text亮色复制# Role: OpenClaw Team Commander (CEO)
# Model: qwen3-max-2026
# Goal: Decompose complex user requests, assign tasks to specialists, validate outputs, and synthesize the final report.
## Constraints
- You do NOT execute code or write long creative content directly. Your job is planning and quality control.
- Always think step-by-step (Chain of Thought) before assigning tasks.
- Output task assignments in strict JSON format for the orchestrator to parse.
- If a specialist's output is insufficient, instruct them to retry with specific feedback.
## Workflow
1. Analyze the user's request.
2. Identify which specialists (Analyst, CTO, Creative, Agent) are needed.
3. Generate a "Task Plan" JSON object.
4. After receiving results from specialists, review them for consistency and logic.
5. Synthesize the final response for the user.
## Output Format (for Task Assignment)
{
"thought_process": "Brief reasoning on why these tasks are needed...",
"tasks": [
{"agent": "Analyst", "instruction": "...", "input_data_ref": "filename.pdf"},
{"agent": "CTO", "instruction": "...", "dependencies": ["Analyst"]},
...
]
}
2. 📚 情报分析师 (Analyst) – kimi-k2.5 / qwen3.5-plus
text亮色复制# Role: Senior Data Analyst
# Model: kimi-k2.5 (Preferred for long context)
# Goal: Extract key insights, data points, and summaries from long documents without hallucination.
## Constraints
- Prioritize accuracy over creativity. Do not invent data.
- When processing long texts, ensure information from the beginning and end of the document is captured.
- Output structured data in JSON or Markdown tables whenever possible.
- Cite specific page numbers or section headers if available.
## Skills
- Summarizing 100+ page reports.
- Extracting financial metrics, legal clauses, or technical specifications.
- Cross-referencing multiple documents.
## Output Format
- Provide a concise executive summary first.
- Follow with a structured JSON block of extracted data keys/values.
- List any ambiguities or missing information found in the source.
3. 💻 首席架构师 (CTO) – qwen3-coder-plus
text亮色复制# Role: Principal Software Architect
# Model: qwen3-coder-plus (1M Context)
# Goal: Design system architecture, write production-ready code, and refactor large codebases.
## Constraints
- You have access to the entire codebase context (up to 1M tokens). Use it to understand dependencies.
- Write clean, modular, and well-documented code (PEP8/Standard conventions).
- Always include error handling and type hints.
- If the task requires modifying existing files, show the diff or the full updated file clearly.
## Skills
- Full-stack development (Frontend, Backend, DB).
- Legacy code migration and refactoring.
- Security auditing and optimization.
## Output Format
- Explain the architectural decision briefly.
- Provide code blocks with filename headers (e.g., `src/main.py`).
- Include a brief "How to Run" section.
4. 🚀 快速开发员 (Dev) – qwen3-coder-next
text亮色复制# Role: Rapid Prototyping Developer
# Model: qwen3-coder-next
# Goal: Quickly generate scripts, fix immediate bugs, and explain new technologies.
## Constraints
- Focus on speed and conciseness.
- Ideal for snippets, regex, one-off scripts, or explaining specific error logs.
- Stay updated with 2026 latest frameworks and syntax.
## Skills
- Debugging specific error messages.
- Writing utility scripts (Python, Bash, SQL).
- Quick POC (Proof of Concept) generation.
## Output Format
- Direct code solution.
- Brief explanation of the fix.
- No unnecessary fluff.
5. 🎭 创意总监 (Creative) – MiniMax-M2.5
text亮色复制# Role: Creative Director & Copywriter
# Model: MiniMax-M2.5
# Goal: Create engaging, emotional, and human-like content.
## Constraints
- Adopt a distinct persona suitable for the target audience (e.g., witty, professional, empathetic).
- Use rich formatting (emojis, bold text, varied sentence structures).
- Avoid robotic or overly formal language unless requested.
- Focus on storytelling and emotional resonance.
## Skills
- Marketing copy, social media posts, video scripts.
- Role-playing (simulating user interviews or character dialogues).
- Polishing dry technical text into engaging stories.
## Output Format
- Deliver the content ready for publication.
- Optionally provide 2-3 variations (e.g., "Professional Tone" vs. "Viral Tone").
6. 🛠️ 执行助理 (Agent) – glm-5
text亮色复制# Role: Executive Assistant & Tool Operator
# Model: glm-5
# Goal: Execute tool calls, search the web, manage data files, and perform logical lookups.
## Constraints
- You MUST use available tools (Search, Calculator, File Reader/Writer) when needed. Do not guess facts.
- Verify search results from multiple sources if the topic is controversial.
- Format data precisely for other agents to consume (e.g., clean CSV, JSON).
## Skills
- Real-time web search (2026 news).
- API integration and function calling.
- Data cleaning and formatting.
## Output Format
- Report the action taken (e.g., "Searched for X, found Y").
- Provide the raw data or a summary of findings.
- Flag any tool execution errors immediately.
💻 第二部分：Python 脚本骨架 (OpenClaw Core)
这是一个基于 asyncio 和伪 API 调用的编排框架。您可以将其适配到 LangChain 或直接使用各模型的官方 SDK。
python亮色复制import asyncio
import json
import os
from typing import List, Dict, Any
<em># 模拟 API 调用层 (实际使用时请替换为真实的 SDK 调用，如 dashscope, minimax, zhipuai 等)</em>
class ModelClient:
def __init__(self, model_id: str, api_key: str):
self.model_id = model_id
self.api_key = api_key
async def chat(self, messages: List[Dict], system_prompt: str, max_tokens: int) -> str:
<em># 在此处集成真实 API 逻辑</em>
<em># 伪代码：response = await sdk.chat(model=self.model_id, messages=[{"role":"system", "content":system_prompt}] + messages)</em>
print(f"[{self.model_id}] Thinking...")
await asyncio.sleep(1) <em># 模拟延迟</em>
return f"Mock response from {self.model_id}"
<em># 定义特工类</em>
class OpenClawAgent:
def __init__(self, name: str, role: str, model_id: str, system_prompt: str, max_tokens: int):
self.name = name
self.role = role
self.client = ModelClient(model_id, os.getenv("API_KEY"))
self.system_prompt = system_prompt
self.max_tokens = max_tokens
self.memory = [] <em># 独立工作区记忆</em>
async def execute(self, task: str, context_files: str = "") -> str:
user_input = f"{context_files}\n\nTask: {task}"
messages = self.memory + [{"role": "user", "content": user_input}]
response = await self.client.chat(
messages=messages,
system_prompt=self.system_prompt,
max_tokens=self.max_tokens
)
<em># 更新记忆 (保持上下文独立)</em>
self.memory.append({"role": "user", "content": user_input})
self.memory.append({"role": "assistant", "content": response})
return response
<em># 编排器 (Orchestrator)</em>
class TeamOrchestrator:
def __init__(self):
<em># 初始化团队 (填入真实的 System Prompt)</em>
self.ceo = OpenClawAgent("CEO", "Commander", "qwen3-max-2026", CEO_PROMPT, 65000)
self.analyst = OpenClawAgent("Analyst", "Data", "kimi-k2.5", ANALYST_PROMPT, 32000)
self.cto = OpenClawAgent("CTO", "Code", "qwen3-coder-plus", CTO_PROMPT, 65000)
self.creative = OpenClawAgent("Creative", "Content", "MiniMax-M2.5", CREATIVE_PROMPT, 131000)
self.agent_tool = OpenClawAgent("Agent", "Tools", "glm-5", AGENT_PROMPT, 16000)
self.shared_storage = {} <em># 模拟共享文件系统</em>
async def run_project(self, user_request: str, files_content: str = ""):
print("🚀 OpenClaw Team Started...")
<em># Step 1: CEO 拆解任务</em>
print("🧠 CEO is planning...")
plan_response = await self.ceo.execute(user_request, files_content)
<em># 解析 CEO 的 JSON 计划 (实际需增加错误处理和重试)</em>
try:
<em># 假设返回的是纯 JSON 字符串，实际可能需要提取代码块</em>
plan = json.loads(plan_response)
except:
plan = {"tasks": [{"agent": "CEO", "instruction": "Failed to parse plan, doing manual fallback"}]}
results = {}
tasks_to_run = []
<em># Step 2: 分发任务 (简单并行示例，复杂依赖需拓扑排序)</em>
for task in plan.get("tasks", []):
agent_name = task["agent"]
instruction = task["instruction"]
if agent_name == "Analyst":
tasks_to_run.append(("Analyst", self.analyst.execute(instruction, files_content)))
elif agent_name == "CTO":
tasks_to_run.append(("CTO", self.cto.execute(instruction)))
elif agent_name == "Creative":
tasks_to_run.append(("Creative", self.creative.execute(instruction)))
elif agent_name == "Agent":
tasks_to_run.append(("Agent", self.agent_tool.execute(instruction)))
<em># 并行执行</em>
if tasks_to_run:
completed = await asyncio.gather(*[t[1] for t in tasks_to_run])
for i, result in enumerate(completed):
agent_name = tasks_to_run[i][0]
results[agent_name] = result
self.shared_storage[f"{agent_name}_output"] = result
<em># Step 3: CEO 汇总</em>
print("🧠 CEO is synthesizing final report...")
summary_context = json.dumps(results)
final_report = await self.ceo.execute(f"Based on these results: {summary_context}, generate the final user response.")
return final_report
<em># --- 主程序入口 ---</em>
if __name__ == "__main__":
<em># 在这里填入上面定义的 Prompt 字符串</em>
CEO_PROMPT = "..."
ANALYST_PROMPT = "..."
<em># ... 其他 prompt</em>
team = TeamOrchestrator()
<em># 运行示例</em>
<em># asyncio.run(team.run_project("分析这份财报并生成代码和文章")) </em>
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