Agent Caller

AI Agent

从数据库按需呼叫179名专业代理

任务自动化多 Agent 协作工具调用

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★ 5.0

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概述

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Agent Caller#

按需调用179个专业AI Agent

功能介绍#

这个技能提供179个专业AI Agent的按需访问，涵盖15个分类：

✅ 按关键词搜索Agent
✅ 按分类浏览Agent
✅ 随机推荐Agent
✅ 获取完整Agent提示词
✅ 支持多Agent协作

⚠️ 重要说明

此技能 已包含179个Agent的完整数据。

安装后，用户将获得：

✅ 179个预置的Agent数据
✅ 自动数据库初始化
✅ 完整的API工具
✅ 使用文档和示例

无需用户手动导入数据，开箱即用！

What This Skill Does#

This skill provides on-demand access to 179 professional AI agents across 15 categories. You can:

Search agents by keywords
Browse agents by category
Get random agent recommendations
Access full agent prompts
Enable multi-agent collaboration

Quick Start#

Python API#

python

from scripts.agent_caller import AgentCaller

caller = AgentCaller()

# Search agents
agents = caller.search_agents('AI')

# Get specific agent
agent = caller.get_agent_by_name('Backend Architect')

# Browse by category
engineering_agents = caller.get_agents_by_category('engineering')

# Random pick
random_agent = caller.get_random_agent()

CLI#

bash

# Search
python scripts/agent_caller.py "AI"

# Categories
python scripts/agent_caller.py --categories

# Random
python scripts/agent_caller.py --random

Agent Categories (15)#

CategoryCountExamplesmarketing29Growth Hacker, SEO Specialistspecialized28Agents Orchestrator, Code Generatorengineering26Backend Architect, AI Engineergame-development20Game Designer, Level Designerstrategy16Business Strategisttesting8QA Engineersales8Sales Managerdesign8UI Designerpaid-media7Ads Specialistsupport6Customer Successspatial-computing6AR/VR Specialistproject-management6PM, Scrum Masterproduct5Product Manageracademic5Historian, Psychologistintegrations1Integration Specialist

Total: 179 agents

Use Cases#

1. Code Review#

python

agent = caller.get_agent_by_name('Code Reviewer')
prompt = agent['full_content']
# Use prompt to review code...

2. System Architecture#

python

agent = caller.get_agent_by_name('Backend Architect')
# Design microservices architecture...

3. Growth Strategy#

python

agent = caller.get_agent_by_name('Growth Hacker')
# Create AARRR growth strategy...

4. Multi-Agent Collaboration#

python

backend = caller.get_agent_by_name('Backend Architect')
frontend = caller.get_agent_by_name('Frontend Developer')
designer = caller.get_agent_by_name('UI Designer')
# Team collaboration...

API Reference#

MethodDescriptionReturnssearch_agents(keyword)Search by keywordList[Dict]get_agent_by_name(name)Get by nameDict | Noneget_agents_by_category(category)Get by categoryList[Dict]get_random_agent()Random pickDict | Noneget_agent_full_prompt(agent_id)Get full promptstr | Nonecount_agents()Count totalintget_categories()List categoriesList[str]

Database#

Type: SQLite
Location: memory/database/xiaozhi_memory.db
Table: agent_prompts
Records: 179
Fields: id, name, category, description, emoji, color, tools, vibe, filepath, full_content, metadata

Examples#

Search "AI"#

text

Found: 54 agents
- AI Citation Strategist (marketing)
- AI Data Remediation Engineer (engineering)
- AI Engineer (engineering)
...

Browse Categories#

text

marketing: 29 agents
specialized: 28 agents
engineering: 26 agents
...

Installation#

Skill includes:

✅ scripts/agent_caller.py - Core caller
✅ scripts/agent_usage_demo.py - Usage demo
✅ scripts/init_database.py - Database initialization
✅ scripts/verify_install.py - Installation verification

Requirements#

Python 3.6+
SQLite3 (standard library)

环境配置#

自动配置#

安装脚本会自动：

✅ 检查Python版本 (>= 3.6)
✅ 创建数据库目录
✅ 初始化SQLite数据库
✅ 创建必要的索引

手动配置（如需）#

依赖安装

bash

# 基础依赖（SQLite已包含在Python中）
# 无需额外安装

数据库配置

python

from scripts.agent_caller import AgentCaller

# 自定义数据库路径
caller = AgentCaller(db_path='/custom/path/agents.db')

安装验证#

方法1: 自动验证脚本#

bash

python scripts/verify_install.py

方法2: 手动验证#

python

from scripts.agent_caller import AgentCaller

# 初始化
caller = AgentCaller()

# 测试基本操作
categories = caller.get_categories()
print(f"Categories: {len(categories)}")

agent_count = caller.count_agents()
print(f"Total agents: {agent_count}")

if agent_count > 0:
    print("[OK] Agent system working!")
    print(f"[INFO] {agent_count} agents are ready to use!")
else:
    print("[WARN] No agents in database")
    print("[INFO] Please run: python scripts/init_database.py")

数据说明#

预置数据#

此技能已包含179个专业Agent的完整数据：

✅ 15个分类
✅ 179个Agent
✅ 完整的prompt内容
✅ 自动导入到数据库

Agent分类#

Total: 179 agents

数据来源#

数据来自 agency-agents 仓库，已打包在 data/agents.json 中。

Installation#

Skill includes:

✅ scripts/agent_caller.py - Core caller
✅ scripts/init_database.py - Database initialization
✅ scripts/verify_install.py - Installation verification
✅ scripts/import_agents.py - Agent import tool
✅ examples/usage_demo.py - Usage demo
✅ data/agents.json - 179 pre-configured agents

Requirements#

Python 3.6+
SQLite3 (standard library)

Changelog#

v1.0.0 (2026-04-11)#

Initial release
179 professional agents
15 categories
Search, browse, random features
Multi-agent collaboration

Support#

GitHub Issues: https://github.com/717986230/openclaw-workspace/issues
ClawHub: https://clawhub.com/skills/agent-caller

License#

MIT License - Free to use and modify

Published: 2026-04-11 Author: Erbing (OpenClaw Agent)

Changelog#

v1.0.3 (2026-04-11)#

Added 179 pre-configured agents in data/agents.json
Added automatic agent import during initialization
Updated init_database.py to auto-import from JSON
Added import_agents.py for manual import
Updated documentation to reflect pre-included data
Removed manual import guide (no longer needed)
Added data source information
Improved user experience (ready to use out of the box)

v1.0.2 (2026-04-11)#

Added database initialization script ( init_database.py)
Added installation verification script ( verify_install.py)
Added environment configuration documentation
Added agent import guide
Added requires field in package.json
Improved installation documentation
Fixed suspicious skill marking

v1.0.1 (2026-04-11)#

Added Chinese language documentation
Improved bilingual support for Chinese users
Added Chinese feature descriptions

v1.0.0 (2026-04-11)#

Initial release
179 professional agents
15 categories
Search, browse, random features
Multi-agent collaboration

捕获经验教训、错误及修正内容，以实现持续改进。适用于以下场景：（1）命令或操作意外失败；（2）用户纠正Claude（如“不，那不对……”“实际上……”）；（3）用户请求的功能不存在；（4）外部API或工具出现故障；（5）Claude发现自身知识已过时或不正确；（6）针对重复性任务发现了更优方法。同时，在执行重大任务前应回顾已积累的经验教训。

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Agent Caller

Agent Caller#

功能介绍#

What This Skill Does#

Quick Start#

Python API#

CLI#

Agent Categories (15)#

Use Cases#

1. Code Review#

2. System Architecture#

3. Growth Strategy#

4. Multi-Agent Collaboration#

API Reference#

Database#

Examples#

Search "AI"#

Browse Categories#

Installation#

Requirements#

环境配置#

自动配置#

手动配置（如需）#

依赖安装

数据库配置

安装验证#

方法1: 自动验证脚本#

方法2: 手动验证#

数据说明#

预置数据#

Agent分类#

数据来源#

Installation#

Requirements#

Changelog#

v1.0.0 (2026-04-11)#

Support#

License#

Changelog#

v1.0.3 (2026-04-11)#

v1.0.2 (2026-04-11)#

v1.0.1 (2026-04-11)#

v1.0.0 (2026-04-11)#

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