• App.jsx · dashboard-react/src/
• react.svg · dashboard-react/src/assets/
• AgentMiniCard.jsx · dashboard-react/src/components/
• Header.jsx · dashboard-react/src/components/
• LastUpdated.jsx · dashboard-react/src/components/
• Nav.jsx · dashboard-react/src/components/
• Agents.jsx · dashboard-react/src/components/tabs/
• Alerts.jsx · dashboard-react/src/components/tabs/
• Broadcast.jsx · dashboard-react/src/components/tabs/
• Budget.jsx · dashboard-react/src/components/tabs/
• Comms.jsx · dashboard-react/src/components/tabs/
• Crons.jsx · dashboard-react/src/components/tabs/
• HITL.jsx · dashboard-react/src/components/tabs/
• Knowledge.jsx · dashboard-react/src/components/tabs/
• OKRs.jsx · dashboard-react/src/components/tabs/
• Overview.jsx · dashboard-react/src/components/tabs/
• Projects.jsx · dashboard-react/src/components/tabs/
• RD.jsx · dashboard-react/src/components/tabs/
• Tasks.jsx · dashboard-react/src/components/tabs/
• Velocity.jsx · dashboard-react/src/components/tabs/
• useDashboard.js · dashboard-react/src/hooks/
• index.css · dashboard-react/src/
• main.jsx · dashboard-react/src/
• vite.config.js · dashboard-react/
• dashboard.html
• dashboard.py
• generate.py
• dashboard.md · references/
• auto-dispatch.py · scripts/
• register-crons.py · scripts/

SKILL.md (first lines)

# agi-farm

Builds a complete multi-agent AI team on OpenClaw. One wizard, full team.

## Commands

| Command | What it does |
|---------|-------------|
| `/agi-farm setup` | Full wizard — agents, workspace, crons, bundle, GitHub |
| `/agi-farm status` | Team health: agents, tasks, cron status |
| `/agi-farm rebuild` | Regenerate workspace from existing bundle (preserves edits) |
| `/agi-farm export` | Push bundle to GitHub |
| `/agi-farm dashboard` | Launch live ops room — see [references/dashboard.md](references/dashboard.md) |
| `/agi-farm dispatch` | Run auto-dispatcher — see [scripts/auto-dispatch.py](scripts/auto-dispatch.py) |

---

## `/agi-farm setup`

Ask **one question at a time**. Do not proceed until confirmed.

### Step 1 — Team name
> "What should we call your team? (e.g. NovaCorp, TradingDesk — default: MyTeam)"

Store as `TEAM_NAME`.

### Step 2 — Orchestrator name
> "What's your orchestrator's name? (default: Cooper)"

Store as `ORCHESTRATOR_NAME`.

### Step 3 — Team size
> "How many agents?
> **3** — Minimal: Orchestrator + Researcher + Builder
> **5** — Standard: adds QA + Content
> **11** — Full stack: complete AGI system (recommended)"

Store as `PRESET`.

### Step 3.5 — Domain
> "What domain? software / trading / research / general (default) / custom"

If custom: ask for one-phrase description. Store as `DOMAIN`.

### Step 3.6 — Custom agents _(PRESET 3 or 5 only)_
> "Add a custom agent? (yes/no, default: no)"

If yes, collect per agent: `id`, `name`, `emoji`, `role`, `goal`. Max 3 custom agents.
Append to roster in Step 7 with `"template": "generic"`.

### Step 4 — Frameworks
> "Collaboration frameworks? autogen / crewai / langgraph / all / none"

Store as `FRAMEWORKS` list. `all` → `["autogen", "crewai", "langgraph"]`.

### Step 5 — GitHub
> "Create a GitHub repo for the bundle? yes / no"

Store as `CREATE_GITHUB`.

### Step 6 — Confirm
Show summary, ask "Shall I proceed? (yes/no)". If no → restart Step 1.

---

### Step 7 — Write `team.json`

```bash
mkdir -p ~/.openclaw/workspace/agi-farm-bundle/
openclaw agents list --json   # use output to assign appropriate models per role
```

Use the `openclaw agents list` output to assign each agent a model appropriate for
its role. Write resolved model strings directly into the `"model"` fields.

**Model selection cheat sheet** (based on `openclaw agents list --json` output):

| Role | Recommended tier | Why |
|------|-----------------|-----|
| Orchestrator | High-capability (e.g. `sonnet`, `opus`) | Needs broad reasoning, delegation judgment |
| Solution Architect / Researcher | High-capability | Deep analysis + design |
| Implementation Engineer | Mid-tier (e.g. `glm-5`, `sonnet`) | Fast code gen; cost-efficiency matters |
| Debugger | High-capability (e.g. `opus`) | Root-cause analysis benefits from deep reasoning |
| Business Analyst / Knowledge | Mid-high (e.g. `gemini-2.0-pro-exp`) | Long-context research tasks |
| QA Engineer | Fast/cheap (e.g. `glm-4.7-flash`) | High volume, pattern-matching checks |
| Content / Multimodal | Multimodal-capable (e.g. `gemini-2.0-pro-exp`) | Vision + rich generation |
| R&D / Process Improvement | High-capability | Creative + structured experimentation |

> Tip: assign `opus` or `sonnet` to roles that make decisions; use `flash`/`glm-4.7-flash` for high-frequency reviewers to manage cost.

**3-agent roster:**
```json
{"team_name":"<TEAM_NAME>","orchestrator_name":"<ORCHESTRATOR_NAME>","preset":"3",
 "domain":"<DOMAIN>","frameworks":<FRAMEWORKS_JSON>,"created_at":"<ISO_TIMESTAMP>",
 "agents":[
   {"id":"main",       "name":"<ORCHESTRATOR_NAME>","emoji":"🦅","role":"Orchestrator",           "goal":"Orchestrate the team, delegate tasks, synthesize results",             "model":"<MODEL>","workspace":"."},
   {"id":"researcher", "name":"Sage",               "emoji":"🔮","role":"Researcher",             "goal":"Research deeply and surface the insights that matter most",            "model":"<MODEL>","workspace":"researcher"},
   {"id":"builder",    "name":"Forge",              "emoji":"⚒️","role":"Builder",                "goal":"Implement solutions cleanly and efficiently",                          "model":"<MODEL>","workspace":"builder"}
 ]}
```

**5-agent:** add to 3-agent roster:
```json
{"id":"qa",     "name":"Vigil", "emoji":"🛡️","role":"QA Engineer",       "goal":"Ensure every output meets quality standards","model":"<MODEL>","workspace":"qa"},
{"id":"content","name":"Anchor","emoji":"⚓", "role":"Content Specialist","goal":"Craft clear content that communicates complex ideas simply","model":"<MODEL>","workspace":"content"}
```

**11-agent roster:**
```json
[
  {"id":"main",  "name":"<ORCHESTRATOR_NAME>","emoji":"🦅","role":"Orchestrator",            "goal":"Orchestrate specialists, delegate tasks, synthesize results",                          "model":"<MODEL>","workspace":"."},
  {"id":"sage",  "name":"Sage",              "emoji":"🔮","role":"Solution Architect",       "goal":"Design robust, scalable architectures",                                               "model":"<MODEL>","workspace":"solution-architect"},
  {"id":"forge", "name":"Forge",             "emoji":"⚒️","role":"Implementation Engineer", "goal":"Implement clean, well-tested code efficiently",                                        "model":"<MODEL>","workspace":"implementation-engineer"},
  {"id":"pixel", "name":"Pixel",             "emoji":"🐛","role":"Debugger",                "goal":"Find the true root cause of any bug or failure",                                       "model":"<MODEL>","workspace":"debugger"},
  {"id":"vista", "name":"Vista",             "emoji":"🔭","role":"Business Analyst",        "goal":"Research deeply and surface the insights that matter most",                            "model":"<MODEL>","workspace":"business-analyst"},
  {"id":"cipher","name":"Cipher",            "emoji":"🔊","role":"Knowledge Curator",       "goal":"Curate and surface knowledge so the team never forgets",                              "model":"<MODEL>","workspace":"knowledge-curator"},
  {"id":"vigil", "name":"Vigil",             "emoji":"🛡️","role":"QA Engineer",             "goal":"Ensure every output meets quality standards",                                         "model":"<MODEL>","workspace":"quality-assurance"},
  {"id":"anchor","name":"Anchor",            "emoji":"⚓", "role":"Content Specialist",     "goal":"Craft clear content that communicates complex ideas simply",                           "model":"<MODEL>","workspace":"content-specialist"},
  {"id":"lens",  "name":"Lens",              "emoji":"📡","role":"Multimodal Specialist",   "goal":"Extract meaning from images, documents, and multimodal inputs",                       "model":"<MODEL>","workspace":"multimodal-specialist"},

From LeoYeAI/openclaw-master-skills, MIT.

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