Task Manager REST API - Spec-Driven Development Assessment

Candidate: Vinay
GitHub Username: VinaSai128
Job ID: ALLY-REQ-043719/ALLY-REQ-043720
Scope: Backend-only application
Date Completed: February 11, 2026

Summary

Built a complete Task Manager REST API following spec-driven development methodology using Claude AI as the primary code generation tool. This project demonstrates effective use of AI to accelerate development while maintaining high code quality and comprehensive test coverage.

Spec-Driven Development Workflow

Following RULES.md requirements:

✅ Step 1: Specification First

• Created SPECS/task-manager-api.md BEFORE writing any code
• Defined 10 acceptance criteria (AC1-AC10)
• Documented all API endpoints, data models, and validation rules

✅ Step 2: TODO Tracking

• Maintained TODO.md to track implementation progress
• Followed TODO hygiene rules from RULES.md

✅ Step 3: Implementation

• Built Flask REST API following specification exactly
• Implemented 7 endpoints as specified
• Added comprehensive input validation and error handling

✅ Step 4: Testing

• Created test suite validating all 10 acceptance criteria
• 23 comprehensive tests covering all endpoints and edge cases
• 100% endpoint coverage

✅ Step 5: Documentation

• Created SETUP.md for user instructions
• Followed RULES.md: "LLM must not refer to README.md as context"

AI Code Generation Approach

Primary Tool: Claude (Anthropic AI)

How Claude AI Was Used:

1. Specification Phase

   • Generated detailed feature specification
   • Created comprehensive acceptance criteria
   • Defined API endpoints and data models

2. Implementation Phase

   • Generated Flask application structure
   • Created endpoint implementations following spec
   • Generated Task model class with proper validation

3. Testing Phase

   • Generated comprehensive pytest test suite
   • Created tests for each acceptance criterion
   • Added edge case and error scenario tests

4. Documentation Phase

   • Generated setup and usage instructions
   • Created API examples and troubleshooting guide

Benefits Achieved:
✅ Built complete API in ~2 hours
✅ Comprehensive test coverage from start
✅ Best practices applied automatically
✅ Clear documentation auto-generated

Features Implemented

API Endpoints (7 total)

Test Results

================================ 23 passed in 0.12s ==================================

All 10 acceptance criteria validated ✅

Technology Stack

• Backend: Python 3.13, Flask 3.1
• Testing: pytest 8.3
• CORS: Flask-CORS 5.0
• Storage: In-memory (Python list)
• AI Tool: Claude (Anthropic)

Running the Application

See SETUP.md for detailed instructions.

Quick Start:

python -m venv venv
.\venv\Scripts\activate
pip install -r requirements.txt
python app.py
pytest test_app.py -v

Key Achievements

✅ Working Product: All 7 endpoints functional and tested
✅ Test Coverage: 23 tests, 100% endpoint coverage, all passing
✅ Engineering Quality: Clean code, proper error handling
✅ AI Code Generation: Effective use of Claude AI
✅ Documentation: Clear setup and API docs
✅ Spec-Driven: Specification created first
✅ RULES.md Compliance: Followed all requirements

Total Development Time: ~2 hours
Status: Ready for review

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