partial-tables

Edit drafts into sharper, more human writing while preserving the writer's personal voice, or detect AI-slop patterns without rewriting. Use when the user wants a draft clearer, more direct, more opinionated, or less AI-sounding, or asks whether writing reads as AI.

Review a GitHub pull request with parallel specialized agents and post inline review comments rated by severity. Reviews the PR's changed lines and caps minor nitpicks; does not fix anything. Use when asked to review a PR or run /code-review.

Review a GitHub pull request with parallel specialized agents and post inline review comments rated by severity. Reviews the PR's changed lines and caps minor nitpicks; does not fix anything. Use when asked to review a PR or run /code-review.

Strictly review the branch's changes for reuse, simplification, abstraction quality, and maintainability, then fix the issues. An ambitious code-quality pass that hunts for structural simplifications (code-judo moves), giant files, and spaghetti-condition growth.

Run a large-scale, whole-repository refactor — code simplification, slop and code-smell removal, de-duplication, and cross-module consistency — with behavior preserved and uncertain changes escalated to the user. Use when asked to refactor the codebase, clean up at scale, or consolidate duplicated logic.

Run a large-scale, whole-repository refactor — code simplification, slop and code-smell removal, de-duplication, and cross-module consistency — with behavior preserved and uncertain changes escalated to the user. Use when asked to refactor the codebase, clean up at scale, or consolidate duplicated logic.

Common Python anti-patterns to avoid. Use as a checklist when reviewing code, before finalizing implementations, or when debugging issues that might stem from known bad practices.

Python background job patterns including task queues, workers, and event-driven architecture. Use when implementing async task processing, job queues, long-running operations, or decoupling work from request/response cycles.

Python configuration management via environment variables and typed settings. Use when externalizing config, setting up pydantic-settings, managing secrets, or implementing environment-specific behavior.

Python design patterns including KISS, Separation of Concerns, Single Responsibility, and composition over inheritance. Use when making architecture decisions, refactoring code structure, or evaluating when abstractions are appropriate.

Python error handling patterns including input validation, exception hierarchies, and partial failure handling. Use when implementing validation logic, designing exception strategies, handling batch processing failures, or building robust APIs.

Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.

Python project organization, module architecture, and public API design. Use when setting up new projects, organizing modules, defining public interfaces with __all__, or planning directory layouts.

Python resilience patterns including automatic retries, exponential backoff, timeouts, and fault-tolerant decorators. Use when adding retry logic, implementing timeouts, building fault-tolerant services, or handling transient failures.

Python resource management with context managers, cleanup patterns, and streaming. Use when managing connections, file handles, implementing cleanup logic, or building streaming responses with accumulated state.

Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.

Python type safety with type hints, generics, protocols, and strict type checking. Use when adding type annotations, implementing generic classes, defining structural interfaces, or configuring mypy/pyright.