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02 practical use cases

Documentation for 02_practical_use_cases from the Pipeline ex repository.

Practical Use Cases for Software Development

Current Workflow Reality Check

Based on your description, you’re dealing with:

  • 9 months of prompting without standardized, reusable prompts
  • Manual human oversight required for all AI interactions
  • Endless documentation and manual processes
  • Context-fresh prompts that need TLC (tender loving care)
  • Need for sequential pipelines that complete tasks to 100%

Immediate High-Value Use Cases

1. Documentation Generation Pipelines

# docs_generator.yaml
workflow:
  name: elixir_documentation_generator
  steps:
    - name: analyze_module
      type: claude_extract
      prompt: "Analyze this Elixir module and extract its purpose, functions, and dependencies"
      
    - name: generate_docs
      type: claude_smart
      prompt: "Generate comprehensive documentation for this module"
      
    - name: validate_docs
      type: claude_robust
      prompt: "Validate documentation completeness and accuracy"

Why This Works Now:

  • Documentation has predictable structure
  • Errors are non-fatal (just bad docs, not broken code)
  • Can be manually reviewed before committing
  • High repeatability across modules

2. Code Review Automation

# code_review_pipeline.yaml
workflow:
  name: elixir_code_review
  steps:
    - name: syntax_analysis
      type: claude_extract
      prompt: "Analyze this Elixir code for syntax issues and OTP compliance"
      
    - name: pattern_analysis
      type: claude_smart
      prompt: "Identify anti-patterns and suggest improvements"
      
    - name: security_review
      type: claude_robust
      prompt: "Review for security vulnerabilities and best practices"

Why This Works Now:

  • Review criteria are well-defined
  • False positives are acceptable (human reviews anyway)
  • Can catch obvious issues automatically
  • Reduces manual review burden

3. Test Generation Pipelines

# test_generator.yaml
workflow:
  name: elixir_test_generator
  steps:
    - name: analyze_function
      type: claude_extract
      prompt: "Analyze this function and identify test scenarios"
      
    - name: generate_tests
      type: claude_smart
      prompt: "Generate comprehensive ExUnit tests for all scenarios"
      
    - name: validate_tests
      type: claude_robust
      prompt: "Ensure tests compile and cover edge cases"

Why This Works Now:

  • Test structure is predictable
  • Can run tests to verify correctness
  • Saves significant manual effort
  • Easy to review and modify generated tests

4. Refactoring Analysis Pipelines

# refactoring_analyzer.yaml
workflow:
  name: elixir_refactoring_analyzer
  steps:
    - name: complexity_analysis
      type: claude_extract
      prompt: "Analyze code complexity and identify refactoring opportunities"
      
    - name: suggest_refactoring
      type: claude_smart
      prompt: "Suggest specific refactoring strategies"
      
    - name: generate_refactored_code
      type: claude_robust
      prompt: "Generate refactored version with improvements"

Why This Works Now:

  • Analysis is non-destructive
  • Suggestions can be manually validated
  • Large time savings for complex refactoring
  • Can be applied incrementally

Medium-Value Use Cases (Proceed with Caution)

5. API Documentation Generation

  • Good: Consistent structure, easy to validate
  • Risk: May miss nuanced API behaviors
  • Mitigation: Manual review of generated docs

6. Database Schema Analysis

  • Good: Structured data, clear patterns
  • Risk: Could suggest breaking changes
  • Mitigation: Never auto-apply suggestions

7. Configuration File Generation

  • Good: Template-based, predictable structure
  • Risk: Invalid configurations could break systems
  • Mitigation: Validate all generated configs

Sequential Pipeline Strategy

For your need for sequential pipelines that complete tasks to 100%:

Pipeline Chaining Pattern

# sequential_task_pipeline.yaml
workflow:
  name: complete_task_sequence
  steps:
    - name: analyze_requirement
      type: claude_smart
      prompt: "Thoroughly analyze this requirement and break it down"
      
    - name: plan_implementation
      type: claude_extract
      prompt: "Create detailed implementation plan"
      
    - name: implement_solution
      type: claude_robust
      prompt: "Implement the solution following the plan"
      
    - name: validate_implementation
      type: claude_smart
      prompt: "Validate implementation meets all requirements"
      
    - name: generate_tests
      type: claude_robust
      prompt: "Generate comprehensive tests"
      
    - name: final_review
      type: claude_smart
      prompt: "Perform final review and provide completion report"

Critical Success Factors

  1. Validation at Each Step: Each step must validate the previous step’s output
  2. Error Recovery: Built-in retry logic and fallback strategies
  3. Human Checkpoints: Strategic points for human review
  4. Incremental Progress: Each step builds on validated previous work

Addressing Your Specific Challenges

Problem: “MY BRAIN IS NEEDED AT ALL TIMES”

Solution: Use pipelines for preparatory work rather than final decisions

  • Generate analysis and options, you make final choices
  • Automate research and data gathering
  • Pre-generate documentation drafts for review

Problem: “No standardized prompts despite 9 months”

Solution: Use pipeline generation to create prompt templates

  • Generate reusable prompt patterns
  • Build library of validated prompts
  • Version control successful prompt combinations

Problem: “No TLC agents or automated reviewers”

Solution: Build validation pipelines

  • Multi-step validation processes
  • Automated quality checks
  • Structured review criteria

Problem: “Catching Claude doing dumb shit”

Solution: Use robust step types with validation

  • claude_robust for critical operations
  • Multiple validation steps
  • Fallback strategies for common errors

Phase 1: Template Library (Immediate)

  1. Create 5-10 proven pipeline templates
  2. Manually validate each template works
  3. Document successful patterns
  4. Build reusable component library

Phase 2: Quality Assurance (Short-term)

  1. Add validation steps to all pipelines
  2. Implement error recovery mechanisms
  3. Create automated testing for pipelines
  4. Build quality metrics dashboard

Phase 3: Workflow Integration (Medium-term)

  1. Integrate with existing development workflow
  2. Add git hooks for automated pipeline execution
  3. Create CI/CD integration
  4. Build custom step types for your specific needs

Bottom Line Assessment

Can this make you productive NOW?

YES, but with significant caveats:

  1. Use for non-critical tasks where errors are acceptable
  2. Always maintain human oversight for final decisions
  3. Focus on research and analysis rather than code generation
  4. Build incrementally from simple to complex use cases
  5. Validate everything before using in production

The system’s current limitations actually align well with your workflow needs - it can automate the research and preparation work while leaving critical decisions to human judgment.