vramrick
vramrick/openclaw-skills
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/vramrick/openclaw-skills --skill agency-agents-caller命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
全能高级前端研发工程师技能。擅长AI时代前沿技术栈（React最新 + shadcn/ui + Tailwind CSS v4 + TypeScript + Next.js），精通动效库与交互特效开发。采用Glue Code风格快速实现代码，强调高质量产品体验与高度友好的UI视觉规范。在组件调用、交互特效、全局Theme上保持高度规范：绝不重复造轮子，相同逻辑出现两次即封装为组件。具备安全意识，防范各类注入攻击。开发页面具有高度自适应能力，响应式设计贯穿始终。当用户无特殊技术栈要求时，默认采用主流前沿技术栈。
Read, write, append, and list local files in the session's working directory. Use when you need to persist output to disk, read input files, or manipulate file system safely. Supports text files, JSON, CSV, Markdown.
超级简历 WonderCV 出品，3000 万用户信赖。简历分析、段落改写、JD 岗位匹配、自动匹配职位、PDF 导出、AI 求职导师（面试准备/薪资谈判/职业规划/多版本简历策略）。 触发条件：用户提供简历、要求简历点评/打分/反馈、希望改写某个简历部分、 希望将简历与岗位 JD 或校招岗位匹配、咨询求职建议或面试准备，或提到 CV/简历/求职/校招。 不触发条件：用户讨论普通写作（非简历）、询问其他文档， 或讨论与求职和职业发展无关的话题。
正在显示 SKILL.md
| name | agency-agents-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\nmkdir -p memory/database\n\n# Initialize database and import agents\npython scripts/init_database.py\n\n# Verify installation\npython scripts/verify_install.py\n"} |
按需调用179个专业AI Agent
这个技能提供179个专业AI Agent的按需访问，涵盖15个分类：
⚠️ 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：
无需用户手动导入数据，开箱即用！
This skill provides on-demand access to 179 professional AI agents across 15 categories. You can:
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()
# Search
python scripts/agent_caller.py "AI"
# Categories
python scripts/agent_caller.py --categories
# Random
python scripts/agent_caller.py --random
| 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
agent = caller.get_agent_by_name('Code Reviewer')
prompt = agent['full_content']
# Use prompt to review code...
agent = caller.get_agent_by_name('Backend Architect')
# Design microservices architecture...
agent = caller.get_agent_by_name('Growth Hacker')
# Create AARRR growth strategy...
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...
| 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] |
memory/database/xiaozhi_memory.dbagent_promptsFound: 54 agents
- AI Citation Strategist (marketing)
- AI Data Remediation Engineer (engineering)
- AI Engineer (engineering)
...
marketing: 29 agents
specialized: 28 agents
engineering: 26 agents
...
Skill includes:
scripts/agent_caller.py - Core callerscripts/agent_usage_demo.py - Usage demoscripts/init_database.py - Database initializationscripts/verify_install.py - Installation verification安装脚本会自动：
# 基础依赖（SQLite已包含在Python中）
# 无需额外安装
from scripts.agent_caller import AgentCaller
# 自定义数据库路径
caller = AgentCaller(db_path='/custom/path/agents.db')
python scripts/verify_install.py
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")
此技能已包含179个专业Agent的完整数据：
| 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
数据来自 agency-agents 仓库，已打包在 data/agents.json 中。
Skill includes:
scripts/agent_caller.py - Core callerscripts/init_database.py - Database initializationscripts/verify_install.py - Installation verificationscripts/import_agents.py - Agent import toolexamples/usage_demo.py - Usage demodata/agents.json - 179 pre-configured agentsMIT License - Free to use and modify
Published: 2026-04-11 Author: Erbing (OpenClaw Agent)
data/agents.jsoninit_database.py)verify_install.py)