Total 50,370 skills, Code Quality has 2287 skills
Showing 12 of 2287 skills
Use when code has been written and needs validation before committing, or when the user asks for a code review or security check.
Safely track pull request feedback, resolve review comments or merge conflicts, validate fixes, and use a read-only cross-review before committing or pushing follow-up changes.
Findings-first review discipline for code, diffs, task plans, live workflow changes, and implementation evidence.
Codacy integration. Manage Repositories, Organizations. Use when the user wants to interact with Codacy data.
Use when implementing any code in recursive-mode Phase 3. Enforces strict RED-GREEN-REFACTOR discipline with The Iron Law - no production code without a failing test first. Trigger phrases: "implement this", "add feature", "fix bug", "write a failing test", "TDD".
Step-by-step process for adopting Cavekit on an existing codebase. Covers the 6-step brownfield process, bootstrap prompt design, spec validation against existing behavior, and the decision between brownfield adoption vs deliberate rewrite. Trigger phrases: "brownfield", "existing codebase", "add Cavekit to existing project", "adopt Cavekit", "layer kits on code", "retrofit kits"
Apply a simple code transform via agent-booster's WASM engine — sub-millisecond, deterministic, $0 (no LLM call). Companion to cost-booster-route.
Runs the full validator workflow after coding tasks for requests such as "run the validator", "run final verification", "validate before commit", or "run validation". Executes checks and reviews before commit, push, or PR creation.
Write, fix, and standardize Python docstrings in Google style. Use whenever the user asks to add or improve docstrings, convert mixed docstrings to Google format, add missing Args/Returns/Raises/Attributes/Example sections, or make docstrings concise and API-focused.
Change size guidance (800 lines)
Review, design, and refactor TensorRT-LLM PyTorch MoE code for architecture fit, clean code, maintainability, and testability. Always use for any modification, review, refactor, or design planning that touches MoE modules, including tensorrt_llm/_torch/modules/fused_moe, ConfigurableMoE, MoE backends, MoEScheduler/moe_scheduler.py, forward execution/chunking, communication strategies, EPLB, quantization/weight handling, routing, factories, MoE docs, or MoE tests. Also use when the user asks whether a MoE design follows the current architecture or whether a MoE refactor is reasonable.
Identifies code smells and provides step-by-step refactoring recipes. Use when improving legacy code maintainability or teaching students how to apply Clean Code and SOLID principles.