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‎.cursor/commands/batch-problem-creation.md‎

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# Batch Problem Creation Command
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## Assistant Workflow
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When user requests **batch creation of multiple problems**, the assistant will:
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1. **Get count from user** (default: 5 problems)
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2. **Loop through each problem** following the complete workflow
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3. **For each problem**:
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- Find next problem via `poetry run python .cursor/.dev/next_problem.py`
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- Follow all steps from `.cursor/commands/problem-creation.md`
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- Verify reproducibility and quality via `.cursor/commands/test-quality-assurance.md`
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4. **Provide batch summary** at the end
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## High-Level Process
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### Step 1: Initialize Batch
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- Ask user for count (default: 5)
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- Confirm batch creation parameters
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- Set up progress tracking
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### Step 2: Problem Creation Loop
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For each problem (1 to count):
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#### 2.1: Find Next Problem
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```bash
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poetry run python .cursor/.dev/next_problem.py
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```
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- Extract problem number and name from output
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- Log progress: "Problem X/Count: #NUMBER - NAME"
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#### 2.2: Follow Problem Creation Workflow
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Execute complete workflow from `.cursor/commands/problem-creation.md`:
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1. **Scrape** problem data using `poetry run lcpy scrape`
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2. **Transform** data into proper JSON template format
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3. **Include images** - Extract image URLs and add to readme_examples
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4. **Create** JSON file in `leetcode_py/cli/resources/leetcode/json/problems/{problem_name}.json`
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5. **Update** Makefile with `PROBLEM ?= {problem_name}`
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6. **Generate** problem structure using `make p-gen`
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7. **Verify** with `make p-lint` and fix template issues
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8. **Iterate** if needed: re-run `make p-gen PROBLEM={problem_name} FORCE=1` and `make p-lint`
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#### 2.3: Implement Optimal Solution
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**CRITICAL**: Before running quality assurance, implement the optimal solution:
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1. **Implement solution**: Write the optimal algorithm in `solution.py`
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2. **Add multiple approaches**: Include alternative solutions (e.g., Solution, SolutionOptimized)
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3. **Use parametrized testing**: Ensure all solution approaches are tested
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4. **Verify correctness**: Solution must handle all test cases correctly
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#### 2.4: Quality Assurance
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Execute workflow from `.cursor/commands/test-quality-assurance.md`:
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1. **Run tests**: `make p-test PROBLEM={problem_name}`
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2. **Check test coverage**: Ensure minimum 12 test cases
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3. **Enhance if needed**: Update JSON with more test cases
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4. **Verify reproducibility**: Ensure all tests pass consistently
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5. **Final verification**: `make p-lint PROBLEM={problem_name}`
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### Step 3: Batch Summary
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Provide comprehensive summary:
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- Total problems created
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- Success rate
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- Failed problems (if any) with reasons
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- Time taken
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- Next steps for any failures
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## Example Assistant Response
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```
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I'll help you create 5 problems in batch. Let me start the process:
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=== Batch Problem Creation Started ===
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Creating 5 problems...
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=== Problem 1/5 ===
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Finding next problem...
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Running: poetry run python .cursor/.dev/next_problem.py
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Next problem: Problem #123 - Word Ladder
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Processing: #123 - Word Ladder
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Following problem creation workflow:
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1. Scraping problem data...
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Running: poetry run lcpy scrape -n 123
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✓ Scraped successfully
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2. Creating JSON template...
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✓ JSON template created with images included
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✓ Saved to leetcode_py/cli/resources/leetcode/json/problems/word_ladder.json
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3. Updating Makefile...
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✓ Updated PROBLEM ?= word_ladder
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4. Generating problem structure...
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Running: make p-gen PROBLEM=word_ladder
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✓ Problem structure generated
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5. Verifying with linting...
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Running: make p-lint PROBLEM=word_ladder
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✓ Linting passed
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6. Implementing optimal solution...
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✓ Solution implemented with multiple approaches
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✓ Parametrized testing configured
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7. Running quality assurance...
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Running: make p-test PROBLEM=word_ladder
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✓ Tests passed (15 test cases)
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✓ Quality assurance completed
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=== Problem 2/5 ===
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[Continue with same process...]
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=== Batch Summary ===
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Total Problems Created: 5/5
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Success Rate: 100%
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Failed Problems: None
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Time Taken: 12 minutes
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Next Steps: All problems created successfully!
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=== Batch Problem Creation Completed ===
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```
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## Error Handling
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### If a problem fails:
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1. **Log the failure** with specific reason
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2. **Continue with next problem** (don't stop the batch)
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3. **Add to retry list** for manual intervention later
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4. **Update success rate** accordingly
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### Common failure scenarios:
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- Scraping fails (problem not found)
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- JSON template issues (invalid syntax)
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- Generation fails (missing dependencies)
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- Tests fail (incorrect expected values)
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- Linting errors (template problems)
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### Recovery actions:
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- **Scraping**: Try alternative parameters or manual data entry
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- **JSON**: Fix syntax and regenerate
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- **Generation**: Check Makefile and dependencies
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- **Tests**: Update expected values in JSON template
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- **Linting**: Fix template issues and regenerate
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## Success Criteria
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Each problem must meet:
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- ✅ All files generated (README.md, solution.py, test_solution.py, helpers.py, playground.ipynb, **init**.py)
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- ✅ **Optimal solution implemented** with correct algorithm
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- ✅ **Multiple solution approaches** (e.g., Solution, SolutionOptimized)
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- ✅ **Parametrized testing** for all solution approaches
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- ✅ Linting passes without errors
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- ✅ All tests pass
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- ✅ Minimum 12 comprehensive test cases
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- ✅ Images included in README if available
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- ✅ Proper JSON template created
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- ✅ Code coverage includes edge cases
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## Batch Parameters
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- **Count**: Number of problems to create (default: 5)
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- **Tags**: Optional filter for specific problem lists
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- **Force**: Whether to overwrite existing problems
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- **Dry Run**: Preview mode without actual creation
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## Notes
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- **Sequential Processing**: Create one problem at a time to avoid conflicts
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- **Progress Tracking**: Show clear progress indicators
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- **Error Recovery**: Continue batch even if individual problems fail
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- **Solution Implementation**: **MUST** implement optimal solution before quality assurance
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- **Quality Focus**: Ensure each problem meets all quality standards
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- **Reproducibility**: Verify tests pass consistently with implemented solutions
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- **Documentation**: Maintain clear logs of the entire process

‎.cursor/commands/problem-creation.md‎

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@@ -50,6 +50,12 @@ Required fields for `leetcode_py/cli/resources/leetcode/json/problems/{problem_n
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- `playground_assertion`: Use single quotes for string literals
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- Double quotes in JSON + cookiecutter + Jupyter notebook = triple escaping issues
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**Test Cases Format:**
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- `test_cases`: Use structured format with `{"list": ["..."]}` instead of string arrays
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- Each test case should be a string representation of the tuple/parameters
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- Example: `{"list": ["('input1', 'input2', expected)", "('input3', 'input4', expected)"]}`
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**IMPORTANT: Create actual JSON files, not JSON5**
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The template below uses JSON5 format with comments for documentation purposes only. When creating the actual `.json` file, you must:
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parametrize: "s, t, expected", // pytest parametrize parameters
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// For tree: "root_list, expected_list"
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// For design: "operations, inputs, expected"
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test_cases: "[('anagram', 'nagaram', True), ('rat', 'car', False), ('listen', 'silent', True), ('hello', 'bello', False), ('', '', True), ('a', 'a', True), ('a', 'b', False), ('ab', 'ba', True), ('abc', 'bca', True), ('abc', 'def', False), ('aab', 'abb', False), ('aabbcc', 'abcabc', True), ('abcd', 'abcde', False), ('race', 'care', True), ('elbow', 'below', True), ('study', 'dusty', True), ('night', 'thing', True), ('stressed', 'desserts', True)]",
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// For tree: "[([4, 2, 7, 1, 3, 6, 9], [4, 7, 2, 9, 6, 3, 1]), ([2, 1, 3], [2, 3, 1]), ([], [])]"
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// For design: "[(['LRUCache', 'put', 'get'], [[2], [1, 1], [1]], [None, None, 1])]"
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test_cases: {
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list: [
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"('anagram', 'nagaram', True)",
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"('rat', 'car', False)",
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"('listen', 'silent', True)",
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"('hello', 'bello', False)",
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"('', '', True)",
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"('a', 'a', True)",
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"('a', 'b', False)",
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"('ab', 'ba', True)",
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"('abc', 'bca', True)",
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"('abc', 'def', False)",
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"('aab', 'abb', False)",
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"('aabbcc', 'abcabc', True)",
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"('abcd', 'abcde', False)",
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"('race', 'care', True)",
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"('elbow', 'below', True)",
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"('study', 'dusty', True)",
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"('night', 'thing', True)",
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"('stressed', 'desserts', True)",
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],
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},
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// For tree: {"list": ["([4, 2, 7, 1, 3, 6, 9], [4, 7, 2, 9, 6, 3, 1])", "([2, 1, 3], [2, 3, 1])", "([], [])"]}
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// For design: {"list": ["(['LRUCache', 'put', 'get'], [[2], [1, 1], [1]], [None, None, 1])"]}
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body: " result = run_is_anagram(Solution, s, t)\n assert_is_anagram(result, expected)",
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// For tree: " result = run_invert_tree(Solution, root_list)\n assert_invert_tree(result, expected_list)"
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// For design: " result, _ = run_lru_cache(LRUCache, operations, inputs)\n assert_lru_cache(result, expected)"
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// - solution_class_name: "Solution"
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// - solution_imports: ""
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// - Simple method signatures: "(self, s: str, t: str) -> bool"
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// - Basic test cases: string/number parameters
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// - Basic test cases: structured format with {"list": ["..."]}
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// - Playground: single quotes for strings
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//
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// TREE PROBLEMS (invert_binary_tree):
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// - solution_class_name: "Solution"
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// - solution_imports: "from leetcode_py import TreeNode"
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// - Tree method signatures: "(self, root: TreeNode[int] | None) -> TreeNode[int] | None"
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// - Helper functions use TreeNode.from_list()
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// - Test cases: list representations of trees
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// - Test cases: structured format with list representations of trees
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// - Playground: TreeNode imports and list conversions
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//
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// LINKED LIST PROBLEMS (merge_two_sorted_lists):
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// - solution_class_name: "Solution"
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// - solution_imports: "from leetcode_py import ListNode"
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// - List method signatures: "(self, list1: ListNode[int] | None, list2: ListNode[int] | None) -> ListNode[int] | None"
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// - Helper functions use ListNode.from_list()
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// - Test cases: list representations of linked lists
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// - Test cases: structured format with list representations of linked lists
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// - Playground: ListNode imports and list conversions
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//
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// DESIGN PROBLEMS (lru_cache):
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// - solution_class_name: "LRUCache" (custom class name)
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// - Multiple methods including __init__
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// - Operations-based testing: operations, inputs, expected arrays
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// - Operations-based testing: structured format with operations, inputs, expected arrays
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// - Complex test body with operation loops
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// - Helper functions return (results, instance) for debugging
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// - Playground: print results, return instance
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// - solution_class_name: "Trie(DictTree[str])" (with inheritance)
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// - solution_imports: "from leetcode_py.data_structures import DictTree, RecursiveDict"
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// - Custom class with inheritance from DictTree
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// - Operations-based testing like design problems
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// - Operations-based testing with structured format like design problems
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// - Helper functions return (results, instance) for debugging
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//
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// MULTIPLE SOLUTIONS (invert_binary_tree, lru_cache):

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