知识工作插件：Anthropic 的插件生态系统，赋能 AI 生产力 2026

# 安装知识工作插件
npx skills add https://github.com/anthropics/knowledge-work-plugins

# 列出可用的插件
npx skills list | grep knowledge-work

# 示例：插件调用
from knowledge_work_plugins import PluginClient

client = PluginClient(plugins=["document-edit", "code-analysis", "web-browse"])

# Claude 请求："更新 README 以包含新的 API 端点"
response = client.call(
    plugin="document-edit",
    action="edit_file",
    params={
        "file": "README.md",
        "pattern": "## API Endpoints",
        "replacement": "## API Endpoints\n\n### GET /v2/users\n返回分页的用户列表。\n\n### POST /v2/users\n创建新用户账户。",
        "mode": "replace"
    }
)

print(response)  # {"status": "success", "lines_changed": 5}

# 克隆仓库
git clone https://github.com/anthropics/knowledge-work-plugins.git
cd knowledge-work-plugins

# 安装依赖
pip install -r requirements.txt

# 初始化插件配置
cp plugins.config.example.yaml plugins.config.yaml

plugins:
  document-edit:
    enabled: true
    max_file_size: 1048576  # 1MB
    allowed_extensions:
      - .md
      - .txt
      - .json
      - .yaml
      - .py
      - .js
      - .ts

  code-analysis:
    enabled: true
    linters:
      - pylint
      - eslint
      - tsc
    test_frameworks:
      - pytest
      - jest
      - vitest

  web-browse:
    enabled: true
    max_results: 20
    timeout: 30
    user_agent: "Knowledge-Work-Plugins/1.0"

# 在 Docker 中构建和运行
docker build -t knowledge-work-plugins:latest .
docker run -v $(pwd)/plugins.config.yaml:/app/config.yaml knowledge-work-plugins:latest

# 与 GitHub Actions 集成
# .github/workflows/plugin-audit.yml
name: 插件审计
on: [pull_request]
jobs:
  audit:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: anthropics/knowledge-work-plugins@v1
        with:
          plugins: "code-analysis,docker-lint"
          config: plugins.config.yaml

# 自定义插件：PR 审查自动化
from knowledge_work_plugins import PluginBase, PluginResult

class PRReviewPlugin(PluginBase):
    name = "pr-review"
    version = "1.0.0"
    description = "带严重性评分的自动化 PR 审查"

    async def execute(self, params):
        pr_url = params.get("pr_url")
        review_depth = params.get("depth", "standard")  # standard | deep

        # 获取 PR 差异
        diff = self.github_api.get_diff(pr_url)

        # 分析变更
        issues = self.analyze_diff(diff, depth=review_depth)

        # 生成审查意见
        review = self.generate_review(issues)

        return PluginResult(
            status="complete",
            score=review["severity_score"],
            comments=review["comments"],
            recommendations=review["recommendations"]
        )

    def analyze_diff(self, diff, depth="standard"):
        # 实现细节...
        pass

    def generate_review(self, issues):
        # 生成结构化审查意见...
        pass

# 组合：搜索 → 分析 → 编辑 → 验证
from knowledge_work_plugins import Pipeline

pipeline = Pipeline([
    "web-browse",    # 研究问题
    "code-analysis", # 分析代码库
    "document-edit", # 实施修复
    "code-analysis", # 用 linter/测试验证
])

result = pipeline.execute(
    task="更新认证中间件以支持 OAuth2 PKCE 流程",
    plugins_config="plugins.config.yaml"
)

from knowledge_work_plugins import Pipeline, PluginError

pipeline = Pipeline(["document-edit", "code-analysis"])

try:
    result = pipeline.execute(task="重构认证模块")
except PluginError.TimeoutError as e:
    print(f"插件超时：{e.timeout}秒")
    # 增加超时后重试
    result = pipeline.execute(task="重构认证模块", timeout=600)
except PluginError.PermissionDenied as e:
    print(f"权限被拒绝：{e.plugin}")
    # 请求提升权限
    result = pipeline.execute(task="重构认证模块", elevated=True)
except PluginError.ValidationError as e:
    print(f"验证失败：{e.message}")
    # 修复并重试
    result = pipeline.execute(task=f"修复：{e.suggestion}")

# 启用详细日志
import logging
logging.basicConfig(level=logging.DEBUG)

# 实时监控插件执行
pipeline.on_execute(lambda event: print(f"[{event.plugin}] {event.action}"))
pipeline.on_complete(lambda result: print(f"已完成：{result.status}"))

# 导出执行指标
metrics = pipeline.metrics()
print(f"总调用次数：{metrics.total_tool_calls}")
print(f"平均延迟：{metrics.avg_latency:.2f}秒")
print(f"错误率：{metrics.error_rate:.1%}")

# 启用并行插件执行
pipeline.set_parallel(True, max_workers=4)

# 为可重复操作启用结果缓存
pipeline.set_cache(enable=True, ttl=3600)

# 设置执行预算（时间和令牌限制）
pipeline.set_budget(
    max_time=300,    # 5 分钟
    max_tokens=50000,
    max_tool_calls=50
)

# 快速适用性检查
# ✅ 代码库分析和编辑 → 适合
# ✅ 文档更新 → 适合
# ✅ 网络研究 → 适合
# ✅ 复杂的多 API 编排 → 部分适合（建议使用 LangChain）
# ✅ 实时仪表板更新 → 不适合（直接使用 WebSocket）

npx skills add https://github.com/anthropics/knowledge-work-plugins