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AI Fix Prompts for Gamestudy

Copy any prompt below into Claude, ChatGPT, or your AI coding assistant to automatically fix the issue. Each prompt includes full context, code location, and step-by-step fix instructions.

4

Total Prompts

0

Critical (P0)

1

High (P1)

3

Medium (P2)

0

Low (P3)

HIGH ⚙ moderate #1

Fix quality gate failures (1 conditions)

quality-gate quality

Expected outcome: All quality gate conditions pass

Files to modify: Will be determined by the AI

Prompt (copy this into your AI assistant)

Repository 'r-papir__gamestudy' is failing the quality gate.

Failed conditions:
- overall_score: actual 40.5 >= 50 (FAILED)

Fix each failing condition to make the repo pass the quality gate.

Repobility · MCP-ready · https://repobility.com

MEDIUM ⚡ quick-fix #2

Add a LICENSE file

license legal

Expected outcome: LICENSE file added

Files to modify: Will be determined by the AI

Prompt (copy this into your AI assistant)

Repository 'r-papir__gamestudy' has no detectable license. Add a LICENSE file. For open-source projects, MIT is recommended unless dependencies require a specific license. Create the full file.

MEDIUM ⚙ moderate #3

Fix 6 scorecard failures (40%)

scorecard compliance

Expected outcome: Scorecard score improved from 40% to 80%+

Files to modify: Will be determined by the AI

Prompt (copy this into your AI assistant)

Repository 'r-papir__gamestudy' fails 6 scorecard checks (score: 40%):

- Has LICENSE: Repository has a LICENSE file
- Has CI/CD: Repository has CI/CD configuration
- Has Tests: Repository has test files
- Has Docker: Repository has Dockerfile
- Quality Gate Passed: Passes the default quality gate
- Grade C or Above: Overall quality grade is C or better

Fix each failing check.

MEDIUM ⚒ significant #4

Simplify 5 high-complexity files

complexity refactoring quality

Expected outcome: All listed files reduced to medium or low complexity

Files to modify: mediapipe/face_mesh/face_mesh_solution_simd_wasm_bin.js, mediapipe/face_mesh/face_mesh_solution_wasm_bin.js, NLP_program.py, data_analysis.py, prepare_order_effects_data.py

Prompt (copy this into your AI assistant)

These files in 'r-papir__gamestudy' have high cyclomatic complexity:

- **mediapipe/face_mesh/face_mesh_solution_simd_wasm_bin.js**: complexity=2514, max nesting=0, longest function=12 lines
- **mediapipe/face_mesh/face_mesh_solution_wasm_bin.js**: complexity=2514, max nesting=0, longest function=12 lines
- **NLP_program.py**: complexity=329, max nesting=9, longest function=130 lines
- **data_analysis.py**: complexity=283, max nesting=8, longest function=100 lines
- **prepare_order_effects_data.py**: complexity=66, max nesting=6, longest function=157 lines

For each file:
1. Break large functions into smaller, focused functions
2. Reduce nesting depth (extract early returns, use guard clauses)
3. Simplify conditional logic
4. Extract complex expressions into named variables