Specify a webhook-driven task for OpenClaw
Describe event inputs, validation, deduplication, and intended actions before integration work.
Reviews code changes for bugs, style issues, and best practices. Use when looking for errors or improving code quality.
--- name: code-review-ac73390d80fc description: Reviews code changes for bugs, style issues, and best practices. Use when looking for errors or improving code quality. --- # Code Review Skill When reviewing code, systematically check the following areas: ## 1. Correctness & Logic - **Boundary Conditions**: Are edge cases (empty lists, None values, zero division) handled? - **Error Handling**: Are exceptions caught specifically? (No bare `except:`). - **Type Safety**: Do function signatures match their usage? ## 2. Architecture & Design - **Single Responsibility**: Does the function/class do one thing? - **Coupling**: Are dependencies manageable? (e.g., UI code shouldn't import low-level processors directly if possible). - **Resource Management**: Are files and GPU memory released properly? (Look for `try...finally` or context managers). ## 3. Style & Conventions - **Naming**: Do names reflect intent? (e.g., `pnm_adjacency` vs `adjacency`). - **Docstrings**: Are complex logic blocks explained? - **Imports**: Are imports grouped and sorted? ## 4. Performance (Specific to CT/Volume Data) - **Vectorization**: Is `numpy`/`cupy` used instead of explicit loops? - **Memory**: Are large arrays copied unnecessarily? - **GPU Usage**: Is generic `try-import` used for optional GPU backends? ## Output Format Provide feedback in a structured list: - **Severity**: [Critical/Major/Minor] - **File**: [Filename] - **Issue**: [Description] - **Suggestion**: [Proposed Fix]--- name: code-review-ac73390d80fc description: Reviews code changes for bugs, style issues, and best practices. Use when looking for errors or improving code quality. --- # Code Review Skill When reviewing code, systematically check the following areas: ## 1. Correctness & Logic - **Boundary Conditions**: Are edge cases (empty lists, None values, zero division) handled? - **Error Handling**: Are exceptions caught specifically? (No bare `except:`). - **Type Safety**: Do function signatures match their usage? ## 2. Architecture & Design - **Single Responsibility**: Does the function/class do one thing? - **Coupling**: Are dependencies manageable? (e.g., UI code shouldn't import low-level processors directly if possible). - **Resource Management**: Are files and GPU memory released properly? (Look for `try...finally` or context managers). ## 3. Style & Conventions - **Naming**: Do names reflect intent? (e.g., `pnm_adjacency` vs `adjacency`). - **Docstrings**: Are complex logic blocks explained? - **Imports**: Are imports grouped and sorted? ## 4. Performance (Specific to CT/Volume Data) - **Vectorization**: Is `numpy`/`cupy` used instead of explicit loops? - **Memory**: Are large arrays copied unnecessarily? - **GPU Usage**: Is generic `try-import` used for optional GPU backends? ## Output Format Provide feedback in a structured list: - **Severity**: [Critical/Major/Minor] - **File**: [Filename] - **Issue**: [Description] - **Suggestion**: [Proposed Fix]
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.