Skill: 717986230/agent-caller (version 1.0.3)

--- name: agent-caller version: "1.0.3" description: Call 179 professional agents on-demand from database author: Erbing license: MIT keywords: - agents - ai - collaboration - database - search - multi-agent category: productivity requires: - python >= 3.6 - sqlite3 install: post_install: | # Create database directory mkdir -p memory/database # Initialize database and import agents python scripts/init_database.py # Verify installation python scripts/verify_install.py --- # 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) | Category | Count | Examples | |----------|-------|----------| | marketing | 29 | Growth Hacker, SEO Specialist | | specialized | 28 | Agents Orchestrator, Code Generator | | engineering | 26 | Backend Architect, AI Engineer | | game-development | 20 | Game Designer, Level Designer | | strategy | 16 | Business Strategist | | testing | 8 | QA Engineer | | sales | 8 | Sales Manager | | design | 8 | UI Designer | | paid-media | 7 | Ads Specialist | | support | 6 | Customer Success | | spatial-computing | 6 | AR/VR Specialist | | project-management | 6 | PM, Scrum Master | | product | 5 | Product Manager | | academic | 5 | Historian, Psychologist | | integrations | 1 | Integration 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 | Method | Description | Returns | |--------|-------------|---------| | `search_agents(keyword)` | Search by keyword | List[Dict] | | `get_agent_by_name(name)` | Get by name | Dict \| None | | `get_agents_by_category(category)` | Get by category | List[Dict] | | `get_random_agent()` | Random pick | Dict \| None | | `get_agent_full_prompt(agent_id)` | Get full prompt | str \| None | | `count_agents()` | Count total | int | | `get_categories()` | List categories | List[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" ``` Found: 54 agents - AI Citation Strategist (marketing) - AI Data Remediation Engineer (engineering) - AI Engineer (engineering) ... ``` ### Browse Categories ``` 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： 1. ✅ 检查Python版本 (>= 3.6) 2. ✅ 创建数据库目录 3. ✅ 初始化SQLite数据库 4. ✅ 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 | Category | Count | Examples | |----------|-------|----------| | marketing | 29 | Growth Hacker, SEO Specialist | | specialized | 28 | Agents Orchestrator, Code Generator | | engineering | 26 | Backend Architect, AI Engineer | | game-development | 20 | Game Designer, Level Designer | | strategy | 16 | Business Strategist | | testing | 8 | QA Engineer | | sales | 8 | Sales Manager | | design | 8 | UI Designer | | paid-media | 7 | Ads Specialist | | support | 6 | Customer Success | | spatial-computing | 6 | AR/VR Specialist | | project-management | 6 | PM, Scrum Master | | product | 5 | Product Manager | | academic | 5 | Historian, Psychologist | | integrations | 1 | Integration Specialist | **Total: 179 agents** ### 数据来源 数据来自 [agency-agents](https://github.com/717986230/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


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)