{"data":{"kind":"file","path":"README.md","version_id":"f398f2g1phkd2m535la92tll","entry":{"name":"README.md","path":"README.md","is_directory":false,"size":1024,"modified_at":"2026-08-06T02:21:34.516000","content_hash":"8e1914c39f42bd44a06716cc9aa95b10f93fa9af7bcc4f6cd266c212ba9c8c40"},"entries":[],"content":"# Documentation Generator\n\nA verifiers environment that evaluates an LLM's ability to generate high-quality documentation for Python code.\n\n## Overview\n\nThe model is given Python code snippets (functions, classes, modules) and must produce comprehensive documentation including:\n- Clear description of what the code does\n- Parameter documentation with types and descriptions\n- Return value documentation\n- Usage examples\n- Edge cases and error handling notes\n\n## Evaluation Metrics\n\n- **Completeness**: All parameters, return values, and exceptions documented\n- **Accuracy**: Documentation correctly describes the code's behavior\n- **Clarity**: Documentation is readable and well-structured\n- **Example Quality**: Usage examples are correct and illustrative\n- **Format Compliance**: Follows standard docstring conventions (Google/NumPy style)\n\n## Usage\n\n```python\nimport verifiers as vf\nenv = vf.load_environment(\"rayy/doc-generator\")\n```\n\n## Tags\n- documentation\n- code-generation\n- python\n- docstrings\n- technical-writing\n","encoding":"utf-8","truncated":false,"total_bytes":1024},"status":null}