Review Repository

Evaluates the overall quality and structure of the repository and creates an improvement roadmap.

Procedure

1. Understand Directory Structure:

   • Use list_dir to obtain an overview of the project (e.g., gwexpy, tests, docs, examples).
   • Use find_by_name to check the distribution of major file formats (.py, .ipynb, .md, .toml).

2. Verify Design and Dependencies:

   • Read README.md to understand the project's purpose and key features.
   • Check pyproject.toml or setup.py to grasp dependencies and build configurations.

3. Code Quality Screening:

   • Use grep_search to find anti-patterns or lingering items:
     • except Exception: (Overly broad exception catching)
     • pass (Empty blocks)
     • TODO, FIXME, XXX
     • Support code for older Python versions (sys.version_info)
   • Use view_file_outline to check if methods in major classes have type hints and docstrings.

4. Verification of Testing and QA:

   • Assess the scale and status of tests using the run_tests skill or run_command (pytest --collect-only).
   • Check CI configurations such as GitHub Actions (.github/workflows).

5. Report Generation and Task Creation:

   • Create a report including the following sections:
     • Overview: Project scale and current state.
     • Strengths: Points of good implementation and design.
     • Improvements (P1: High, P2: Med, P3: Low): Specific issues organized by priority.
   • Based on the report, save a "Markdown-formatted improvement task prompt" in the .agent/ directory that is easy for the AI to understand.