Agent skill

Edict Multi Agent Orchestration

by LeoYeAI in 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

MITAuto-check passedAgent Workflows

Install Edict Multi Agent Orchestration

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill edict-multi-agent-orchestration -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills edict-multi-agent-orchestration --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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-src

Use ~/.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/

Facts

Skill name
edict-multi-agent-orchestration
GitHub stars
2.2k
Token cost
~4.2k tokens
SKILL.md length
484 words
Files
2
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

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

  • Tasks that involve Multi-agent orchestration
  • SKILL.md covers Architecture Overview, Prerequisites, Installation and Running the System, plus 7 more sections
  • Calls python3, docker and bash; reaches github.com and open.feishu.cn; needs ANTHROPIC_API_KEY and OPENAI_API_KEY
  • Tasks that involve Task management

What it does

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.

When your agent uses it

  • Tasks that involve Multi-agent orchestration
  • Tasks that involve Task management

Example prompts

  • “/edict-multi-agent-orchestration”

Requirements

  • Python 3
  • Node.js
  • Docker
  • A credential in ANTHROPIC_API_KEY
  • A credential in OPENAI_API_KEY

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    Shell commands in SKILL.md call:

    • python3
    • docker
    • bash
    • npm
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com
    • open.feishu.cn

    Also links to:

    • openclaw.ai
    • ara.so

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • ANTHROPIC_API_KEY
    • OPENAI_API_KEY
    • NEWS_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

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.

Always · name and description, kept in context so the agent knows when to use it
~43
When it runs · the whole SKILL.md, loaded when a task matches
~4.2k

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.

Safety

Auto-check passed

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.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 484 words, ~4,207 tokens.

Download SKILL.mdSave it as .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.
name
edict-multi-agent-orchestration
description
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
triggers
set up edict multi agent system, configure sansheng liubu agents, install edict orchestration, use openclaw multi agent dashboard, set up AI agent pipeline…

Edict (三省六部) Multi-Agent Orchestration

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.


Architecture Overview

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.


Prerequisites

  • OpenClaw installed and running
  • Python 3.9+
  • Node.js 18+ (for React dashboard build)
  • macOS or Linux

Installation

Quick Demo (Docker — no OpenClaw needed)
bash
# 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 up

Open http://localhost:7891

Full Installation
bash
git clone https://github.com/cft0808/edict.git
cd edict
chmod +x install.sh && ./install.sh

The install script automatically:

  • Creates all 12 agent workspaces (taizi, zhongshu, menxia, shangshu, hubu, libu, bingbu, xingbu, gongbu, libu2, zaochao, legacy-compat)
  • Writes SOUL.md role definitions to each agent workspace
  • Registers agents and permission matrix in openclaw.json
  • Symlinks shared data directories across all agent workspaces
  • Sets sessions.visibility all for inter-agent message routing
  • Syncs API keys across all agents
  • Builds React frontend
  • Initializes data directory and syncs official stats
First-time API Key Setup
bash
# Configure API key on first agent
openclaw agents add taizi
# Then re-run install to propagate to all agents
./install.sh

Running the System

bash
# 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

Key Commands

OpenClaw Agent Management
bash
# 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
python
# 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
Data Scripts
bash
# 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.sh

Configuration

Agent Model Configuration (openclaw.json)
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"
    }
  }
}
Per-Agent Model Hot-Switching (via Dashboard)

Navigate to ⚙️ Models panel → select agent → choose LLM → Apply. Gateway restarts automatically (~5 seconds).

Environment Variables
bash
# 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 Roles Reference

AgentRoleResponsibility
taizi太子 Crown PrinceTriage: 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 RevenueFinance, data analysis tasks
libu礼部 Ministry of RitesCommunication, documentation tasks
bingbu兵部 Ministry of WarStrategy, security tasks
xingbu刑部 Ministry of JusticeReview, compliance tasks
gongbu工部 Ministry of WorksEngineering, technical tasks
libu2吏部 Ministry of PersonnelHR, agent management tasks
zaochao早朝官Morning briefing aggregator
Show full SKILL.md (169 more words)Show less
Permission Matrix (who can message whom)
python
# 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"],
}

Task State Machine

python
# 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 corruption

Real Code Examples

Send an Edict Programmatically
python
import 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")
Read Kanban State
python
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)}")
Update Task Status (with validation)
python
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 True
Dashboard REST API Client
python
import 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"
})
Agent Health Check
python
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}")
Custom SOUL.md (Agent Personality)
markdown
<!-- ~/.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.

Dashboard Panels

PanelURL FragmentKey Features
Kanban#kanbanTask columns, heartbeat badges, filter/search, pause/cancel/resume
Monitor#monitorAgent health cards, task distribution charts
Memorials#memorialsCompleted task archive, 5-stage timeline, Markdown export
Templates#templates9 preset edict templates with parameter forms
Officials#officialsToken usage ranking, activity stats
News#newsDaily tech/finance briefing, Feishu push
Models#modelsPer-agent LLM switcher (hot reload ~5s)
Skills#skillsView/add agent skills
Sessions#sessionsLive OC-* session monitor
Court#courtMulti-agent discussion around a topic

Common Patterns

Pattern 1: Parallel Ministry Execution
python
# 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
Pattern 2: Menxia Veto Loop
python
# 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
Pattern 3: News Aggregation + Push
python
# 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)

Troubleshooting

exec format error in Docker
bash
# Force platform on x86/amd64
docker run --platform linux/amd64 -p 7891:7891 cft0808/sansheng-demo
Agents not receiving messages
bash
# 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
API key not propagated to all agents
bash
# Re-run install after configuring key on first agent
openclaw agents add taizi  # configure key here
./install.sh               # propagates to all agents
Dashboard shows stale data
bash
# 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
React frontend not built
bash
# Requires Node.js 18+
cd dashboard/frontend
npm install && npm run build
# server.py will then serve the built assets
Invalid state transition error
python
# 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']]}")
Gateway restart after model change
bash
# After editing openclaw.json models section
openclaw gateway restart
# Wait ~5 seconds for agents to reconnect

Project Structure

edict/
├── 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

Files

SKILL.md and 1 other file in skills/edict-multi-agent-orchestration of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • _meta.json

Open the folder on GitHubat commit e5199b5

Compare with similar skills

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Works with

Questions about Edict Multi Agent Orchestration

What does Edict Multi Agent Orchestration do?

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.

When should I use Edict Multi Agent Orchestration?

Edict Multi Agent Orchestration fits situations like: tasks that involve Multi-agent orchestration; tasks that involve Task management.

How do I install Edict Multi Agent Orchestration in Claude Code?

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.

How do I install Edict Multi Agent Orchestration in Codex?

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.

Can I use Edict Multi Agent Orchestration in Cursor, Gemini CLI or GitHub Copilot?

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.

What does Edict Multi Agent Orchestration need to run?

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.

Does Edict Multi Agent Orchestration access the network?

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.

Is Edict Multi Agent Orchestration safe to install?

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.

What licence does Edict Multi Agent Orchestration use?

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.

How many tokens does Edict Multi Agent Orchestration use?

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.

What are the alternatives to Edict Multi Agent Orchestration?

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.

Who maintains Edict Multi Agent Orchestration?

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.