1. Software Development

Enforces architectural patterns and conventions as first-class artifacts, requiring explicit overrides for violations.

Verifies code changes via test execution, build validation, and linting to maintain quality standards in development workflows.

Architectural code analysis tool for Python that evaluates design quality based on simplicity, functional core, and coupling principles.

Executes actual code modifications via Cursor CLI, delegating edits to cursor-agent based on analysis for refactoring, feature additions, and bug fixes.

Asks for clarification on underspecified requirements before implementation to prevent errors.

Analyzes codebases to identify structure, dependencies, patterns, and issues before modifications.

Provides performance optimization guidelines for React components, data fetching, and bundle management to enhance application efficiency.

Guides Python development with best practices for code quality, error handling, testing, and environment management in .py and .ipynb files.

Guides developers in using Databricks Python SDK, Connect, CLI, and REST API for building and managing Databricks workloads.

Converts Java Maven test scripts for RTS with OpenClover, handles multi-module exclusions, and resolves test instrumentation issues.

Converts C/C++ build and test scripts to support incremental builds, ensuring idempotency and Docker compatibility for efficient re-execution.

Analyzes JVM RTS test logs to extract errors, failed test classes, and categorize issues for debugging.

Analyzes RTS logs to suggest code modifications for build.sh and test.sh scripts, resolving build and test failures in Maven projects.

Provides best practices and guidance for modern frontend development using current technology stacks.

Enables state management in Streamlit applications using st.session_state for data persistence, widget state handling, and debugging.

Configures Python environments for Streamlit applications using uv for dependency management and app execution.

Optimizes Streamlit application performance by implementing caching, fragments, and efficient widget choices to reduce reruns and improve load times.

Structures Streamlit code using modular patterns, separation of concerns, and clean imports for improved maintainability.

Guides integration of third-party custom components into Streamlit applications, covering installation and usage scenarios.

Builds conversational UIs for chatbots and AI assistants using Streamlit's chat components, including message history and streaming responses.

Builds multi-page Streamlit applications with navigation and state management across pages for data-focused web applications.

Enables creation, debugging, and deployment of production-ready Streamlit applications for data-driven web interfaces.

Enables reading, manipulating, and generating PDF documents using Python libraries and command-line tools for software integration.

Enforces consistent coding style guidelines for Python and JavaScript development.