Loading...
Loading...
Found 1,191 Skills
This skill should be used when the user asks to "set up ESLint", "configure ESLint rules", "fix ESLint errors", "migrate to flat config", or needs guidance on JavaScript/TypeScript linting best practices.
Detect and remediate Go anti-patterns: premature interface abstraction, goroutine overkill, context soup, error wrapping mistakes, generic abuse, channel misuse, unnecessary function extraction, and interface pollution. Use when reviewing Go code for quality, detecting over-engineering, or when user mentions "anti-pattern", "code smell", "Go mistake", or "bad Go". Do NOT use for feature implementation, performance optimization without a code smell, or non-Go languages.
Execute a micro-level NestJS code quality audit. Validates code against live GitHub standards for testing, architecture, DTO validation, error handling, and code implementation. Produces a detailed violations report with prioritized action plan. Use when the user asks to check NestJS code quality, validate best practices, or review backend code standards. Triggers on: 'nestjs best practices', 'backend code quality', 'code review', 'nestjs standards', 'dto validation', 'error handling review'.
Run Gemini CLI review against the current branch and report only the review comments that are still valid for the current codebase, without applying fixes.
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".
Phase 2 of the feature workflow — Write code according to the implementation sequence in {slug}-design.md, and submit a completion report in a unified format for user review after finishing. Prerequisites: {slug}-design.md has been approved (standard design includes test design, or fastforward design includes acceptance criteria), and {slug}-checklist.yaml exists in the same directory. Trigger scenarios: User says "The plan is confirmed, start implementation", "Write code according to the plan", "Start working". If you encounter situations not covered by the plan during implementation (new concepts, out-of-scope files, need for patch branches), proactively stop and discuss with the user based on the plan, do not proceed forcefully.
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.
Local Code Review - analyzes code changes and provides structured feedback before commit
Auto-selects best Kaizen method (Gemba Walk, Value Stream, or Muda) for target
Reflect on previus response and output, based on Self-refinement framework for iterative improvement with complexity triage and verification