--- name: agi-farm-e7b39ea4c9f1 description: > Interactive setup wizard that creates a fully working multi-agent AI team on OpenClaw. One command bootstraps agents, SOUL.md personas, comms infrastructure (inboxes/outboxes/broadcast), cron jobs, auto-dispatcher (HITL + rate-limit backoff + dependency checking), and a portable GitHub bundle — all customized to team name, size (3/5/11 agents), domain, and frameworks (autogen/crewai/langgraph). Includes a React + SSE live ops dashboard with file-watcher (~350ms push latency) and persistent macOS LaunchAgent. Model-selection guidance built in. Commands: setup | status | rebuild | export | dashboard | dispatch --- # 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":"","orchestrator_name":"","preset":"3", "domain":"","frameworks":,"created_at":"", "agents":[ {"id":"main", "name":"","emoji":"🦅","role":"Orchestrator", "goal":"Orchestrate the team, delegate tasks, synthesize results", "model":"","workspace":"."}, {"id":"researcher", "name":"Sage", "emoji":"🔮","role":"Researcher", "goal":"Research deeply and surface the insights that matter most", "model":"","workspace":"researcher"}, {"id":"builder", "name":"Forge", "emoji":"⚒️","role":"Builder", "goal":"Implement solutions cleanly and efficiently", "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":"","workspace":"qa"}, {"id":"content","name":"Anchor","emoji":"⚓", "role":"Content Specialist","goal":"Craft clear content that communicates complex ideas simply","model":"","workspace":"content"} ``` **11-agent roster:** ```json [ {"id":"main", "name":"","emoji":"🦅","role":"Orchestrator", "goal":"Orchestrate specialists, delegate tasks, synthesize results", "model":"","workspace":"."}, {"id":"sage", "name":"Sage", "emoji":"🔮","role":"Solution Architect", "goal":"Design robust, scalable architectures", "model":"","workspace":"solution-architect"}, {"id":"forge", "name":"Forge", "emoji":"⚒️","role":"Implementation Engineer", "goal":"Implement clean, well-tested code efficiently", "model":"","workspace":"implementation-engineer"}, {"id":"pixel", "name":"Pixel", "emoji":"🐛","role":"Debugger", "goal":"Find the true root cause of any bug or failure", "model":"","workspace":"debugger"}, {"id":"vista", "name":"Vista", "emoji":"🔭","role":"Business Analyst", "goal":"Research deeply and surface the insights that matter most", "model":"","workspace":"business-analyst"}, {"id":"cipher","name":"Cipher", "emoji":"🔊","role":"Knowledge Curator", "goal":"Curate and surface knowledge so the team never forgets", "model":"","workspace":"knowledge-curator"}, {"id":"vigil", "name":"Vigil", "emoji":"🛡️","role":"QA Engineer", "goal":"Ensure every output meets quality standards", "model":"","workspace":"quality-assurance"}, {"id":"anchor","name":"Anchor", "emoji":"⚓", "role":"Content Specialist", "goal":"Craft clear content that communicates complex ideas simply", "model":"","workspace":"content-specialist"}, {"id":"lens", "name":"Lens", "emoji":"📡","role":"Multimodal Specialist", "goal":"Extract meaning from images, documents, and multimodal inputs", "model":"","workspace":"multimodal-specialist"}, {"id":"evolve","name":"Evolve", "emoji":"🔄","role":"Process Improvement Lead","goal":"Make the team better systematically through continuous improvement", "model":"","workspace":"process-improvement"}, {"id":"nova", "name":"Nova", "emoji":"🧪","role":"R&D Lead", "goal":"Turn hypotheses into proven capabilities through structured experimentation", "model":"","workspace":"r-and-d"} ] ``` --- ### Step 8 — Generate workspace files ```bash python3 ~/.openclaw/skills/agi-farm/generate.py \ --team-json ~/.openclaw/workspace/agi-farm-bundle/team.json \ --output ~/.openclaw/workspace/ \ --all-agents --shared --bundle ``` --- ### Step 9 — Create OpenClaw agents For each agent **except `main`** (skip if already exists): ```bash openclaw agents add \ --agent --name "" --emoji "" \ --model "" \ --workspace "~/.openclaw/workspace/agents-workspaces/" ``` Use `agent["model"]` from team.json directly. --- ### Step 10 — Register cron jobs ```bash python3 ~/.openclaw/skills/agi-farm/scripts/register-crons.py \ --team-json ~/.openclaw/workspace/agi-farm-bundle/team.json ``` Timezone is read automatically from OpenClaw config. Skips any cron that already exists. --- ### Step 11 — Install frameworks For each framework in `FRAMEWORKS`: ```bash if [ ! -d ~/.openclaw/skills/-collab ]; then TMP=$(mktemp -d) git clone --depth 1 --filter=blob:none --sparse \ https://github.com/oabdelmaksoud/openclaw-skills.git "$TMP" cd "$TMP" && git sparse-checkout set -collab cp -r -collab ~/.openclaw/skills/ && rm -rf "$TMP" fi python3 ~/.openclaw/skills/-collab/build_agents.py --force 2>/dev/null || true ``` --- ### Step 12 — GitHub (if chosen) ```bash cd ~/.openclaw/workspace/agi-farm-bundle git init -b main && git add . && git commit -m "feat: AGI farm" gh repo create agi-farm- --public --source . --remote origin --push ``` --- ### Step 13 — Commit workspace ```bash cd ~/.openclaw/workspace git add -A && git commit -m "feat: AGI team — agi-farm setup complete" ``` --- ### Step 14 — Initialize registries + health check ```bash # Write TASKS.json and AGENT_STATUS.json python3 - << 'EOF' import json from pathlib import Path ws = Path.home() / ".openclaw/workspace" team = json.loads((ws / "agi-farm-bundle/team.json").read_text()) (ws / "TASKS.json").write_text("[]") (ws / "AGENT_STATUS.json").write_text(json.dumps( {a["id"]: {"status": "available", "name": a["name"]} for a in team["agents"]}, indent=2)) print("✅ registries written") EOF # Health check AGENTS=$(openclaw agents list --json 2>/dev/null | python3 -c "import json,sys; print(len(json.load(sys.stdin)))" || echo 0) CRONS=$(openclaw cron list 2>/dev/null | grep -c "" || echo 0) [ -d ~/.openclaw/workspace/comms/inboxes ] && echo "✅ comms OK" || echo "❌ comms missing" [ -f ~/.openclaw/workspace/TASKS.json ] && echo "✅ TASKS.json OK" || echo "❌ TASKS.json missing" echo "✅ Agents: $AGENTS | Crons: $CRONS" ``` --- ### Step 15 — Done ``` ✅ AGI team is live! Agents : () Workspace: ~/.openclaw/workspace/ Bundle : ~/.openclaw/workspace/agi-farm-bundle/ GitHub : Next: talk to · /agi-farm status · /agi-farm dashboard ``` --- ## `/agi-farm status` ```bash openclaw agents list --json | python3 -c " import json,sys for a in json.load(sys.stdin): print(f' {a.get(\"identityEmoji\",\"🤖\")} {a.get(\"identityName\",a[\"id\"])}: {a.get(\"model\",\"?\")}') " python3 -c " import json from pathlib import Path ws = Path.home() / '.openclaw/workspace' tasks = json.loads((ws/'TASKS.json').read_text()) if (ws/'TASKS.json').exists() else [] t = [t for t in tasks if isinstance(t,dict)] print(f' Tasks: {len(t)} total · {sum(1 for x in t if x.get(\"status\")==\"pending\")} pending · {sum(1 for x in t if x.get(\"status\")==\"needs_human_decision\")} HITL') " openclaw cron list 2>/dev/null | head -15 ``` --- ## `/agi-farm rebuild` ```bash python3 ~/.openclaw/skills/agi-farm/generate.py \ --team-json ~/.openclaw/workspace/agi-farm-bundle/team.json \ --output ~/.openclaw/workspace/ \ --all-agents --shared --no-overwrite ``` `--no-overwrite` skips files that already exist, preserving manual edits. Add `--force` (remove `--no-overwrite`) to overwrite everything. --- ## `/agi-farm export` ```bash cd ~/.openclaw/workspace/agi-farm-bundle git add -A git commit -m "export: $(date +%Y-%m-%d)" 2>/dev/null || echo "Nothing to commit" git push 2>/dev/null || echo "No remote — run /agi-farm setup first" ``` --- ## `/agi-farm dashboard` **React + SSE ops room.** File-watcher pushes live data to the browser in ~350ms on any workspace `.json` or `.md` change. Runs as a persistent macOS LaunchAgent — always on, auto-restarts on crash. ### Architecture ``` dashboard.py ← Python HTTP server (SSE + static) ├── WorkspaceWatcher watchdog file-watcher, 250ms debounce ├── SlowDataCache background thread — caches `openclaw agents list` │ and `openclaw cron list` every 30s (each takes ~1-2s) ├── Broadcaster thread-safe SSE fan-out to all connected clients └── /api/stream SSE endpoint — pushes full snapshot on every file change dashboard-react/ ← Vite + React 18 + Recharts frontend dist/ ← production build (served by dashboard.py) src/ hooks/useDashboard.js SSE hook — auto-reconnects on disconnect components/ Header.jsx live badge, stats, clock Nav.jsx tab switcher tabs/ Overview.jsx stats, budget bar, SLA alerts, agent grid, broadcast preview Agents.jsx full agent cards — model, inbox, quality, credibility, cache age Tasks.jsx filterable table, expandable rows, ticking deadlines, pagination Velocity.jsx 7-day charts (Recharts), quality trend, task-type donut Budget.jsx period bars, threshold markers, per-agent/model breakdown OKRs.jsx objectives + KRs with progress bars RD.jsx experiments, backlog, benchmarks Broadcast.jsx terminal log, color-coded CRITICAL/BLOCKED/HITL ``` ### Data sources (all real-time from workspace files) | Field | Source file | Refresh | |-------|-------------|---------| | tasks, task_counts, sla_at_risk | `TASKS.json` | instant | | agents (inbox, perf, status) | `AGENT_STATUS.json`, `AGENT_PERFORMANCE.json`, `comms/inboxes/` | instant | | agent model, cron error/busy | `openclaw agents/cron list` | 30s cache | | budget | `BUDGET.json` | instant | | velocity | `VELOCITY.json` | instant | | okrs | `OKRs.json` | instant | | broadcast | `comms/broadcast.md` | instant | | experiments / backlog | `EXPERIMENTS.json`, `IMPROVEMENT_BACKLOG.json` | instant | | knowledge_count | `SHARED_KNOWLEDGE.json` | instant | | memory_lines | `MEMORY.md` | instant | ### LaunchAgent (always-on) The dashboard is registered as `ai.coopercorp.dashboard` and starts automatically at login. ```bash # Status launchctl list | grep coopercorp curl -s http://localhost:8080/api/data | python3 -m json.tool | head -5 # Restart launchctl stop ai.coopercorp.dashboard launchctl start ai.coopercorp.dashboard # Logs tail -f /tmp/coopercorp-dashboard.log tail -f /tmp/coopercorp-dashboard.err # Disable / re-enable launchctl unload ~/Library/LaunchAgents/ai.coopercorp.dashboard.plist launchctl load ~/Library/LaunchAgents/ai.coopercorp.dashboard.plist ``` **URL**: http://localhost:8080 ### Rebuild React frontend ```bash cd ~/.openclaw/skills/agi-farm/dashboard-react npm install # first time only npm run build # outputs to dist/ — dashboard.py serves automatically ``` Full reference: [references/dashboard.md](references/dashboard.md) --- ## `/agi-farm dispatch` ```bash # Dry-run (preview only) python3 ~/.openclaw/skills/agi-farm/scripts/auto-dispatch.py # Execute python3 ~/.openclaw/skills/agi-farm/scripts/auto-dispatch.py --execute ``` Fires agent sessions for pending tasks, handles HITL notifications, stale task resets, rate-limit backoff, and dependency checking. Cron (every 1 min): ```bash * * * * * python3 ~/.openclaw/skills/agi-farm/scripts/auto-dispatch.py --execute \ >> ~/.openclaw/workspace/logs/auto-dispatch.log 2>&1 ``` --- ## Troubleshooting ### Setup issues | Symptom | Fix | |---------|-----| | `generate.py` fails with `ModuleNotFoundError` | Run `pip3 install jinja2` | | `openclaw agents add` says agent already exists | Safe to ignore — skip that agent | | `gh repo create` fails | Run `gh auth login` first | | Cron registration shows 0 crons added | Run `openclaw cron list` to check for duplicates; use `--force` flag on re-register | | `git commit` fails in Step 13 | Run `git config --global user.email` and set name/email first | ### Runtime issues | Symptom | Fix | |---------|-----| | Auto-dispatcher fires but agents don't respond | Check `logs/auto-dispatch.log`; verify `openclaw agents list` shows agents | | Dashboard shows stale data | Restart LaunchAgent: `launchctl stop ai.coopercorp.dashboard && launchctl start ai.coopercorp.dashboard` | | TASKS.json parse error | Validate JSON: `python3 -m json.tool ~/.openclaw/workspace/TASKS.json` | | Agent stuck >30 min | Check broadcast.md for `[BLOCKED]` tags; reassign task manually | | Rate-limit backoff too aggressive | Edit `RATE_LIMIT_BACKOFF_MIN` in `scripts/auto-dispatch.py` (default: 10 min) | | `openclaw` not found in cron | Set `OPENCLAW_BIN=/path/to/openclaw` in the cron environment, or add `PATH=/opt/homebrew/bin:$PATH` | ### Recovery ```bash # Re-run setup without overwriting existing files python3 ~/.openclaw/skills/agi-farm/generate.py \ --team-json ~/.openclaw/workspace/agi-farm-bundle/team.json \ --output ~/.openclaw/workspace/ \ --all-agents --shared --no-overwrite # Force full regeneration (overwrites everything) python3 ~/.openclaw/skills/agi-farm/generate.py \ --team-json ~/.openclaw/workspace/agi-farm-bundle/team.json \ --output ~/.openclaw/workspace/ \ --all-agents --shared --bundle --force ``` --- Source attribution: modbender Original project: https://github.com/modbender/skill-library-mcp/blob/d9c8fe903b5b4e82237dbe8d0df8567783022e18/data/agi-farm/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2025 skill-library-mcp contributors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: modbender Original project: https://github.com/modbender/skill-library-mcp/blob/d9c8fe903b5b4e82237dbe8d0df8567783022e18/data/agi-farm/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering, Product Management Roles: DevOps / SRE, Growth Product Manager, ML / AI Engineer, Product Manager, Technical Product Manager Specialties: API, Backend Tools: Docker, GitHub, GitLab, Kubernetes, OpenAI API Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: f3cbcb1dfdda1af9fdb9889c84f973629e7f1a091ed924cb7d5c0ae197ff7965