periplon 0.2.0

# Example: Deterministic Tasks with Variables, Secrets, and Multiple Execution Types
# This workflow demonstrates all supported task execution types with variable interpolation

name: "Deterministic Tasks Example"
version: "1.0.0"
dsl_version: "1.0.0"

# Secrets management - secure credential handling
secrets:
  api_token:
    source:
      type: env
      var: "API_TOKEN"
    description: "API authentication token from environment"

  db_password:
    source:
      type: file
      path: ".secrets/db_password.txt"
    description: "Database password from file"

# Workflow input variables
inputs:
  project_name:
    type: string
    required: true
    default: "MyProject"
    description: "Name of the project to process"

  api_endpoint:
    type: string
    required: true
    default: "https://api.example.com"
    description: "Base API endpoint URL"

  max_retries:
    type: number
    required: false
    default: 3
    description: "Maximum number of retries for API calls"

# Workflow output variables
outputs:
  final_report:
    source:
      type: file
      path: "./reports/${workflow.project_name}_report.md"
    description: "Generated project report"

  analysis_data:
    source:
      type: state
      key: "analysis_result"
    description: "Parsed analysis data"

# Tasks demonstrating different execution types
tasks:
  # 1. Script execution - Python
  analyze_data:
    description: "Analyze project data using Python script"
    script:
      language: python
      content: |
        import json
        import sys
        import os

        project = os.environ.get('PROJECT_NAME', 'Unknown')
        print(f"Analyzing project: {project}")

        # Simulate analysis
        result = {
            "project": project,
            "files_analyzed": 42,
            "issues_found": 3,
            "score": 85
        }

        # Output JSON result
        print(json.dumps(result))
      env:
        PROJECT_NAME: "${workflow.project_name}"
        MAX_RETRIES: "${workflow.max_retries}"
      timeout_secs: 30
    outputs:
      analysis_result:
        source:
          type: state
          key: "script_output"
        description: "Analysis results from Python script"

  # 2. Command execution - External tool
  generate_report:
    description: "Generate report using external command"
    command:
      executable: "pandoc"
      args:
        - "--from=markdown"
        - "--to=html"
        - "-o"
        - "reports/${workflow.project_name}_report.html"
        - "data/input.md"
      working_dir: "."
      timeout_secs: 60
      capture_stdout: true
      capture_stderr: true
    depends_on:
      - analyze_data
    outputs:
      report_path:
        source:
          type: file
          path: "reports/${workflow.project_name}_report.html"

  # 3. HTTP request - API call with authentication
  fetch_external_data:
    description: "Fetch data from external API with Bearer auth"
    http:
      method: GET
      url: "${workflow.api_endpoint}/projects/${workflow.project_name}/data"
      headers:
        Content-Type: "application/json"
        User-Agent: "DSL-Workflow/1.0"
      auth:
        type: bearer
        token: "${secret.api_token}"
      timeout_secs: 30
      follow_redirects: true
      verify_tls: true
    outputs:
      api_response:
        source:
          type: state
          key: "http_response"
        description: "Response from external API"

  # 4. HTTP POST with Basic auth
  submit_results:
    description: "Submit analysis results to API with Basic auth"
    http:
      method: POST
      url: "${workflow.api_endpoint}/projects/${workflow.project_name}/results"
      headers:
        Content-Type: "application/json"
      body: |
        {
          "project": "${workflow.project_name}",
          "score": "${task.analysis_result}",
          "timestamp": "2024-01-01T00:00:00Z"
        }
      auth:
        type: basic
        username: "api_user"
        password: "${secret.db_password}"
      timeout_secs: 30
    depends_on:
      - analyze_data
      - fetch_external_data

  # 5. MCP tool invocation - Direct tool call
  process_with_mcp:
    description: "Process data using MCP tool"
    mcp_tool:
      server: "data_processor"
      tool: "transform_data"
      parameters:
        input_file: "data/${workflow.project_name}.json"
        output_format: "csv"
        options:
          normalize: true
          remove_duplicates: true
      timeout_secs: 60
    depends_on:
      - fetch_external_data
    outputs:
      transformed_data:
        source:
          type: file
          path: "output/${workflow.project_name}.csv"

  # 6. JavaScript script with API key authentication
  validate_with_js:
    description: "Validate data using Node.js script"
    script:
      language: javascript
      content: |
        const project = process.env.PROJECT_NAME;
        const apiKey = process.env.API_KEY;

        console.log(`Validating project: ${project}`);
        console.log(`Using API key: ${apiKey.substring(0, 4)}...`);

        // Validation logic
        const isValid = project.length > 0;
        console.log(JSON.stringify({ valid: isValid, project: project }));
      env:
        PROJECT_NAME: "${workflow.project_name}"
        API_KEY: "${secret.api_token}"
      timeout_secs: 15
    depends_on:
      - analyze_data

  # 7. Bash script for file operations
  cleanup_temp_files:
    description: "Clean up temporary files"
    script:
      language: bash
      content: |
        #!/bin/bash
        PROJECT="${PROJECT_NAME}"
        echo "Cleaning up temp files for project: $PROJECT"

        # Remove temp files
        rm -f /tmp/${PROJECT}_*.tmp

        echo "Cleanup complete"
      env:
        PROJECT_NAME: "${workflow.project_name}"
      working_dir: "."
      timeout_secs: 10
    depends_on:
      - generate_report
      - submit_results
      - process_with_mcp
      - validate_with_js

  # 8. Command with custom environment
  run_tests:
    description: "Run tests with custom environment"
    command:
      executable: "pytest"
      args:
        - "tests/"
        - "-v"
        - "--project=${workflow.project_name}"
      env:
        PYTHONPATH: "/app/src"
        TEST_MODE: "ci"
        PROJECT: "${workflow.project_name}"
      working_dir: "."
      timeout_secs: 300
      capture_stdout: true
      capture_stderr: true

  # 9. HTTP with API key authentication
  notify_webhook:
    description: "Send notification to webhook"
    http:
      method: POST
      url: "https://hooks.example.com/notify"
      headers:
        Content-Type: "application/json"
      body: |
        {
          "event": "workflow_complete",
          "project": "${workflow.project_name}",
          "status": "success"
        }
      auth:
        type: api_key
        header: "X-API-Key"
        key: "${secret.api_token}"
      timeout_secs: 10
    depends_on:
      - cleanup_temp_files
      - run_tests

  # 10. Script from external file
  run_external_script:
    description: "Run script from external file"
    script:
      language: python
      file: "scripts/process_data.py"
      working_dir: "."
      env:
        PROJECT_NAME: "${workflow.project_name}"
        DATA_DIR: "data"
      timeout_secs: 120
    depends_on:
      - process_with_mcp