> ## 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
# Examples

End-to-end workflow examples combining CLI features

## Lead Enrichment Pipeline

Collect LinkedIn profiles from a search, enrich them with LLM analysis, and load results into PostgreSQL.

  
#### 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
    ```
  

---

## Competitor Monitoring

Track competitor companies, their employees, and recent posts on a weekly schedule.

  
#### 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: key_employees
        endpoint: /api/linkedin/company/employees
        dependency:
          from_source: companies
          field: urn.value
        input_key: companies
        parallel: 3
        rate_limit: "10/s"
        refresh: auto

      - 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"
    ```
  
  
#### Start the scheduled collection

    ```bash
    anysite dataset schedule competitor-monitor.yaml --incremental --load-db pg
    ```
  
  
#### Analyze the data

    ```bash
    # What are competitors posting about?
    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

    # New hires this week
    anysite dataset query competitor-monitor.yaml --sql "
      SELECT name, headline, company_name
      FROM key_employees
      ORDER BY collected_at DESC
      LIMIT 20
    " --format table
    ```
  

---

## Multi-Platform Research

Collect data from LinkedIn, Twitter, and GitHub for a set of people, merge the results, and export a unified dataset.

  
#### 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
    # Collect all sources
    anysite dataset collect research.yaml

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

    # Get statistics
    anysite dataset stats research.yaml
    ```
  

---

## Quick One-Liners

Common tasks that don't need a full pipeline:

```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
```