con un clic
code-task-generator
// Generate structured .code-task.md files from rough descriptions or PDD plans.
// Generate structured .code-task.md files from rough descriptions or PDD plans.
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Implement coding tasks with an explore-plan-code workflow and explicit verification gates.
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| name | code-task-generator |
| description | Generate structured .code-task.md files from rough descriptions or PDD plans. |
| kind | sop |
This sop generates structured code task files from rough descriptions, ideas, or PDD implementation plans. It automatically detects the input type and creates properly formatted code task files following Amazon's code task format specification. For PDD plans, it processes implementation steps one at a time to allow for learning and adaptation between steps.
Constraints for parameter acquisition:
Automatically determine whether input is a description or PDD plan.
Constraints:
Parse and understand the input content based on detected mode.
Constraints:
Organize requirements and determine task breakdown based on mode.
Constraints:
Present task breakdown for user approval before generation.
Constraints:
Create appropriate file structure based on mode and approved plan.
Constraints:
step{NN} where NN is zero-padded (e.g., step01, step02, step10)task-01-{title}.code-task.md, task-02-{title}.code-task.md, etc..code-task.md extensionInform user about generated tasks and next steps.
Constraints:
Each code task file MUST follow this exact structure:
# Task: [Task Name]
## Description
[A clear description of what needs to be implemented and why]
## Background
[Relevant context and background information needed to understand the task]
## Reference Documentation
**Required:**
- Design: [path to detailed design document]
**Additional References (if relevant to this task):**
- [Specific research document or section]
**Note:** You MUST read the detailed design document before beginning implementation. Read additional references as needed for context.
## Technical Requirements
1. [First requirement]
2. [Second requirement]
3. [Third requirement]
## Dependencies
- [First dependency with details]
- [Second dependency with details]
## Implementation Approach
1. [First implementation step or approach]
2. [Second implementation step or approach]
## Acceptance Criteria
1. **[Criterion Name]**
- Given [precondition]
- When [action]
- Then [expected result]
2. **[Another Criterion]**
- Given [precondition]
- When [action]
- Then [expected result]
## Metadata
- **Complexity**: [Low/Medium/High]
- **Labels**: [Comma-separated list of labels]
- **Required Skills**: [Skills needed for implementation]
# Task: Create Email Validator Function
## Description
Create a function that validates email addresses and returns detailed error messages for invalid formats. This will be used across the application to ensure data quality and provide user-friendly feedback.
## Background
The application currently accepts any string as an email address, leading to data quality issues and failed communications. We need a robust validation function that can identify common email format errors and provide specific feedback to users.
## Reference Documentation
**Required:**
- Design: planning/design/detailed-design.md
**Additional References (if relevant to this task):**
- planning/research/validation-libraries.md (for email validation approach)
**Note:** You MUST read the detailed design document before beginning implementation. Read additional references as needed for context.
## Technical Requirements
1. Create a function that accepts an email string and returns validation results
2. Implement comprehensive email format validation using regex or email parsing library
3. Return detailed error messages for specific validation failures
4. Support common email formats including international domains
5. Include performance optimization for high-volume validation
## Dependencies
- Email validation library or regex patterns
- Error handling framework for structured error responses
- Unit testing framework for comprehensive test coverage
## Implementation Approach
1. Research and select appropriate email validation approach (regex vs library)
2. Implement core validation logic with specific error categorization
3. Add comprehensive error messaging for different failure types
4. Optimize for performance if needed for high-volume scenarios
## Acceptance Criteria
1. **Valid Email Acceptance**
- Given a properly formatted email address
- When the validation function is called
- Then the function returns success with no errors
2. **Invalid Format Detection**
- Given an email with invalid format (missing @, invalid characters, etc.)
- When the validation function is called
- Then the function returns failure with specific error message
3. **Detailed Error Messages**
- Given various types of invalid emails
- When validation fails
- Then specific error messages are returned (e.g., "Missing @ symbol", "Invalid domain format")
4. **Performance Requirements**
- Given 1000 email validations
- When executed in sequence
- Then all validations complete within 1 second
5. **Unit Test Coverage**
- Given the email validator implementation
- When running the test suite
- Then all validation scenarios have corresponding unit tests with >90% coverage
## Metadata
- **Complexity**: Low
- **Labels**: Validation, Email, Data Quality, Utility Function
- **Required Skills**: Regular expressions, email standards, unit testing
input: "I need a function that validates email addresses and returns detailed error messages"
output_dir: ".agents/tasks/my-project"
Detected mode: description
Generated code task: .agents/tasks/my-project/email-validator.code-task.md
Created task for email validation functionality with comprehensive acceptance criteria and implementation guidance.
Next steps: Run code-assist on the generated task to implement the solution.
input: ".agents/planning/my-project/implementation/plan.md"
Detected mode: pdd
Generated code tasks for step 2: .agents/tasks/my-project/step02/
Created tasks:
- task-01-create-data-models.code-task.md
- task-02-implement-validation.code-task.md
- task-03-add-serialization.code-task.md
Next steps: Run code-assist on each task in sequence
Step demo: Working data models with validation that can create, validate, and serialize/deserialize data objects
If the task description is too vague or unclear:
If the description suggests a very large or complex task:
If technical implementation details are unclear:
If the specified plan file doesn't exist:
If the plan doesn't follow expected PDD format:
If all steps in the checklist are marked complete: