Teamclaw

A high-performance Agent subsystem designed for complex multi-agent orchestration (OASIS),automated computer use tasks, and real-time visual monitoring via a dedicated Web UI.

Safety Notice

This listing is from the official public ClawHub registry. Review SKILL.md and referenced scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "Teamclaw" with this command: npx skills add avalon-467/teamclawtestv101

Language: English | 中文

<a name="english-version"></a>

Teamclaw — Agent Subsystem Skill (English)

Overview

Teamclaw is a multifunctional AI Agent service providing:

  • Conversational Agent: A multi-tool AI assistant based on LangGraph that supports streaming/non-streaming conversations
  • OASIS Forum: A multi-expert parallel discussion/execution engine that orchestrates collaboration among multiple Agents
  • Scheduled Tasks: A task scheduling center based on APScheduler
  • Bark Push: Mobile push notifications
  • Web UI Frontend: A complete chat interface

Skill Scripts

All scripts are located in selfskill/scripts/ and are called uniformly through the run.sh entry point. All are non-interactive.

selfskill/scripts/
├── run.sh          # Main entry (start/stop/status/setup/add-user/configure)
├── adduser.py      # Non-interactive user creation
└── configure.py    # Non-interactive .env configuration management

Quick Start

All commands are executed from the project root directory.

1. Initial Deployment

# Install dependencies
bash selfskill/scripts/run.sh setup

# Initialize configuration file
bash selfskill/scripts/run.sh configure --init

# Configure LLM (required)
bash selfskill/scripts/run.sh configure --batch \
  LLM_API_KEY=sk-your-key \
  LLM_BASE_URL=https://api.deepseek.com \
  LLM_MODEL=deepseek-chat

# Create user
bash selfskill/scripts/run.sh add-user system MySecurePass123

2. Start/Stop/Status

bash selfskill/scripts/run.sh start     # Start background service
bash selfskill/scripts/run.sh status    # Check status
bash selfskill/scripts/run.sh stop      # Stop service

3. Configuration Management

# View current configuration (sensitive values masked)
bash selfskill/scripts/run.sh configure --show

# Set single item
bash selfskill/scripts/run.sh configure PORT_AGENT 51200

# Batch set
bash selfskill/scripts/run.sh configure --batch TTS_MODEL=gemini-2.5-flash-preview-tts TTS_VOICE=charon

Configurable Items

Configuration ItemDescriptionDefault Value
LLM_API_KEYLLM API key (required)—
LLM_BASE_URLLLM API addresshttps://api.deepseek.com
LLM_MODELModel namedeepseek-chat
LLM_PROVIDERProvider (google/anthropic/deepseek/openai, auto-inferred)Auto
LLM_VISION_SUPPORTImage support (auto-inferred)Auto
PORT_AGENTMain Agent service port51200
PORT_SCHEDULERScheduling port51201
PORT_OASISOASIS forum port51202
PORT_FRONTENDWeb UI port51209
PORT_BARKBark push port58010
TTS_MODELTTS model (optional)—
TTS_VOICETTS voice (optional)—
INTERNAL_TOKENInternal communication key (auto-generated)Auto

Ports and Services

PortService
51200AI Agent main service
51201Scheduled tasks
51202OASIS forum
51209Web UI

API Authentication

Method 1: User Authentication

Authorization: Bearer <user_id>:<password>

Method 2: Internal Token (inter-service calls, recommended)

Authorization: Bearer <INTERNAL_TOKEN>:<user_id>

INTERNAL_TOKEN is auto-generated on the first startup and can be viewed via configure --show-raw.

Core APIs

Base URL: http://127.0.0.1:51200

Chat (OpenAI Compatible)

POST /v1/chat/completions
Authorization: Bearer <token>

{"model":"mini-timebot","messages":[{"role":"user","content":"Hello"}],"stream":true,"session_id":"my-session"}

System Trigger (Internal Call)

POST /system_trigger
X-Internal-Token: <INTERNAL_TOKEN>

{"user_id":"system","text":"Execute task","session_id":"task-001"}

Cancel Session

POST /cancel

{"user_id":"<user_id>","session_id":"<session_id>"}

OASIS Discussion/Execution

POST http://127.0.0.1:51202/topics

{"question":"Discussion topic","user_id":"system","max_rounds":3,"discussion":true,"schedule_yaml":"...","callback_url":"http://127.0.0.1:51200/system_trigger","callback_session_id":"my-session"}

Example Configuration Reference

Below is a sample configuration from an actual running instance (sensitive information masked):

bash selfskill/scripts/run.sh configure --init
bash selfskill/scripts/run.sh configure --batch \
  LLM_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxx4c74 \
  LLM_BASE_URL=https://deepseek.com \
  LLM_MODEL=deepseek-chat \
  LLM_VISION_SUPPORT=true \
  TTS_MODEL=gemini-2.5-flash-preview-tts \
  TTS_VOICE=charon \
  PORT_AGENT=51200 \
  PORT_SCHEDULER=51201 \
  PORT_OASIS=51202 \
  PORT_FRONTEND=51209 \
  PORT_BARK=58010 \
  OPENAI_STANDARD_MODE=false
bash selfskill/scripts/run.sh add-user system <your-password>

Output of configure --show after configuration:

  PORT_SCHEDULER=51201
  PORT_AGENT=51200
  PORT_FRONTEND=51209
  PORT_OASIS=51202
  OASIS_BASE_URL=http://127.0.0.1:51202
  PORT_BARK=58010
  INTERNAL_TOKEN=f1aa****57e7          # Auto-generated, do not leak
  LLM_API_KEY=sk-7****4c74
  LLM_BASE_URL=https://deepseek.com
  LLM_MODEL=deepseek-chat
  LLM_VISION_SUPPORT=true
  TTS_MODEL=gemini-2.5-flash-preview-tts
  TTS_VOICE=charon
  OPENAI_STANDARD_MODE=false

Note: INTERNAL_TOKEN is auto-generated on first startup, PUBLIC_DOMAIN / BARK_PUBLIC_URL are auto-written by tunnel, no manual configuration needed.

Typical Usage Workflow

cd /home/avalon/Teamclaw

# Initial configuration
bash selfskill/scripts/run.sh setup
bash selfskill/scripts/run.sh configure --init
bash selfskill/scripts/run.sh configure --batch LLM_API_KEY=sk-xxx LLM_BASE_URL=https://api.deepseek.com LLM_MODEL=deepseek-chat
bash selfskill/scripts/run.sh add-user system MyPass123

# Start service
bash selfskill/scripts/run.sh start

# Access Web UI
# Open browser and navigate to: http://127.0.0.1:51209
# Log in with username: system and password: MyPass123

# Call API
curl -X POST http://127.0.0.1:51200/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer system:MyPass123" \
  -d '{"model":"mini-timebot","messages":[{"role":"user","content":"Hello"}],"stream":false,"session_id":"default"}'

# Stop service
bash selfskill/scripts/run.sh stop

Important Notes

  • All skill scripts are located in selfskill/scripts/ and do not affect the original project functionality
  • Process management via PID files; start supports idempotent calls
  • Do not leak INTERNAL_TOKEN
  • Log path: logs/launcher.log
  • How to Enable Visualization & Login:
    • After starting the service, open your browser and navigate to http://127.0.0.1:51209
    • Use the credentials you created with add-user command (default: username system, password MySecurePass123)
    • You can now access the chat interface and participate in OASIS forum discussions

<a name="中文版本"></a>

Teamclaw — Agent 子系统 Skill (中文)

简介

Teamclaw 是一个多功能 AI Agent 服务，提供：

  • 对话 Agent：基于 LangGraph 的多工具 AI 助手，支持流式/非流式对话
  • OASIS 论坛：多专家并行讨论/执行引擎，可编排多个 Agent 协作
  • 定时调度：基于 APScheduler 的任务调度中心
  • Bark 推送：移动端推送通知
  • 前端 Web UI：完整的聊天界面

Skill 脚本

所有脚本位于 selfskill/scripts/，统一通过 run.sh 入口调用，全部非交互式。

selfskill/scripts/
├── run.sh          # 主入口（start/stop/status/setup/add-user/configure）
├── adduser.py      # 非交互式用户创建
└── configure.py    # 非交互式 .env 配置管理

快速启动

所有命令在项目根目录下执行。

1. 首次部署

# 安装依赖
bash selfskill/scripts/run.sh setup

# 初始化配置文件
bash selfskill/scripts/run.sh configure --init

# 配置 LLM（必填）
bash selfskill/scripts/run.sh configure --batch \
  LLM_API_KEY=sk-your-key \
  LLM_BASE_URL=https://api.deepseek.com \
  LLM_MODEL=deepseek-chat

# 创建用户
bash selfskill/scripts/run.sh add-user system MySecurePass123

2. 启动/停止/状态

bash selfskill/scripts/run.sh start     # 后台启动
bash selfskill/scripts/run.sh status    # 检查状态
bash selfskill/scripts/run.sh stop      # 停止服务

3. 配置管理

# 查看当前配置（敏感值脱敏）
bash selfskill/scripts/run.sh configure --show

# 设置单项
bash selfskill/scripts/run.sh configure PORT_AGENT 51200

# 批量设置
bash selfskill/scripts/run.sh configure --batch TTS_MODEL=gemini-2.5-flash-preview-tts TTS_VOICE=charon

可配置项

配置项说明默认值
LLM_API_KEYLLM API 密钥（必填）—
LLM_BASE_URLLLM API 地址https://api.deepseek.com
LLM_MODEL模型名称deepseek-chat
LLM_PROVIDER厂商（google/anthropic/deepseek/openai，可自动推断）自动
LLM_VISION_SUPPORT是否支持图片（可自动推断）自动
PORT_AGENTAgent 主服务端口51200
PORT_SCHEDULER定时调度端口51201
PORT_OASISOASIS 论坛端口51202
PORT_FRONTENDWeb UI 端口51209
PORT_BARKBark 推送端口58010
TTS_MODELTTS 模型（可选）—
TTS_VOICETTS 声音（可选）—
INTERNAL_TOKEN内部通信密钥（自动生成）自动

端口与服务

端口服务
51200AI Agent 主服务
51201定时调度
51202OASIS 论坛
51209Web UI

API 认证

方式 1：用户认证

Authorization: Bearer <user_id>:<password>

方式 2：内部 Token（服务间调用，推荐）

Authorization: Bearer <INTERNAL_TOKEN>:<user_id>

INTERNAL_TOKEN 首次启动自动生成，可通过 configure --show-raw 查看。

核心 API

Base URL: http://127.0.0.1:51200

对话（OpenAI 兼容）

POST /v1/chat/completions
Authorization: Bearer <token>

{"model":"mini-timebot","messages":[{"role":"user","content":"你好"}],"stream":true,"session_id":"my-session"}

系统触发（内部调用）

POST /system_trigger
X-Internal-Token: <INTERNAL_TOKEN>

{"user_id":"system","text":"请执行某任务","session_id":"task-001"}

终止会话

POST /cancel

{"user_id":"<user_id>","session_id":"<session_id>"}

OASIS 讨论/执行

POST http://127.0.0.1:51202/topics

{"question":"讨论主题","user_id":"system","max_rounds":3,"discussion":true,"schedule_yaml":"...","callback_url":"http://127.0.0.1:51200/system_trigger","callback_session_id":"my-session"}

案例配置参考

以下是一份实际运行的配置示例（敏感信息已脱敏）：

bash selfskill/scripts/run.sh configure --init
bash selfskill/scripts/run.sh configure --batch \
  LLM_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxx4c74 \
  LLM_BASE_URL=https://deepseek.com \
  LLM_MODEL=deepseek-chat \
  LLM_VISION_SUPPORT=true \
  TTS_MODEL=gemini-2.5-flash-preview-tts \
  TTS_VOICE=charon \
  PORT_AGENT=51200 \
  PORT_SCHEDULER=51201 \
  PORT_OASIS=51202 \
  PORT_FRONTEND=51209 \
  PORT_BARK=58010 \
  OPENAI_STANDARD_MODE=false
bash selfskill/scripts/run.sh add-user system <your-password>

配置完成后 configure --show 输出：

  PORT_SCHEDULER=51201
  PORT_AGENT=51200
  PORT_FRONTEND=51209
  PORT_OASIS=51202
  OASIS_BASE_URL=http://127.0.0.1:51202
  PORT_BARK=58010
  INTERNAL_TOKEN=f1aa****57e7          # 自动生成，勿泄露
  LLM_API_KEY=sk-7****4c74
  LLM_BASE_URL=https://deepseek.com
  LLM_MODEL=deepseek-chat
  LLM_VISION_SUPPORT=true
  TTS_MODEL=gemini-2.5-flash-preview-tts
  TTS_VOICE=charon
  OPENAI_STANDARD_MODE=false

说明：INTERNAL_TOKEN 首次启动自动生成，PUBLIC_DOMAIN / BARK_PUBLIC_URL 由 tunnel 自动写入，无需手动配置。

典型使用流程

cd /home/avalon/Teamclaw

# 首次配置
bash selfskill/scripts/run.sh setup
bash selfskill/scripts/run.sh configure --init
bash selfskill/scripts/run.sh configure --batch LLM_API_KEY=sk-xxx LLM_BASE_URL=https://api.deepseek.com LLM_MODEL=deepseek-chat
bash selfskill/scripts/run.sh add-user system MyPass123

# 启动
bash selfskill/scripts/run.sh start

# 访问 Web UI
# 打开浏览器导航到：http://127.0.0.1:51209
# 使用用户名：system，密码：MyPass123 登录

# 调用 API
curl -X POST http://127.0.0.1:51200/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer system:MyPass123" \
  -d '{"model":"mini-timebot","messages":[{"role":"user","content":"你好"}],"stream":false,"session_id":"default"}'

# 停止
bash selfskill/scripts/run.sh stop

注意事项

  • 所有 skill 脚本位于 selfskill/scripts/，不影响项目原有功能
  • 通过 PID 文件管理进程，start 支持幂等调用
  • INTERNAL_TOKEN 勿泄露
  • 日志路径: logs/launcher.log
  • 如何开启可视化界面和登录：
    • 启动服务后，打开浏览器导航到 http://127.0.0.1:51209
    • 使用通过 add-user 命令创建的凭证（默认：用户名 system，密码 MySecurePass123）
    • 即可访问聊天界面并参与 OASIS 论坛讨论

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Open Registry RecordOpen in ClawHub

Related Skills

Related by shared tags or category signals.

Automation

Synthesis Evaluation

Synthesis evaluation workflows combining SynFormer-ED, Retrosynthesis Planner, and SAScore through SciMiner.

Registry SourceRecently Updated
2181sciminer
Automation

αExtractor

Optical chemical structure recognition workflow for extracting molecule structures and names from images through SciMiner.

Registry SourceRecently Updated
1880sciminer
Automation

alphaExtractor

Optical chemical structure recognition workflow for extracting molecule structures and names from images through SciMiner.

Registry SourceRecently Updated
1900sciminer
Automation

Virtual Screening

Virtual screening workflows combining protein-sequence lookup, docking box calculation, transformer-based library screening, and docking-based proprietary li...

Registry SourceRecently Updated
2291sciminer