# Pipeline Prompt System Guide
## Table of Contents
1. [Overview](#overview)
2. [Prompt Types Reference](#prompt-types-reference)
3. [File-Based Prompt Management](#file-based-prompt-management)
4. [Prompt Template Standards](#prompt-template-standards)
5. [Component Library Standards](#component-library-standards)
6. [Advanced Prompt Patterns](#advanced-prompt-patterns)
7. [Content Processing Features](#content-processing-features)
8. [Best Practices](#best-practices)
9. [Complete Examples](#complete-examples)
10. [Migration Guide](#migration-guide)
## Overview
The Pipeline Prompt System provides a comprehensive framework for managing, reusing, and composing prompts across AI workflows. This system enables:
- **External Prompt Files**: Store prompts in dedicated files for reusability
- **Dynamic Composition**: Combine multiple prompt sources into complex instructions
- **Template Libraries**: Standardized prompt components for common patterns
- **Content Processing**: Advanced extraction and transformation of prompt content
- **Version Control**: Track and manage prompt evolution over time
### Architecture
```
pipeline_ex/
├── pipelines/
│ ├── prompts/ # Reusable prompt templates
│ │ ├── analysis/ # Analysis-focused prompts
│ │ ├── generation/ # Content generation prompts
│ │ ├── extraction/ # Data extraction prompts
│ │ └── validation/ # Quality check prompts
│ └── components/ # Reusable step components
│ ├── validation_steps.yaml
│ ├── transformation_steps.yaml
│ └── llm_steps.yaml
└── workflows/ # Complete workflow definitions
├── development/ # Development workflows
├── analysis/ # Analysis workflows
└── production/ # Production workflows
```
## Prompt Types Reference
### 1. Static Content (`static`)
Inline text content defined directly in the YAML:
```yaml
prompt:
- type: "static"
content: |
Analyze this code for the following criteria:
1. Security vulnerabilities
2. Performance issues
3. Code maintainability
4. Best practice adherence
```
**Use Cases:**
- Short, workflow-specific instructions
- Connecting text between other prompt types
- Conditional logic instructions
### 2. File Content (`file`)
Load content from external files:
```yaml
prompt:
- type: "file"
path: "pipelines/prompts/analysis/security_review.md"
- type: "file"
path: "src/main.py"
```
**Features:**
- Automatic file change detection and caching
- Support for any text-based file format
- Relative and absolute path support
- Error handling for missing files
**Use Cases:**
- Reusable prompt templates
- Loading source code for analysis
- Loading documentation or requirements
- Standard prompt components
### 3. Previous Response (`previous_response`)
Reference outputs from earlier workflow steps:
```yaml
prompt:
- type: "previous_response"
step: "code_analysis"
- type: "previous_response"
step: "security_scan"
extract: "vulnerabilities"
```
**Fields:**
- `step` (required): Name of the previous step
- `extract` (optional): Extract specific JSON field
- `extract_with` (optional): Use ContentExtractor for processing
- `summary` (optional): Generate summary of content
- `max_length` (optional): Limit content length
**Use Cases:**
- Building on previous analysis
- Passing structured data between steps
- Context accumulation across workflow
### 4. Session Context (`session_context`)
Reference conversation history from Claude sessions:
```yaml
prompt:
- type: "session_context"
session_id: "code_review_session"
include_last_n: 5
```
**Fields:**
- `session_id` (required): Session identifier
- `include_last_n` (optional): Number of recent messages to include
**Use Cases:**
- Multi-turn conversations
- Maintaining context across session restarts
- Referencing earlier decisions in long workflows
### 5. Claude Continue (`claude_continue`)
Continue existing Claude conversations with new prompts:
```yaml
prompt:
- type: "claude_continue"
new_prompt: "Now add comprehensive error handling to the implementation"
```
**Use Cases:**
- Extending existing implementations
- Iterative development workflows
- Progressive enhancement patterns
## File-Based Prompt Management
### Directory Structure Standards
#### `/pipelines/prompts/` - Prompt Template Library
Organized by purpose and domain:
```
pipelines/prompts/
├── analysis/
│ ├── code_review.md
│ ├── security_audit.md
│ ├── performance_analysis.md
│ └── dependency_check.md
├── generation/
│ ├── api_documentation.md
│ ├── test_generation.md
│ ├── code_scaffolding.md
│ └── tutorial_creation.md
├── extraction/
│ ├── data_parsing.md
│ ├── entity_extraction.md
│ └── content_summarization.md
├── validation/
│ ├── quality_checks.md
│ ├── compliance_review.md
│ └── output_validation.md
└── common/
├── system_prompts.md
├── error_handling.md
└── context_setup.md
```
#### Prompt File Naming Conventions
- **Descriptive names**: `security_vulnerability_scan.md` not `scan.md`
- **Action-oriented**: `generate_api_tests.md` not `api_tests.md`
- **Domain prefixes**: `frontend_component_analysis.md`, `backend_service_review.md`
- **Version suffixes**: `code_review_v2.md` for major updates
#### Prompt File Structure
Each prompt file should follow this template:
```markdown
# Prompt Title
## Purpose
Brief description of what this prompt accomplishes.
## Context Requirements
- List of required context or input data
- Expected format of inputs
- Prerequisites or dependencies
## Variables
Document any template variables used:
- `{PROJECT_TYPE}` - Type of project being analyzed
- `{LANGUAGE}` - Programming language
- `{FRAMEWORK}` - Framework or library being used
## Prompt Content
[Main prompt content here, using clear sections and examples]
## Expected Output Format
Description of expected response structure.
## Usage Examples
Reference to workflows that use this prompt.
## Version History
- v1.0 (2024-07-03): Initial version
- v1.1 (2024-07-04): Added error handling instructions
```
### Template Variables System
Support for dynamic prompt content:
```markdown
# Security Analysis Prompt
Analyze the {LANGUAGE} {PROJECT_TYPE} for security vulnerabilities.
Focus areas for {FRAMEWORK} projects:
- Authentication mechanisms
- Data validation
- Error handling
- Dependency security
## Code to Analyze
{CODE_CONTENT}
## Previous Findings
{PREVIOUS_ANALYSIS}
```
Usage in workflows:
```yaml
prompt:
- type: "file"
path: "pipelines/prompts/analysis/security_review.md"
variables:
LANGUAGE: "Python"
PROJECT_TYPE: "web application"
FRAMEWORK: "FastAPI"
- type: "file"
path: "src/main.py"
inject_as: "CODE_CONTENT"
- type: "previous_response"
step: "initial_scan"
inject_as: "PREVIOUS_ANALYSIS"
```
## Prompt Template Standards
### Template Categories
#### 1. Analysis Templates (`/analysis/`)
**Code Review Template** (`code_review.md`):
```markdown
# Code Review Analysis
## Objective
Perform comprehensive code review focusing on quality, security, and maintainability.
## Review Criteria
1. **Code Quality**
- Readability and clarity
- Proper naming conventions
- Code organization and structure
- Documentation completeness
2. **Security Assessment**
- Input validation
- Authentication and authorization
- Data handling and storage
- Dependency vulnerabilities
3. **Performance Considerations**
- Algorithm efficiency
- Resource usage
- Scalability factors
- Caching strategies
4. **Maintainability**
- Code modularity
- Test coverage
- Error handling
- Configuration management
## Output Format
Provide analysis in JSON format:
```json
{
"overall_score": 85,
"categories": {
"quality": {"score": 90, "issues": []},
"security": {"score": 80, "issues": []},
"performance": {"score": 85, "issues": []},
"maintainability": {"score": 85, "issues": []}
},
"critical_issues": [],
"recommendations": [],
"next_steps": []
}
```
```
**Security Audit Template** (`security_audit.md`):
```markdown
# Security Vulnerability Assessment
## Scope
Comprehensive security analysis covering OWASP Top 10 and industry best practices.
## Assessment Areas
1. **Authentication & Authorization**
- User authentication mechanisms
- Session management
- Access control implementation
- Multi-factor authentication
2. **Data Protection**
- Data encryption at rest and in transit
- Sensitive data handling
- PII protection measures
- Database security
3. **Input Validation**
- SQL injection prevention
- XSS protection
- CSRF safeguards
- Input sanitization
4. **Infrastructure Security**
- Server configuration
- Network security
- Dependency management
- Container security
## Risk Classification
- **Critical**: Immediate security risk, requires urgent attention
- **High**: Significant vulnerability, should be addressed soon
- **Medium**: Potential security concern, plan for resolution
- **Low**: Minor security improvement opportunity
## Output Requirements
Security assessment report with:
- Executive summary
- Detailed findings by category
- Risk-prioritized recommendations
- Remediation timeline suggestions
```
#### 2. Generation Templates (`/generation/`)
**API Documentation Template** (`api_documentation.md`):
```markdown
# API Documentation Generator
## Objective
Generate comprehensive API documentation from source code and specifications.
## Documentation Requirements
1. **API Overview**
- Purpose and scope
- Authentication methods
- Base URLs and versioning
- Rate limiting information
2. **Endpoint Documentation**
- HTTP methods and paths
- Request/response schemas
- Parameter descriptions
- Example requests and responses
- Error codes and messages
3. **Data Models**
- Schema definitions
- Validation rules
- Relationship mappings
- Example payloads
4. **Integration Guides**
- Getting started tutorial
- Common use cases
- Code examples in multiple languages
- Troubleshooting guide
## Output Format
Generate documentation in OpenAPI 3.0 specification format with accompanying Markdown guides.
```
**Test Generation Template** (`test_generation.md`):
```markdown
# Comprehensive Test Suite Generator
## Testing Strategy
Generate tests covering unit, integration, and end-to-end scenarios.
## Test Categories
1. **Unit Tests**
- Function-level testing
- Edge case coverage
- Error condition handling
- Mock and stub usage
2. **Integration Tests**
- API endpoint testing
- Database integration
- External service mocking
- Configuration testing
3. **End-to-End Tests**
- User journey testing
- Cross-browser compatibility
- Performance benchmarks
- Security testing
## Code Coverage Requirements
- Target: 90%+ line coverage
- 100% coverage for critical paths
- Include negative test cases
- Test error handling paths
## Test Framework Selection
Choose appropriate testing frameworks based on technology stack and project requirements.
```
#### 3. Extraction Templates (`/extraction/`)
**Data Parsing Template** (`data_parsing.md`):
```markdown
# Structured Data Extraction
## Extraction Objectives
Parse and structure unstructured or semi-structured data into standardized formats.
## Supported Input Formats
- Plain text documents
- CSV and TSV files
- JSON and XML data
- Log files and reports
- Configuration files
## Extraction Patterns
1. **Entity Recognition**
- Names, dates, and identifiers
- Technical specifications
- Configuration parameters
- Error messages and codes
2. **Relationship Mapping**
- Dependency relationships
- Hierarchical structures
- Sequential processes
- Cross-references
3. **Data Validation**
- Format compliance
- Completeness checks
- Consistency validation
- Quality scoring
## Output Schema
Provide extracted data in JSON format with metadata about extraction confidence and validation results.
```
#### 4. Validation Templates (`/validation/`)
**Quality Assurance Template** (`quality_checks.md`):
```markdown
# Quality Assurance Validation
## Quality Metrics
Comprehensive evaluation of deliverable quality across multiple dimensions.
## Evaluation Criteria
1. **Functional Correctness**
- Requirements compliance
- Feature completeness
- Business logic accuracy
- User experience quality
2. **Technical Excellence**
- Code quality standards
- Architecture compliance
- Performance benchmarks
- Security requirements
3. **Documentation Quality**
- Completeness and accuracy
- Clarity and organization
- Example quality
- Maintenance procedures
4. **Process Compliance**
- Development standards
- Review procedures
- Testing requirements
- Deployment readiness
## Validation Output
Quality scorecard with pass/fail status, improvement recommendations, and certification readiness assessment.
```
## Component Library Standards
### `/pipelines/components/` - Reusable Step Components
#### Component Categories
**Validation Steps** (`validation_steps.yaml`):
```yaml
# Reusable validation step components
components:
code_quality_check:
type: "gemini"
role: "brain"
model: "gemini-2.5-flash"
token_budget:
max_output_tokens: 2048
temperature: 0.3
prompt:
- type: "file"
path: "pipelines/prompts/validation/quality_checks.md"
- type: "previous_response"
step: "${source_step}"
functions:
- "evaluate_quality"
output_to_file: "quality_assessment.json"
security_validation:
type: "gemini"
role: "brain"
model: "gemini-2.5-flash"
token_budget:
max_output_tokens: 4096
temperature: 0.2
prompt:
- type: "file"
path: "pipelines/prompts/validation/security_audit.md"
- type: "file"
path: "${code_path}"
functions:
- "assess_security"
output_to_file: "security_report.json"
compliance_check:
type: "gemini"
role: "brain"
prompt:
- type: "file"
path: "pipelines/prompts/validation/compliance_review.md"
- type: "previous_response"
step: "${implementation_step}"
extract: "deliverables"
output_to_file: "compliance_status.json"
```
**Transformation Steps** (`transformation_steps.yaml`):
```yaml
components:
data_normalizer:
type: "gemini"
role: "brain"
model: "gemini-2.5-flash"
prompt:
- type: "file"
path: "pipelines/prompts/extraction/data_parsing.md"
- type: "file"
path: "${input_data_path}"
functions:
- "normalize_data"
output_to_file: "normalized_data.json"
content_summarizer:
type: "gemini"
role: "brain"
token_budget:
max_output_tokens: 1024
temperature: 0.4
prompt:
- type: "file"
path: "pipelines/prompts/extraction/content_summarization.md"
- type: "previous_response"
step: "${source_step}"
summary: true
max_length: 2000
output_to_file: "content_summary.json"
format_converter:
type: "claude"
role: "muscle"
claude_options:
max_turns: 5
allowed_tools: ["Write", "Read"]
output_format: "json"
prompt:
- type: "static"
content: "Convert the following data to ${target_format} format:"
- type: "previous_response"
step: "${source_step}"
output_to_file: "converted_${target_format}.${target_extension}"
```
**LLM Steps** (`llm_steps.yaml`):
```yaml
components:
smart_analysis:
type: "claude_smart"
preset: "analysis"
claude_options:
max_turns: 3
allowed_tools: ["Read"]
prompt:
- type: "file"
path: "pipelines/prompts/analysis/${analysis_type}.md"
- type: "file"
path: "${target_file}"
output_to_file: "${analysis_type}_result.json"
robust_implementation:
type: "claude_robust"
retry_config:
max_retries: 3
backoff_strategy: "exponential"
fallback_action: "simplified_prompt"
claude_options:
max_turns: 20
allowed_tools: ["Write", "Edit", "Read", "Bash"]
output_format: "json"
prompt:
- type: "file"
path: "pipelines/prompts/generation/${implementation_type}.md"
- type: "previous_response"
step: "${planning_step}"
output_to_file: "${implementation_type}_result.json"
session_continuation:
type: "claude_session"
session_config:
persist: true
session_name: "${workflow_name}_session"
max_turns: 50
prompt:
- type: "claude_continue"
new_prompt: "${continuation_instruction}"
output_to_file: "session_result.json"
```
#### Component Usage Patterns
**Including Components in Workflows**:
```yaml
workflow:
name: "comprehensive_analysis"
steps:
- name: "initial_scan"
type: "gemini"
prompt:
- type: "file"
path: "src/main.py"
# Use validation component
- <<: *code_quality_check
name: "quality_assessment"
variables:
source_step: "initial_scan"
# Use transformation component
- <<: *content_summarizer
name: "summary_generation"
variables:
source_step: "quality_assessment"
# Use LLM component with customization
- <<: *smart_analysis
name: "detailed_analysis"
variables:
analysis_type: "security_audit"
target_file: "src/main.py"
claude_options:
max_turns: 5 # Override component default
```
## Advanced Prompt Patterns
### 1. Progressive Enhancement Pattern
Build complexity gradually through connected prompts:
```yaml
steps:
- name: "basic_analysis"
type: "gemini"
prompt:
- type: "file"
path: "pipelines/prompts/analysis/basic_code_review.md"
- type: "file"
path: "src/main.py"
- name: "detailed_analysis"
type: "gemini"
prompt:
- type: "file"
path: "pipelines/prompts/analysis/detailed_security_audit.md"
- type: "previous_response"
step: "basic_analysis"
extract: "concerns"
- type: "file"
path: "src/main.py"
- name: "comprehensive_report"
type: "gemini"
prompt:
- type: "file"
path: "pipelines/prompts/generation/comprehensive_report.md"
- type: "previous_response"
step: "basic_analysis"
- type: "previous_response"
step: "detailed_analysis"
```
### 2. Context Accumulation Pattern
Build rich context across multiple steps:
```yaml
steps:
- name: "requirements_analysis"
type: "gemini"
prompt:
- type: "file"
path: "pipelines/prompts/analysis/requirements_review.md"
- type: "file"
path: "requirements.md"
- name: "architecture_review"
type: "gemini"
prompt:
- type: "file"
path: "pipelines/prompts/analysis/architecture_analysis.md"
- type: "file"
path: "architecture.md"
- type: "previous_response"
step: "requirements_analysis"
extract: "constraints"
- name: "implementation_plan"
type: "gemini"
prompt:
- type: "file"
path: "pipelines/prompts/generation/implementation_planning.md"
- type: "static"
content: "Requirements Analysis:"
- type: "previous_response"
step: "requirements_analysis"
- type: "static"
content: "\nArchitecture Review:"
- type: "previous_response"
step: "architecture_review"
```
### 3. Iterative Refinement Pattern
Refine outputs through multiple iterations:
```yaml
steps:
- name: "initial_draft"
type: "claude"
claude_options:
max_turns: 10
allowed_tools: ["Write"]
prompt:
- type: "file"
path: "pipelines/prompts/generation/initial_implementation.md"
- type: "file"
path: "requirements.md"
- name: "review_draft"
type: "gemini"
prompt:
- type: "file"
path: "pipelines/prompts/validation/implementation_review.md"
- type: "previous_response"
step: "initial_draft"
- name: "refine_implementation"
type: "claude_session"
session_config:
persist: true
continue_on_restart: true
prompt:
- type: "claude_continue"
new_prompt: |
Based on this review feedback, please refine the implementation:
- type: "previous_response"
step: "review_draft"
extract: "improvement_suggestions"
```
### 4. Parallel Processing Pattern
Process multiple aspects simultaneously:
```yaml
steps:
- name: "parallel_analysis"
type: "parallel_claude"
parallel_tasks:
- id: "security_analysis"
claude_options:
max_turns: 15
allowed_tools: ["Read"]
prompt:
- type: "file"
path: "pipelines/prompts/analysis/security_focus.md"
- type: "file"
path: "src/main.py"
output_to_file: "security_analysis.json"
- id: "performance_analysis"
claude_options:
max_turns: 15
allowed_tools: ["Read"]
prompt:
- type: "file"
path: "pipelines/prompts/analysis/performance_focus.md"
- type: "file"
path: "src/main.py"
output_to_file: "performance_analysis.json"
- id: "maintainability_analysis"
claude_options:
max_turns: 15
allowed_tools: ["Read"]
prompt:
- type: "file"
path: "pipelines/prompts/analysis/maintainability_focus.md"
- type: "file"
path: "src/main.py"
output_to_file: "maintainability_analysis.json"
- name: "synthesize_results"
type: "gemini"
prompt:
- type: "file"
path: "pipelines/prompts/synthesis/comprehensive_synthesis.md"
- type: "previous_response"
step: "parallel_analysis"
```
## Content Processing Features
### Enhanced Extraction Options
```yaml
prompt:
- type: "previous_response"
step: "code_analysis"
extract_with: "content_extractor" # Use ContentExtractor
format: "structured" # structured, summary, markdown
post_processing:
- "extract_code_blocks"
- "extract_recommendations"
- "extract_links"
include_metadata: true
max_length: 5000
```
### Content Summarization
```yaml
prompt:
- type: "file"
path: "large_specification.md"
summary: true
max_summary_length: 1000
- type: "previous_response"
step: "detailed_analysis"
summary: true
extract: "findings"
```
### Variable Injection
```yaml
prompt:
- type: "file"
path: "pipelines/prompts/analysis/project_analysis.md"
variables:
PROJECT_NAME: "MyApp"
LANGUAGE: "Python"
FRAMEWORK: "FastAPI"
- type: "file"
path: "${PROJECT_PATH}/src/main.py"
inject_as: "SOURCE_CODE"
```
## Best Practices
### 1. Prompt Organization
- **Single Responsibility**: Each prompt file should focus on one specific task
- **Clear Naming**: Use descriptive, action-oriented names
- **Version Control**: Track prompt evolution with version comments
- **Documentation**: Include purpose, context, and expected outputs
### 2. Template Design
- **Parameterization**: Use variables for reusable templates
- **Flexibility**: Design templates that work across different contexts
- **Clarity**: Write clear, unambiguous instructions
- **Examples**: Include examples of expected inputs and outputs
### 3. Component Architecture
- **Modularity**: Design components that can be easily combined
- **Configuration**: Support customization through variables
- **Reusability**: Create components that work across workflows
- **Testing**: Include test cases for component validation
### 4. Content Management
- **Caching**: Leverage file caching for performance
- **Size Limits**: Monitor and control prompt sizes
- **Processing**: Use content extraction for large inputs
- **Validation**: Verify prompt content before execution
### 5. Error Handling
- **Fallbacks**: Provide fallback prompts for error conditions
- **Validation**: Validate file paths and references
- **Recovery**: Design recovery strategies for failed prompts
- **Monitoring**: Track prompt performance and success rates
## Complete Examples
### Example 1: Full-Stack Application Analysis
```yaml
workflow:
name: "fullstack_app_analysis"
steps:
# Requirements gathering
- name: "requirements_analysis"
type: "gemini"
prompt:
- type: "file"
path: "pipelines/prompts/analysis/requirements_analysis.md"
- type: "file"
path: "docs/requirements.md"
- type: "file"
path: "docs/user_stories.md"
output_to_file: "requirements_analysis.json"
# Architecture review
- name: "architecture_review"
type: "gemini"
prompt:
- type: "file"
path: "pipelines/prompts/analysis/architecture_review.md"
- type: "file"
path: "docs/architecture.md"
- type: "previous_response"
step: "requirements_analysis"
extract: "technical_requirements"
output_to_file: "architecture_review.json"
# Parallel code analysis
- name: "code_analysis"
type: "parallel_claude"
parallel_tasks:
- id: "frontend_analysis"
claude_options:
max_turns: 15
allowed_tools: ["Read"]
prompt:
- type: "file"
path: "pipelines/prompts/analysis/frontend_analysis.md"
- type: "file"
path: "frontend/src"
output_to_file: "frontend_analysis.json"
- id: "backend_analysis"
claude_options:
max_turns: 15
allowed_tools: ["Read"]
prompt:
- type: "file"
path: "pipelines/prompts/analysis/backend_analysis.md"
- type: "file"
path: "backend/src"
output_to_file: "backend_analysis.json"
- id: "database_analysis"
claude_options:
max_turns: 10
allowed_tools: ["Read"]
prompt:
- type: "file"
path: "pipelines/prompts/analysis/database_analysis.md"
- type: "file"
path: "database/schema.sql"
output_to_file: "database_analysis.json"
# Security assessment
- name: "security_assessment"
type: "claude_smart"
preset: "analysis"
prompt:
- type: "file"
path: "pipelines/prompts/validation/comprehensive_security_audit.md"
- type: "previous_response"
step: "code_analysis"
- type: "previous_response"
step: "architecture_review"
extract: "security_considerations"
output_to_file: "security_assessment.json"
# Comprehensive report
- name: "final_report"
type: "gemini"
token_budget:
max_output_tokens: 8192
temperature: 0.4
prompt:
- type: "file"
path: "pipelines/prompts/generation/comprehensive_analysis_report.md"
- type: "static"
content: "## Requirements Analysis"
- type: "previous_response"
step: "requirements_analysis"
- type: "static"
content: "\n## Architecture Review"
- type: "previous_response"
step: "architecture_review"
- type: "static"
content: "\n## Code Analysis Results"
- type: "previous_response"
step: "code_analysis"
- type: "static"
content: "\n## Security Assessment"
- type: "previous_response"
step: "security_assessment"
output_to_file: "comprehensive_analysis_report.md"
```
### Example 2: Iterative Code Improvement
```yaml
workflow:
name: "iterative_code_improvement"
steps:
# Initial assessment
- name: "initial_assessment"
type: "gemini"
prompt:
- type: "file"
path: "pipelines/prompts/analysis/code_quality_assessment.md"
- type: "file"
path: "src/legacy_code.py"
functions:
- "assess_code_quality"
output_to_file: "initial_assessment.json"
# First improvement iteration
- name: "first_improvement"
type: "claude_robust"
retry_config:
max_retries: 3
backoff_strategy: "exponential"
claude_options:
max_turns: 20
allowed_tools: ["Read", "Write", "Edit"]
prompt:
- type: "file"
path: "pipelines/prompts/generation/code_improvement.md"
- type: "previous_response"
step: "initial_assessment"
extract: "improvement_priorities"
- type: "file"
path: "src/legacy_code.py"
output_to_file: "first_improvement.json"
# Review first iteration
- name: "review_first_iteration"
type: "gemini"
prompt:
- type: "file"
path: "pipelines/prompts/validation/improvement_review.md"
- type: "previous_response"
step: "first_improvement"
- type: "previous_response"
step: "initial_assessment"
extract: "quality_targets"
output_to_file: "first_review.json"
# Second improvement iteration
- name: "second_improvement"
type: "claude_session"
session_config:
persist: true
session_name: "code_improvement_session"
prompt:
- type: "claude_continue"
new_prompt: |
Based on the review feedback, please make the following additional improvements:
- type: "previous_response"
step: "review_first_iteration"
extract: "additional_improvements"
output_to_file: "second_improvement.json"
# Final validation
- name: "final_validation"
type: "gemini"
prompt:
- type: "file"
path: "pipelines/prompts/validation/final_quality_check.md"
- type: "previous_response"
step: "second_improvement"
- type: "previous_response"
step: "initial_assessment"
extract: "quality_targets"
functions:
- "validate_improvements"
output_to_file: "final_validation.json"
```
## Migration Guide
### Migrating from Inline Prompts
**Before (Inline):**
```yaml
steps:
- name: "analyze_code"
type: "gemini"
prompt:
- type: "static"
content: |
Analyze this code for security issues:
1. Check for SQL injection vulnerabilities
2. Look for XSS vulnerabilities
3. Review authentication mechanisms
4. Check for insecure data handling
Provide analysis in JSON format with severity levels.
```
**After (File-based):**
```yaml
steps:
- name: "analyze_code"
type: "gemini"
prompt:
- type: "file"
path: "pipelines/prompts/analysis/security_analysis.md"
```
### Creating Prompt Libraries
1. **Extract Common Patterns**: Identify frequently used prompt patterns
2. **Create Template Files**: Move prompts to organized template files
3. **Add Variables**: Parameterize templates for reusability
4. **Update Workflows**: Convert workflows to use file references
5. **Test Migration**: Verify equivalent functionality
### Component Migration
1. **Identify Reusable Steps**: Find step patterns used across workflows
2. **Create Component Definitions**: Extract steps into component files
3. **Parameterize Components**: Add variables for customization
4. **Update Workflows**: Use component references instead of inline definitions
5. **Validate Components**: Test components across different contexts
This guide provides a comprehensive framework for leveraging the Pipeline Prompt System's advanced capabilities. Use these patterns and standards to build maintainable, reusable, and powerful AI workflows.
