> ## Agent Instructions
>
> Base URL: https://api.anysite.io
> Authentication: send the `access-token` header. Do NOT use `Authorization: Bearer`.
> Full endpoint catalog: https://app.anysite.io/docs
# 示例

结合 CLI 各项功能的端到端工作流示例，包括代理驱动的用法

## 线索增强管道

从搜索中采集 LinkedIn 资料，用 LLM 分析进行增强，并将结果加载到 PostgreSQL 中。

  
#### Create the pipeline

    ```yaml dataset.yaml
    name: lead-enrichment
    description: Find and qualify engineering leads

    sources:
      - id: search
        endpoint: /api/linkedin/search/users
        params:
          keywords: "Head of Engineering"
          count: 200
        parallel: 1

      - id: profiles
        endpoint: /api/linkedin/user
        dependency:
          from_source: search
          field: urn.value
        input_key: user
        parallel: 5
        rate_limit: "10/s"
        on_error: skip

      - id: qualified
        type: llm
        dependency:
          from_source: profiles
          field: name
        llm:
          - type: classify
            categories: "high_priority,medium,low"
            output_column: lead_score
          - type: enrich
            add:
              - "seniority:junior/mid/senior/lead/executive"
              - "is_technical:boolean"
              - "team_size:small/medium/large"
          - type: summarize
            max_length: 30
            output_column: quick_bio

    storage:
      format: parquet
      path: ./data/
    ```
  
  
#### Run the collection

    ```bash
    anysite dataset collect dataset.yaml
    ```
  
  
#### Load into PostgreSQL

    ```bash
    anysite dataset load-db dataset.yaml -c pg
    ```
  
  
#### Query the results

    ```bash
    anysite db query pg --sql "
      SELECT name, headline, lead_score, seniority, quick_bio
      FROM qualified
      WHERE lead_score = 'high_priority' AND is_technical = true
      ORDER BY name
    " --format table
    ```
  

---

## 竞争对手监控

跟踪竞争对手公司及其员工和近期动态。

  
#### Create a competitors file

    ```text competitors.txt
    anthropic
    openai
    google-deepmind
    mistral-ai
    ```
  
  
#### Define the pipeline

    ```yaml competitor-monitor.yaml
    name: competitor-monitor
    description: Weekly competitor intelligence

    sources:
      - id: companies
        endpoint: /api/linkedin/company
        from_file: competitors.txt
        input_key: company
        parallel: 2
        refresh: always

      - id: recent_posts
        endpoint: /api/linkedin/company/posts
        dependency:
          from_source: companies
          field: urn.value
        input_key: company
        parallel: 3
        rate_limit: "10/s"
        refresh: always

      - id: post_analysis
        type: llm
        dependency:
          from_source: recent_posts
          field: text
        llm:
          - type: classify
            categories: "product_launch,hiring,partnership,thought_leadership,other"
            output_column: post_type
          - type: summarize
            max_length: 30
            output_column: summary

    storage:
      format: parquet
      path: ./data/

    schedule:
      cron: "0 9 * * 1"

    notifications:
      on_complete:
        - url: "https://hooks.slack.com/services/xxx"
    ```
  
  
#### Generate and install the weekly schedule

    `dataset schedule` 会打印一行 crontab 内容 — 它本身不会运行任何东西：
    ```bash
    anysite dataset schedule competitor-monitor.yaml --incremental --load-db pg
    ```
    用 `crontab -e` 添加打印出的这一行（或改用 `--systemd` 生成 systemd 单元）。
    见 [Scheduling](/docs/cli/datasets/scheduling?lang=zh)。
  
  
#### Analyze the data

    ```bash
    anysite dataset query competitor-monitor.yaml --sql "
      SELECT c.name as company, pa.post_type, COUNT(*) as count
      FROM post_analysis pa
      JOIN companies c ON pa.company_id = c.urn_value
      GROUP BY c.name, pa.post_type
      ORDER BY c.name, count DESC
    " --format table
    ```
  

---

## 多平台调研

针对一组人物采集 LinkedIn、Twitter 和 GitHub 上的数据，然后合并结果。

  
#### Define the pipeline

    ```yaml research.yaml
    name: multi-platform-research
    description: Cross-platform person research

    sources:
      - id: linkedin_profiles
        endpoint: /api/linkedin/user
        from_file: people.txt
        input_key: user
        parallel: 3
        rate_limit: "10/s"
        on_error: skip

      - id: twitter_profiles
        endpoint: /api/twitter/user
        from_file: twitter_handles.txt
        input_key: user
        parallel: 3
        rate_limit: "10/s"
        on_error: skip

      - id: github_profiles
        endpoint: /api/github/user
        from_file: github_users.txt
        input_key: user
        parallel: 3
        on_error: skip

      - id: all_profiles
        type: union
        sources: [linkedin_profiles, twitter_profiles, github_profiles]

    storage:
      format: parquet
      path: ./data/
    ```
  
  
#### Collect and query

    ```bash
    anysite dataset collect research.yaml

    anysite dataset query research.yaml --sql "
      SELECT * FROM all_profiles
    " --format csv --output unified_research.csv

    anysite dataset profile research.yaml
    ```
  

---

## 快速单行命令

```bash
# Enrich a single profile and save to database
anysite api /api/linkedin/user user=satyanadella -q --format jsonl | \
  anysite db insert mydb --table profiles --stdin --auto-create

# Batch process a CSV of companies
anysite api /api/linkedin/company --from-file companies.csv --input-key company \
  --parallel 5 --rate-limit "10/s" --on-error skip \
  --format csv --output company_profiles.csv

# Search and export in one command
anysite api /api/linkedin/search/users keywords="AI researcher" count=100 \
  --format csv --output ai_researchers.csv

# Quick database query
anysite db query pg --sql "SELECT name, headline FROM profiles WHERE headline LIKE '%CEO%'" \
  --format table
```

---

## 代理工作流模式

当 AI 代理（或脚本）需要决定如何驱动 CLI 时，这是一个有用的决策树：

```
One-off lookup of 1-5 items?
  → anysite api (ad-hoc call)

Batch from a known list?
  Small (< 20)  → anysite api --from-file
  Large (20+)   → dataset pipeline with a from_file source

Chaining multiple endpoints (search → detail → posts)?
  → dataset pipeline with dependent sources

Needs to run repeatedly (daily, weekly)?
  → dataset pipeline + schedule + incremental

One-time large collection?
  → dataset pipeline (progress tracking, error recovery, Parquet storage)
```

以上示例中都适用的实用默认原则：

- 编写调用或管道配置之前，始终先发现：用 `anysite describe <endpoint>` 获取准确的参数名和类型 —
  永远不要凭猜测。
- 对批处理数据源来说，`parallel: 3-5` 和 `on_error: skip` 是合理的默认值。
- 在首次正式采集前，先对新管道进行 dry-run：`anysite dataset collect dataset.yaml --dry-run`。
- 重复运行时使用 `--incremental`，避免重新采集已有的输入值。
- `anysite dataset guide --json` 会以结构化数据的形式给代理提供完整的管道配置参考 — 见
  [Agent Protocol](/docs/cli/agent-protocol?lang=zh)。

> 
  本 CLI 通过[代理协议](/docs/cli/agent-protocol?lang=zh)进行通信：结构化 JSON 输出、退出码，以及
  两级发现载荷。当从管道或子进程中调用时，大多数子命令会自动切换为 JSON 输出。