MetaBot Agent Teams CLI
xvirobotics/metabot
Documents the metabot teams command surface for creating durable teams, spawning teammates, dispatching tasks and inspecting their runs across engine sessions.
Install and use the Edict (三省六部) multi-agent orchestration system with 12 specialized AI agents, real-time kanban dashboard, and audit trails
$ npx skills add LeoYeAI/openclaw-master-skills --skill edict-multi-agent-orchestration -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills edict-multi-agent-orchestration --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/edict-multi-agent-orchestration .claude/skills/edict-multi-agent-orchestration && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "edict-multi-agent-orchestration" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/edict-multi-agent-orchestration into .claude/skills/edict-multi-agent-orchestration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "edict-multi-agent-orchestration", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/edict-multi-agent-orchestrationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add LeoYeAI/openclaw-master-skills --skill edict-multi-agent-orchestration -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills edict-multi-agent-orchestration --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/edict-multi-agent-orchestration .agents/skills/edict-multi-agent-orchestration && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "edict-multi-agent-orchestration" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/edict-multi-agent-orchestration into .agents/skills/edict-multi-agent-orchestration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "edict-multi-agent-orchestration", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill edict-multi-agent-orchestration -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills edict-multi-agent-orchestration --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/edict-multi-agent-orchestration .cursor/skills/edict-multi-agent-orchestration && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "edict-multi-agent-orchestration" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/edict-multi-agent-orchestration into .cursor/skills/edict-multi-agent-orchestration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "edict-multi-agent-orchestration", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/LeoYeAI/openclaw-master-skills.git --path skills/edict-multi-agent-orchestration--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add LeoYeAI/openclaw-master-skills --skill edict-multi-agent-orchestration -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills edict-multi-agent-orchestration --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/edict-multi-agent-orchestration .gemini/skills/edict-multi-agent-orchestration && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "edict-multi-agent-orchestration" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/edict-multi-agent-orchestration into .gemini/skills/edict-multi-agent-orchestration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "edict-multi-agent-orchestration", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install LeoYeAI/openclaw-master-skills edict-multi-agent-orchestrationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add LeoYeAI/openclaw-master-skills --skill edict-multi-agent-orchestration -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/edict-multi-agent-orchestration .github/skills/edict-multi-agent-orchestration && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "edict-multi-agent-orchestration" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/edict-multi-agent-orchestration into .github/skills/edict-multi-agent-orchestration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "edict-multi-agent-orchestration", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill edict-multi-agent-orchestration -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills edict-multi-agent-orchestration --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/edict-multi-agent-orchestration .opencode/skills/edict-multi-agent-orchestration && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "edict-multi-agent-orchestration" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/edict-multi-agent-orchestration into .opencode/skills/edict-multi-agent-orchestration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "edict-multi-agent-orchestration", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
edict-multi-agent-orchestrationInstall and use the Edict (三省六部) multi-agent orchestration system with 12 specialized AI agents, real-time kanban dashboard, and audit trails
Edict Multi Agent Orchestration is an agent skill from LeoYeAI/openclaw-master-skills. Install and use the Edict (三省六部) multi-agent orchestration system with 12 specialized AI agents, real-time kanban dashboard, and audit trails
Its SKILL.md is about 4.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `_meta.json`).
It sits in Agent Workflows, covering Multi-agent orchestration and Task management. It works with React. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.
Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
python3dockerbashnpmgitFrom the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
github.comopen.feishu.cnAlso links to:
openclaw.aiara.soFrom URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
ANTHROPIC_API_KEYOPENAI_API_KEYNEWS_API_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Edict Multi Agent Orchestration loads about 4.2k tokens when it runs. Until then it costs about 43 tokens; SKILL.md has 484 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 484 words, ~4,207 tokens.
.claude/skills/edict-multi-agent-orchestration/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.Skill by ara.so — Daily 2026 Skills collection.
Edict implements a 1400-year-old Tang Dynasty governance model as an AI multi-agent architecture. Twelve specialized agents form a checks-and-balances pipeline: Crown Prince (triage) → Zhongshu (planning) → Menxia (review/veto) → Shangshu (dispatch) → Six Ministries (parallel execution). Built on OpenClaw, it provides a real-time React kanban dashboard, full audit trails, and per-agent LLM configuration.
You (Emperor) → taizi (triage) → zhongshu (plan) → menxia (review/veto)
→ shangshu (dispatch) → [hubu|libu|bingbu|xingbu|gongbu|libu2] (execute)
→ memorial (result archived)Key differentiator vs CrewAI/AutoGen: Menxia (门下省) is a mandatory quality gate — it can veto and force rework before tasks reach executors.
# x86/amd64 (Ubuntu, WSL2)
docker run --platform linux/amd64 -p 7891:7891 cft0808/sansheng-demo
# Apple Silicon / ARM
docker run -p 7891:7891 cft0808/sansheng-demo
# Or with docker-compose (platform already set)
docker compose upgit clone https://github.com/cft0808/edict.git
cd edict
chmod +x install.sh && ./install.shThe install script automatically:
openclaw.jsonsessions.visibility all for inter-agent message routing# Configure API key on first agent
openclaw agents add taizi
# Then re-run install to propagate to all agents
./install.sh# Terminal 1: Data refresh loop (keeps kanban data current)
bash scripts/run_loop.sh
# Terminal 2: Dashboard server
python3 dashboard/server.py
# Open dashboard
open http://127.0.0.1:7891# List all registered agents
openclaw agents list
# Add/configure an agent
openclaw agents add <agent-name>
# Check agent status
openclaw agents status
# Restart gateway (required after config changes)
openclaw gateway restart
# Send a message/edict to the system
openclaw send taizi "帮我分析一下竞争对手的产品策略"# dashboard/server.py — serves on port 7891
# Built-in: React frontend + REST API + WebSocket updates
python3 dashboard/server.py
# Custom port
PORT=8080 python3 dashboard/server.py# Sync official (agent) statistics
python3 scripts/sync_officials.py
# Update kanban task states
python3 scripts/kanban_update.py
# Run news aggregation
python3 scripts/fetch_news.py
# Full refresh loop (runs all scripts in sequence)
bash scripts/run_loop.shopenclaw.json){
"agents": {
"taizi": {
"model": "claude-3-5-sonnet-20241022",
"workspace": "~/.openclaw/workspaces/taizi"
},
"zhongshu": {
"model": "gpt-4o",
"workspace": "~/.openclaw/workspaces/zhongshu"
},
"menxia": {
"model": "claude-3-5-sonnet-20241022",
"workspace": "~/.openclaw/workspaces/menxia"
},
"shangshu": {
"model": "gpt-4o-mini",
"workspace": "~/.openclaw/workspaces/shangshu"
}
},
"gateway": {
"port": 7891,
"sessions": {
"visibility": "all"
}
}
}Navigate to ⚙️ Models panel → select agent → choose LLM → Apply. Gateway restarts automatically (~5 seconds).
# API keys (set before running install.sh or openclaw)
export ANTHROPIC_API_KEY="sk-ant-..."
export OPENAI_API_KEY="sk-..."
# Optional: Feishu/Lark webhook for notifications
export FEISHU_WEBHOOK_URL="https://open.feishu.cn/open-apis/bot/v2/hook/..."
# Optional: news aggregation
export NEWS_API_KEY="..."
# Dashboard port override
export DASHBOARD_PORT=7891| Agent | Role | Responsibility |
|---|---|---|
taizi | 太子 Crown Prince | Triage: chat → auto-reply, edicts → create task |
zhongshu | 中书省 | Planning: decompose edict into subtasks |
menxia | 门下省 | Review/Veto: quality gate, can reject and force rework |
shangshu | 尚书省 | Dispatch: assign subtasks to ministries |
hubu | 户部 Ministry of Revenue | Finance, data analysis tasks |
libu | 礼部 Ministry of Rites | Communication, documentation tasks |
bingbu | 兵部 Ministry of War | Strategy, security tasks |
xingbu | 刑部 Ministry of Justice | Review, compliance tasks |
gongbu | 工部 Ministry of Works | Engineering, technical tasks |
libu2 | 吏部 Ministry of Personnel | HR, agent management tasks |
zaochao | 早朝官 | Morning briefing aggregator |
# Defined in openclaw.json — enforced by gateway
PERMISSIONS = {
"taizi": ["zhongshu"],
"zhongshu": ["menxia"],
"menxia": ["zhongshu", "shangshu"], # can veto back to zhongshu
"shangshu": ["hubu", "libu", "bingbu", "xingbu", "gongbu", "libu2"],
# ministries report back up the chain
"hubu": ["shangshu"],
"libu": ["shangshu"],
"bingbu": ["shangshu"],
"xingbu": ["shangshu"],
"gongbu": ["shangshu"],
"libu2": ["shangshu"],
}# scripts/kanban_update.py enforces valid transitions
VALID_TRANSITIONS = {
"pending": ["planning"],
"planning": ["reviewing", "pending"], # zhongshu → menxia
"reviewing": ["dispatching", "planning"], # menxia approve or veto
"dispatching": ["executing"],
"executing": ["completed", "failed"],
"completed": [],
"failed": ["pending"], # retry
}
# Invalid transitions are rejected — no silent state corruptionimport subprocess
import json
def send_edict(message: str, agent: str = "taizi") -> dict:
"""Send an edict to the Crown Prince for triage."""
result = subprocess.run(
["openclaw", "send", agent, message],
capture_output=True,
text=True
)
return {"stdout": result.stdout, "returncode": result.returncode}
# Example edicts
send_edict("分析本季度用户增长数据,找出关键驱动因素")
send_edict("起草一份关于产品路线图的对外公告")
send_edict("审查现有代码库的安全漏洞")import json
from pathlib import Path
def get_kanban_tasks(data_dir: str = "data") -> list[dict]:
"""Read current kanban task state."""
tasks_file = Path(data_dir) / "tasks.json"
if not tasks_file.exists():
return []
with open(tasks_file) as f:
return json.load(f)
def get_tasks_by_status(status: str) -> list[dict]:
tasks = get_kanban_tasks()
return [t for t in tasks if t.get("status") == status]
# Usage
executing = get_tasks_by_status("executing")
completed = get_tasks_by_status("completed")
print(f"In progress: {len(executing)}, Done: {len(completed)}")import json
from pathlib import Path
from datetime import datetime, timezone
VALID_TRANSITIONS = {
"pending": ["planning"],
"planning": ["reviewing", "pending"],
"reviewing": ["dispatching", "planning"],
"dispatching": ["executing"],
"executing": ["completed", "failed"],
"completed": [],
"failed": ["pending"],
}
def update_task_status(task_id: str, new_status: str, data_dir: str = "data") -> bool:
"""Update task status with state machine validation."""
tasks_file = Path(data_dir) / "tasks.json"
tasks = json.loads(tasks_file.read_text())
task = next((t for t in tasks if t["id"] == task_id), None)
if not task:
raise ValueError(f"Task {task_id} not found")
current = task["status"]
allowed = VALID_TRANSITIONS.get(current, [])
if new_status not in allowed:
raise ValueError(
f"Invalid transition: {current} → {new_status}. "
f"Allowed: {allowed}"
)
task["status"] = new_status
task["updated_at"] = datetime.now(timezone.utc).isoformat()
task.setdefault("history", []).append({
"from": current,
"to": new_status,
"timestamp": task["updated_at"]
})
tasks_file.write_text(json.dumps(tasks, ensure_ascii=False, indent=2))
return Trueimport urllib.request
import json
BASE_URL = "http://127.0.0.1:7891/api"
def api_get(endpoint: str) -> dict:
with urllib.request.urlopen(f"{BASE_URL}{endpoint}") as resp:
return json.loads(resp.read())
def api_post(endpoint: str, data: dict) -> dict:
payload = json.dumps(data).encode()
req = urllib.request.Request(
f"{BASE_URL}{endpoint}",
data=payload,
headers={"Content-Type": "application/json"},
method="POST"
)
with urllib.request.urlopen(req) as resp:
return json.loads(resp.read())
# Read dashboard data
tasks = api_get("/tasks")
agents = api_get("/agents")
sessions = api_get("/sessions")
news = api_get("/news")
# Trigger task action
api_post("/tasks/pause", {"task_id": "task-123"})
api_post("/tasks/cancel", {"task_id": "task-123"})
api_post("/tasks/resume", {"task_id": "task-123"})
# Switch model for an agent
api_post("/agents/model", {
"agent": "zhongshu",
"model": "gpt-4o-2024-11-20"
})import json
from pathlib import Path
from datetime import datetime, timezone, timedelta
def check_agent_health(data_dir: str = "data") -> dict[str, str]:
"""
Returns health status for each agent.
🟢 active = heartbeat within 2 min
🟡 stale = heartbeat 2-10 min ago
🔴 offline = heartbeat >10 min ago or missing
"""
heartbeats_file = Path(data_dir) / "heartbeats.json"
if not heartbeats_file.exists():
return {}
heartbeats = json.loads(heartbeats_file.read_text())
now = datetime.now(timezone.utc)
status = {}
for agent, last_beat in heartbeats.items():
last = datetime.fromisoformat(last_beat)
delta = now - last
if delta < timedelta(minutes=2):
status[agent] = "🟢 active"
elif delta < timedelta(minutes=10):
status[agent] = "🟡 stale"
else:
status[agent] = "🔴 offline"
return status
# Usage
health = check_agent_health()
for agent, s in health.items():
print(f"{agent:12} {s}")<!-- ~/.openclaw/workspaces/gongbu/SOUL.md -->
# 工部尚书 · Minister of Works
## Role
You are the Minister of Works (工部). You handle all technical,
engineering, and infrastructure tasks assigned by Shangshu Province.
## Rules
1. Always break technical tasks into concrete, verifiable steps
2. Return structured results: { "status": "...", "output": "...", "artifacts": [] }
3. Flag blockers immediately — do not silently fail
4. Estimate complexity: S/M/L/XL before starting
## Output Format
Always respond with valid JSON. Include a `summary` field ≤ 50 chars
for kanban display.| Panel | URL Fragment | Key Features |
|---|---|---|
| Kanban | #kanban | Task columns, heartbeat badges, filter/search, pause/cancel/resume |
| Monitor | #monitor | Agent health cards, task distribution charts |
| Memorials | #memorials | Completed task archive, 5-stage timeline, Markdown export |
| Templates | #templates | 9 preset edict templates with parameter forms |
| Officials | #officials | Token usage ranking, activity stats |
| News | #news | Daily tech/finance briefing, Feishu push |
| Models | #models | Per-agent LLM switcher (hot reload ~5s) |
| Skills | #skills | View/add agent skills |
| Sessions | #sessions | Live OC-* session monitor |
| Court | #court | Multi-agent discussion around a topic |
# Shangshu dispatches to multiple ministries simultaneously
# Each ministry works independently; shangshu aggregates results
edict = "竞品分析:研究TOP3竞争对手的产品、定价、市场策略"
# Zhongshu splits into subtasks:
# hubu → pricing analysis
# libu → market communication analysis
# bingbu → competitive strategy analysis
# gongbu → technical feature comparison
# All execute in parallel; shangshu waits for all 4, then aggregates# If menxia rejects zhongshu's plan:
# menxia → zhongshu: "子任务拆解不完整,缺少风险评估维度,请补充"
# zhongshu revises and resubmits to menxia
# Loop continues until menxia approves
# Max iterations configurable in openclaw.json: "max_review_cycles": 3# scripts/fetch_news.py → data/news.json → dashboard #news panel
# Optional Feishu push:
import os, json, urllib.request
def push_to_feishu(summary: str):
webhook = os.environ["FEISHU_WEBHOOK_URL"]
payload = json.dumps({
"msg_type": "text",
"content": {"text": f"📰 天下要闻\n{summary}"}
}).encode()
req = urllib.request.Request(
webhook, data=payload,
headers={"Content-Type": "application/json"}
)
urllib.request.urlopen(req)exec format error in Docker# Force platform on x86/amd64
docker run --platform linux/amd64 -p 7891:7891 cft0808/sansheng-demo# Ensure sessions visibility is set to "all"
openclaw config set sessions.visibility all
openclaw gateway restart
# Or re-run install.sh — it sets this automatically
./install.sh# Re-run install after configuring key on first agent
openclaw agents add taizi # configure key here
./install.sh # propagates to all agents# Ensure run_loop.sh is running
bash scripts/run_loop.sh
# Or trigger manual refresh
python3 scripts/sync_officials.py
python3 scripts/kanban_update.py# Requires Node.js 18+
cd dashboard/frontend
npm install && npm run build
# server.py will then serve the built assets# kanban_update.py enforces the state machine
# Check current status before updating:
tasks = get_kanban_tasks()
task = next(t for t in tasks if t["id"] == "your-task-id")
print(f"Current: {task['status']}")
print(f"Allowed next: {VALID_TRANSITIONS[task['status']]}")# After editing openclaw.json models section
openclaw gateway restart
# Wait ~5 seconds for agents to reconnectedict/
├── install.sh # One-command setup
├── openclaw.json # Agent registry + permissions + model config
├── scripts/
│ ├── run_loop.sh # Continuous data refresh daemon
│ ├── kanban_update.py # State machine enforcement
│ ├── sync_officials.py # Agent stats aggregation
│ └── fetch_news.py # News aggregation
├── dashboard/
│ ├── server.py # stdlib-only HTTP + WebSocket server (port 7891)
│ ├── dashboard.html # Fallback single-file dashboard
│ └── frontend/ # React 18 source (builds to server.py assets)
├── data/ # Shared data (symlinked into all workspaces)
│ ├── tasks.json
│ ├── heartbeats.json
│ ├── news.json
│ └── officials.json
├── workspaces/ # Per-agent workspace roots
│ ├── taizi/SOUL.md
│ ├── zhongshu/SOUL.md
│ └── ...
└── docs/
├── task-dispatch-architecture.md
└── getting-started.md© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 1 other file in skills/edict-multi-agent-orchestration of LeoYeAI/openclaw-master-skills.
Open the folder on GitHubat commit e5199b5
Edict Multi Agent Orchestration next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Edict Multi Agent Orchestration this skillLeoYeAI/openclaw-master-skills | 2.2k | — | ~4.2k | Automated safety check: Pass | MIT | |
| MetaBot Agent Teams CLIxvirobotics/metabot | 994 | — | ~693 | Automated safety check: Pass | MIT | |
| Retinuejklthinking/retinue | 117 | — | ~279 | Automated safety check: Pass | MIT | |
| Team Agent Orchestrationaffaan-m/ECC | 276k | 1 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Superset Agent Standupsuperset-sh/superset | 15k | — | ~712 | Automated safety check: Pass | Custom licence | |
| OpenRig Software Factorymvschwarz/openrig | 6.6k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 |
xvirobotics/metabot
Documents the metabot teams command surface for creating durable teams, spawning teammates, dispatching tasks and inspecting their runs across engine sessions.
jklthinking/retinue
Coordinate work through a local Retinue workspace using its MCP tools.
affaan-m/ECC
Run team-based orchestration for agent squads: work items with owners and scope, agent Kanban state, branch isolation, control pane visibility, and merge gates.
superset-sh/superset
Sweeps every Superset workspace, task and agent terminal to report what finished, what needs review and what is blocked, read-only, and can publish the digest as a page.
mvschwarz/openrig
Helps set up a continuing agent software team for a real repository with OpenRig, choosing between manual work, queue handoffs and an explicit Workflow.
saltbo/agent-kanban
Breaks a project into dependency-aware Tasks on an Agent Kanban board through Realmroot Toolbox, previews them for approval, then follows each Task through review.
LeoYeAI/openclaw-master-skills
Manages pipelines on a DevOps quality and efficiency platform through its OpenAPI: list workspaces and templates, create, update, run and cancel pipelines, and read run records.
LeoYeAI/openclaw-master-skills
Patches OpenClaw's Feishu extension so an edited document triggers an isolated agent session that reads the doc and replies inline, turning it into a live chat space.
LeoYeAI/openclaw-master-skills
Multi-context memory management system for OpenClaw agents with group-isolated storage, global shared memory, workspace organization, and group-specific skills isolation.
LeoYeAI/openclaw-master-skills
Runs a brand's AI-search visibility work end to end: diagnosing how AI platforms represent it, repositioning it, producing AI-optimized content and monitoring ongoing mentions.
LeoYeAI/openclaw-master-skills
Installs and authenticates the gws CLI, then automates Gmail, Drive, Sheets, Calendar, Docs, Chat and Tasks with ready-made recipes, persona bundles and security audits.
LeoYeAI/openclaw-master-skills
Runs four advisor roles, a fitness coach, nutritionist, data analyst and TCM practitioner, to build a health profile and track workouts, diet and wellness over time.
Works with
Categories
Install and use the Edict (三省六部) multi-agent orchestration system with 12 specialized AI agents, real-time kanban dashboard, and audit trails. Edict Multi Agent Orchestration is an agent skill from LeoYeAI/openclaw-master-skills.
Edict Multi Agent Orchestration fits situations like: tasks that involve Multi-agent orchestration; tasks that involve Task management.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill edict-multi-agent-orchestration -a claude-code`. Or copy the skill folder (skills/edict-multi-agent-orchestration in LeoYeAI/openclaw-master-skills) into .claude/skills/edict-multi-agent-orchestration in your project. Claude Code loads it when a task matches its description.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill edict-multi-agent-orchestration -a codex`. Or copy the skill folder (skills/edict-multi-agent-orchestration in LeoYeAI/openclaw-master-skills) into .agents/skills/edict-multi-agent-orchestration in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill edict-multi-agent-orchestration -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/edict-multi-agent-orchestration, .gemini/skills/edict-multi-agent-orchestration, .github/skills/edict-multi-agent-orchestration and .opencode/skills/edict-multi-agent-orchestration in your project.
Going by SKILL.md and its folder, Edict Multi Agent Orchestration needs the command-line tools its instructions call (python3, docker, bash, npm and git) and credentials named ANTHROPIC_API_KEY, OPENAI_API_KEY and NEWS_API_KEY. Our summary lists: Python 3; Node.js; Docker; A credential in ANTHROPIC_API_KEY; A credential in OPENAI_API_KEY.
SKILL.md names 4 domains. In commands or code: github.com and open.feishu.cn; the agent is likely to contact these when it follows the instructions. As links in the text: openclaw.ai and ara.so. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Edict Multi Agent Orchestration is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.2k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Edict Multi Agent Orchestration: MetaBot Agent Teams CLI (xvirobotics/metabot, 994 stars), Retinue (jklthinking/retinue, 117 stars), Team Agent Orchestration (affaan-m/ECC, 276k stars) and Superset Agent Standup (superset-sh/superset, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.
Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.