القائمة
Review Python code for bugs, edge cases, and pitfalls specific to ML, data, API, and deployment contexts (data leakage, tensor shapes, device placement, missing timeouts, hardcoded secrets, resource leaks). Reports findings in chat without editing files.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
Generate commit messages for staged git changes. Use when the user asks to generate a commit message or wants help writing commit messages.
Debug errors using logs and stack traces. Use when the user shares an error message, crash log, or runtime issue to fix.
Explains code with visual diagrams and analogies. Use when explaining how code works, teaching about a codebase, or when the user asks "how does this work?"
Explain errors without editing files. Use when the user wants to understand an error or crash but not immediately fix it.
Analyze code for logical errors, runtime bugs, and edge cases. Use when the user wants a code review, asks to find bugs, or wants to check code correctness.
Image manipulation via a bundled script, resizing, rotation, flipping, cropping, padding, format conversion (JPEG/PNG/WebP/TIFF/HEIC), transparency operations (remove/replace/extract/blend), grayscale conversion, auto-cropping borders, and file size optimization.
استنادا إلى تصنيف SOC المهني
| name | review-python-code |
| description | Review Python code for bugs, edge cases, and pitfalls specific to ML, data, API, and deployment contexts (data leakage, tensor shapes, device placement, missing timeouts, hardcoded secrets, resource leaks). Reports findings in chat without editing files. |
| when_to_use | Use when the user wants Python reviewed with an ML/API/deployment lens, e.g. "review this training script", "check my inference code". For a general diff review, prefer the built-in code-review skill. |
| argument-hint | ["python-files"] |
| effort | high |
| allowed-tools | Read, Glob, Grep |
Review Python code for bugs, edge cases, and pitfalls in ML/API/deployment contexts.
ML & Data
eval(), gradient accumulation), incorrect loss/metric usageAPIs & Requests
Deployment