batch-problem-creation

name	batch-problem-creation
argument-hint	[count]
description	Batch creation workflow for multiple LeetCode problems - loops through problem creation, solution implementation, and quality assurance for a specified count. Accepts optional count argument (e.g., /batch-problem-creation 10). Defaults to 5 if no argument. Use ONLY when user explicitly requests batch creation via /batch-problem-creation command.

Batch Problem Creation Command

Assistant Workflow

When user requests batch creation of multiple problems, the assistant will:

1. Parse count: Use $ARGUMENTS as count if provided (must be valid integer). Otherwise ask user (default: 5 problems)
2. Loop through each problem following the complete workflow
3. For each problem:
   • Find next problem via uv run python .claude/.dev/next_problem.py
   • Follow all steps from @.claude/skills/problem-creation.md
   • MANDATORY: Read and follow @.claude/skills/test-quality-assurance.md for quality verification
4. Provide batch summary at the end

CRITICAL INSTRUCTION: You MUST read the test-quality-assurance.md file before executing quality assurance for any problem. Do not rely on memory or assumptions about the workflow.

High-Level Process

Step 1: Initialize Batch

• Ask user for count (default: 5)
• Confirm batch creation parameters
• Set up progress tracking

Step 2: Problem Creation Loop

For each problem (1 to count):

2.1: Find Next Problem

uv run python .claude/.dev/next_problem.py

• Extract problem number and name from output
• Log progress: "Problem X/Count: #NUMBER - NAME"
• Note: The script automatically excludes unscrapable problems (premium, API issues, etc.)

2.2: Follow Problem Creation Workflow

Execute complete workflow from @.claude/skills/problem-creation.md:

1. Scrape problem data using uv run lcpy scrape
2. Transform data into proper JSON template format
3. Include images - Extract image URLs and add to readme_examples
4. Create JSON file in leetcode_py/cli/resources/leetcode/json/problems/{problem_name}.json
5. Update tags.json5 with problem name and tags
6. Generate problem structure using bake p-gen
7. Verify with bake lint and fix template issues
8. Iterate if needed: re-run bake p-gen -p {problem_name} -f and bake lint

2.3: Implement Optimal Solution

CRITICAL: Before running quality assurance, implement the optimal solution:

1. Implement solution: Write the optimal algorithm in solution.py - implement only 1 solution in the Solution class, no need to add more classes
2. Verify correctness: Solution must handle all test cases correctly

2.4: Quality Assurance & Reproducibility Verification

MANDATORY: You MUST read and follow the complete workflow from .claude/skills/test-quality-assurance.md for EVERY problem.

REQUIRED ACTION: Before proceeding with quality assurance, you MUST:

1. Read the file: @.claude/skills/test-quality-assurance.md
2. Follow the exact 4-step process described in that file
3. Execute each step as specified in the test-quality-assurance.md workflow

CRITICAL: Do NOT proceed without reading the test-quality-assurance.md file first. The workflow includes specific backup, regenerate, and restore steps that must be followed exactly.

Step 3: Batch Summary

Provide comprehensive summary:

• Total problems created
• Success rate
• Failed problems (if any) with reasons
• Time taken
• Next steps for any failures

Example Assistant Response

I'll help you create 5 problems in batch. Let me start the process:

=== Batch Problem Creation Started ===
Creating 5 problems...

=== Problem 1/5 ===
Finding next problem...
Running: uv run python .claude/.dev/next_problem.py
Next problem: Problem #123 - Word Ladder
Processing: #123 - Word Ladder

Following problem creation workflow:
1. Scraping problem data...
   Running: uv run lcpy scrape -n 123
   ✓ Scraped successfully

2. Creating JSON template...
   ✓ JSON template created with images included
   ✓ Saved to leetcode_py/cli/resources/leetcode/json/problems/word_ladder.json

3. Generating problem structure...
   Running: bake p-gen -p word_ladder
   ✓ Problem structure generated

4. Verifying with linting...
   Running: bake lint
   ✓ Linting passed

5. Implementing optimal solution...
   ✓ Solution implemented with multiple approaches
   ✓ Parametrized testing configured

6. Running quality assurance...
   Running: bake p-test -p word_ladder
   ✓ Tests passed (15 test cases)
   ✓ Quality assurance completed

=== Problem 2/5 ===
[Continue with same process...]

=== Batch Summary ===
Total Problems Created: 5/5
Success Rate: 100%
Failed Problems: None
Time Taken: 12 minutes
Next Steps: All problems created successfully!

=== Batch Problem Creation Completed ===

Error Handling

If a problem fails:

1. Log the failure with specific reason
2. Continue with next problem (don't stop the batch)
3. Add to retry list for manual intervention later
4. Update success rate accordingly

Common failure scenarios:

• Scraping fails (problem not found, premium problem, API issues)
• JSON template issues (invalid syntax)
• Generation fails (missing dependencies)
• Tests fail (incorrect expected values)
• Linting errors (template problems)
• Unscrapable problems (automatically excluded by next_problem.py)

Recovery actions:

• Scraping: Try alternative parameters or manual data entry
• JSON: Fix syntax and regenerate
• Generation: Check bakefile and dependencies
• Tests: Update expected values in JSON template
• Linting: Fix template issues and regenerate
• Unscrapable: Add to .claude/.dev/problem_lists/unscrapable.py and continue with next problem

CRITICAL: Never edit generated files directly (helpers.py, test_solution.py, README.md, etc.). Always fix issues in the JSON template and regenerate to ensure reproducibility. The ONLY exception is solution.py implementation - you may edit this file directly to implement the optimal solution.

Success Criteria

Each problem must meet:

• ✅ All files generated (README.md, solution.py, test_solution.py, helpers.py, playground.ipynb, init.py)
• ✅ Optimal solution implemented with correct algorithm
• ✅ Multiple solution approaches (e.g., Solution, SolutionOptimized)
• ✅ Parametrized testing for all solution approaches
• ✅ Linting passes without errors
• ✅ All tests pass
• ✅ test-quality-assurance.md file read and complete workflow executed
• ✅ Minimum 12 comprehensive test cases (verified via check_test_cases tool)
• ✅ Test reproducibility verified (tests pass consistently)
• ✅ Images included in README if available
• ✅ Proper JSON template created and updated
• ✅ Code coverage includes edge cases
• ✅ Original solution preserved after quality assurance process

Batch Parameters

• Count: Number of problems to create (default: 5)
• Tags: Optional filter for specific problem lists
• Force: Whether to overwrite existing problems
• Dry Run: Preview mode without actual creation

Unscrapable Problems Management

Adding Unscrapable Problems

When encountering a problem that cannot be scraped (premium, API issues, etc.):

1. Add to unscrapable list: Update .claude/.dev/problem_lists/unscrapable.py
2. Format: (problem_number, "problem-name")
3. Continue batch: The next_problem.py script will automatically skip unscrapable problems

Example unscrapable.py entry:

UNSCRAPABLE_PROBLEMS = [
    (252, "meeting-rooms"),
    (253, "meeting-rooms-ii"),  # if also unscrapable
    # Add more as discovered
]

Notes

• Sequential Processing: Create one problem at a time to avoid conflicts
• Progress Tracking: Show clear progress indicators
• Error Recovery: Continue batch even if individual problems fail
• Solution Implementation: MUST implement optimal solution before quality assurance
• Quality Focus: Ensure each problem meets all quality standards
• Reproducibility: Verify tests pass consistently with implemented solutions
• Documentation: Maintain clear logs of the entire process
• Unscrapable Handling: Automatically exclude known unscrapable problems