2026年GitHub开源趋势深度盘点：Agent Skills生态爆发与开源新范式

npx skills add mattpocock/skills

# superpowers工作流示例
workflow:
  - name: spec
    skill: obra/superpowers/spec
    input: "用户需求描述"
    output: "结构化规格文档"
    
  - name: plan
    skill: obra/superpowers/plan
    input: "${spec.output}"
    output: "实施计划与任务分解"
    
  - name: implement
    skill: obra/superpowers/implement
    input: "${plan.output}"
    output: "代码实现"
    
  - name: review
    skill: obra/superpowers/review
    input: "${implement.output}"
    output: "代码审查报告"
    
  - name: ship
    skill: obra/superpowers/ship
    input: "${review.output}"
    output: "部署结果"

# codebase-memory-mcp 工作原理
# 代码库 → Tree-sitter AST解析 → 持久化知识图谱 → 亚毫秒级图查询 → 精确上下文召回

class CodebaseMemoryMCP:
    """给AI Agent装一块外置硬盘"""
    
    def __init__(self):
        self.ast_parser = TreeSitterParser()  # 支持158种编程语言
        self.graph_db = KnowledgeGraph()      # 持久化图结构
        self.query_engine = GraphQuery()      # 子毫秒级查询
    
    def index_codebase(self, project_path):
        """构建代码库知识图谱"""
        files = self.scan_files(project_path)
        for file in files:
            ast = self.ast_parser.parse(file)
            self.extract_dependencies(ast, file)
            self.extract_call_chains(ast, file)
            self.extract_type_info(ast, file)
        self.graph_db.commit()
    
    def query_context(self, query):
        """Agent查询相关代码上下文"""
        # 不再靠"猜"来理解代码，而是像人类一样"查"
        results = self.query_engine.cypher_query(query)
        return self.format_context(results)

# Agent-Reach 支持的平台
supported_platforms = {
    "社交媒体": ["Twitter/X", "Reddit", "B站", "小红书"],
    "代码平台": ["GitHub", "GitLab"],
    "视频平台": ["YouTube", "Vimeo"],
    "搜索":    ["Google", "RSS"]
}

# 每个平台一个Python模块，内置多后端自动路由
class AgentReach:
    def fetch(self, platform, url):
        module = self.load_platform_module(platform)
        # 运行时探测哪个后端可用，自动切换
        for backend in module.backends:
            if backend.is_available():
                return backend.fetch(url)
        raise ConnectionError(f"所有后端均不可用: {platform}")

# OpenMontage 工作流示例
def create_video(prompt):
    """输入自然语言描述，输出完整视频"""
    # Step 1: 调研
    research = agent.research(prompt)
    
    # Step 2: 脚本生成
    script = agent.generate_script(research)
    
    # Step 3: 素材匹配
    footage = agent.match_footage(script)
    
    # Step 4: 镜头编排
    shots = agent.organize_shots(footage, script)
    
    # Step 5: 剪辑合成
    timeline = agent.edit(shots)
    
    # Step 6: 配音配乐
    final = agent.add_audio(timeline)
    
    return final

# design.md 示例
tokens:
  color:
    primary: "#2563eb"
    secondary: "#64748b"
    background: "#ffffff"
    text: "#1e293b"
  spacing:
    xs: "4px"
    sm: "8px"
    md: "16px"
    lg: "24px"
    xl: "32px"
  radius:
    sm: "4px"
    md: "8px"
    lg: "12px"
  font:
    family: "Inter, system-ui, sans-serif"
    size:
      body: "16px"
      heading: "24px"
      caption: "12px"

用户输入: "回测沪深300的20/50日均线交叉策略，2024年全年"
    ↓
Route: 识别市场 → 选择数据源
    ↓
Ground: 拉取行情数据、基本面数据
    ↓
Test: 编写策略代码 → 执行回测 → 生成权益曲线
    ↓
Deliver: 输出分析报告（K线图、相关性热力图、风险指标）

# AI对冲基金团队架构
hedge_fund_team = {
    "巴菲特Agent":   {"风格": "价值投资", "关注": "护城河、自由现金流、ROE"},
    "芒格Agent":     {"风格": "逆向思维", "关注": "认知偏差识别"},
    "木头姐Agent":   {"风格": "成长投资", "关注": "颠覆式创新"},
    "索罗斯Agent":   {"风格": "宏观对冲", "关注": "地缘政治"},
    "格雷厄姆Agent": {"风格": "深度价值", "关注": "安全边际计算"},
    "风控经理":      {"职责": "仓位限制、相关性检查"},
    "组合经理":      {"职责": "最终配置决策"}
}

# 决策流程
# 1. 每个分析师Agent独立出信号
# 2. 风控Agent审核
# 3. 组合经理Agent做最终配置决策

// 拦截列表（部分）
const BLOCKED_COMMANDS: &[&str] = &[
    "rm -rf /",              // 递归删除根目录
    "git push --force origin main",  // 强制推送主分支
    "DROP TABLE",            // 删除数据库表
    "DROP DATABASE",         // 删除数据库
    "chmod -R 777",          // 危险权限设置
    ":(){ :|:& };:",         // Fork炸弹
];

# Agent读取Excel数据
officecli read --file report.xlsx --sheet "Q2 Sales"

# Agent生成分析图表写入Excel
officecli chart --type bar --data ... --output analysis.xlsx

# Agent基于数据生成PPT
officecli pptx --template quarterly-review.pptx --data analysis.xlsx

# 选型推荐矩阵
recommendations = {
    "独立开发者/自由职业者": {
        "Skills": "mattpocock/skills（灵活可组合）",
        "记忆": "codebase-memory-mcp（代码库理解）",
        "安全": "destructive_command_guard（防误操作）",
        "效率": "caveman（省Token）"
    },
    "企业团队": {
        "Skills": "obra/superpowers（流程标准化）",
        "质量": "addyosmani/agent-skills（工程纪律）",
        "安全": "strix（渗透测试）",
        "设计": "hallmark + design.md（视觉一致性）"
    },
    "内容创作者": {
        "视频": "OpenMontage（Agent视频制作）",
        "剪辑": "OpenCut（开源剪映替代）",
        "联网": "Agent-Reach（多平台内容获取）"
    },
    "量化交易入门者": {
        "主力": "Vibe-Trading（自然语言量化）",
        "学习": "ai-hedge-fund（多Agent协作教学）"
    }
}