All authors
Claude Skills by luokai0
github.com/luokai05,222 skills168 installs10,845 views
- Cypress ExpertExpert-level Cypress. Use when writing Cypress tests, custom commands, intercepts, component testing, or CI/CD integration.Votes: 0GitHub stars: 11
- Openai Playwright InteractivePersistent browser and Electron interaction through `js_repl` for fast iterative UI debugging.Votes: 0GitHub stars: 11
- Openai PlaywrightUse when the task requires automating a real browser from the terminal (navigation, form filling, snapshots, screenshots, data extraction, UI-flow debugging) via `playwright-cli` or the bundled wrapper script.Votes: 0GitHub stars: 11
- Playwright ExpertExpert-level Playwright. Use when writing e2e tests, page object model, fixtures, visual regression, API mocking, or CI integration.Votes: 0GitHub stars: 11
- Playwright SkillComplete browser automation with Playwright. Auto-detects dev servers, writes clean test scripts to /tmp. Test pages, fill forms, take screenshots, check responsive design, validate UX, test login flows, check links, automate any browser task. Use when user wants to test websites, automate browser interactions, validate web functionality, or perform any browser-based testing.Votes: 0GitHub stars: 11
- Playwright TestingE2E testing with Playwright - Page Objects, cross-browser, CI/CDVotes: 0GitHub stars: 11
- Code Review ExpertExpert-level code review. Use when reviewing code quality, identifying code smells, refactoring, DRY/YAGNI/KISS principles, or writing review feedback.Votes: 0GitHub stars: 11
- Code SimplifierSimplifies and refines code for clarity, consistency, and maintainability while preserving all functionality. Use when asked to "simplify code", "clean up code", "refactor for clarity", "improve readability", or review recently modified code for elegance. Focuses on project-specific best practices.Votes: 0GitHub stars: 11
- Debugging ExpertExpert-level debugging. Use when debugging complex issues, GDB/lldb/DevTools, stack traces, heap dumps, performance profiles, or production incidents.Votes: 0GitHub stars: 11
- Fix TestsSystematically fix all failing tests after business logic changes or refactoringVotes: 0GitHub stars: 11
- Neolab Review Local ChangesComprehensive review of local uncommitted changes using specialized agents with code improvement suggestionsVotes: 0GitHub stars: 11
- Neolab Review PrComprehensive pull request review using specialized agentsVotes: 0GitHub stars: 11
- Neolab TddUse when implementing any feature or bugfix, before writing implementation code - write the test first, watch it fail, write minimal code to pass; ensures tests actually verify behavior by requiring failure firstVotes: 0GitHub stars: 11
- Neolab Write TestsSystematically add test coverage for all local code changes using specialized review and development agents. Add tests for uncommitted changes (including untracked files), or if everything is commited, then will cover latest commit.Votes: 0GitHub stars: 11
- Obra TddUse when implementing any feature or bugfix, before writing implementation codeVotes: 0GitHub stars: 11
- Receiving Code ReviewUse when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementationVotes: 0GitHub stars: 11
- Requesting Code ReviewUse when completing tasks, implementing major features, or before merging to verify work meets requirementsVotes: 0GitHub stars: 11
- Subagent Driven DevelopmentUse when executing implementation plans with independent tasks in the current sessionVotes: 0GitHub stars: 11
- Systematic DebuggingUse when encountering any bug, test failure, or unexpected behavior, before proposing fixesVotes: 0GitHub stars: 11
- Testing ExpertExpert-level software testing strategy. Use when designing test suites, working with unit/integration/e2e testing, TDD, BDD, test coverage, mocking, test doubles, property-based testing, or defining testing best practices. Also use when the user mentions 'test strategy', 'TDD', 'BDD', 'test coverage', 'mock', 'unit test vs integration test', 'flaky test', 'test pyramid', or 'how do I test this'.Votes: 0GitHub stars: 11
- Wp PhpstanUse when configuring, running, or fixing PHPStan static analysis in WordPress projects (plugins/themes/sites): phpstan.neon setup, baselines, WordPress-specific typing, and handling third-party plugin classes.Votes: 0GitHub stars: 11
- Wp Project TriageUse when you need a deterministic inspection of a WordPress repository (plugin/theme/block theme/WP core/Gutenberg/full site) including tooling/tests/version hints, and a structured JSON report to guide workflows and guardrails.Votes: 0GitHub stars: 11
- Domain Driven Design ExpertExpert-level Domain-Driven Design (DDD). Use when applying DDD principles, identifying bounded contexts, aggregates, entities, value objects, domain events, repositories, application services, or ubiquitous language. Also use when the user mentions 'bounded context', 'aggregate', 'value object', 'domain event', 'ubiquitous language', 'context map', 'DDD', 'domain model', or 'aggregate root'.Votes: 0GitHub stars: 11
- Api Design ExpertExpert-level API design and developer experience. Use when designing REST APIs, versioning, backward compatibility, SDK design, error handling conventions, API documentation, or API governance. Also use when the user mentions 'API design', 'versioning', 'backward compatible', 'OpenAPI', 'SDK design', 'error format', 'pagination', 'rate limiting', 'API governance', or 'REST conventions'.Votes: 0GitHub stars: 11
- System Design ExpertExpert-level system design and software architecture. Use when designing large-scale systems, discussing scalability, availability, consistency, load balancing, caching, message queues, databases, or drawing architecture diagrams. Also use when the user mentions 'design a system', 'how would you scale', 'architecture review', 'trade-offs', 'CAP theorem', 'bottleneck', 'high availability', or 'distributed system'.Votes: 0GitHub stars: 11
- Devex ExpertExpert-level developer experience. Use when improving workflows, ESLint, Prettier, pre-commit hooks, monorepo tooling, Turborepo, Nx, or developer docs.Votes: 0GitHub stars: 11
- Clean Architecture ExpertExpert-level Clean Architecture and SOLID principles. Use when structuring codebases with clean architecture, applying SOLID principles, dependency inversion, use cases, ports and adapters, hexagonal architecture, or onion architecture. Also use when the user mentions 'Clean Architecture', 'SOLID', 'dependency inversion', 'hexagonal', 'use case', 'ports and adapters', 'domain layer', 'application layer', or 'Uncle Bob'.Votes: 0GitHub stars: 11
- Design Patterns ExpertExpert-level software design patterns. Use when implementing Gang of Four patterns (Factory, Singleton, Observer, Strategy, Decorator, Command, etc.), architectural patterns, or recognizing when and how patterns apply. Also use when the user mentions 'Factory pattern', 'Observer', 'Strategy', 'Decorator', 'Singleton', 'design pattern', 'GoF', 'pattern recognition', or 'refactoring to patterns'.Votes: 0GitHub stars: 11
- Microservices ExpertExpert-level microservices architecture. Use when designing microservices, service boundaries, inter-service communication, saga pattern, CQRS, event sourcing, service mesh, API gateways, or managing distributed systems complexity. Also use when the user mentions 'microservices', 'service boundary', 'saga pattern', 'service mesh', 'CQRS', 'event sourcing', 'API gateway', 'distributed transaction', or 'service decomposition'.Votes: 0GitHub stars: 11
- Open Source ExpertExpert-level open source. Use when setting up OSS projects, README, CONTRIBUTING, changelogs, semantic versioning, GitHub Actions releases, or community building.Votes: 0GitHub stars: 11
- Machine Learning EngineerUse this agent when you need to deploy, optimize, or serve machine learning models at scale in production environments.Votes: 0GitHub stars: 11
- Machine Learning ExpertExpert-level machine learning engineering. Use when building ML models, feature engineering, model training/evaluation, scikit-learn, XGBoost, hyperparameter tuning, cross-validation, or deploying ML models to production. Also use when the user mentions 'ML model', 'feature engineering', 'scikit-learn', 'overfitting', 'cross-validation', 'classification', 'regression', 'clustering', 'training data', or 'model accuracy'.Votes: 0GitHub stars: 11
- Ml EngineerUse this agent when building production ML systems requiring model training pipelines, model serving infrastructure, performance optimization, and automated retraining.Votes: 0GitHub stars: 11
- Deep Learning ExpertExpert-level deep learning with PyTorch and TensorFlow. Use when building neural networks, CNNs, RNNs, transformers, training loops, loss functions, backpropagation, GPU optimization, or fine-tuning pretrained models. Also use when the user mentions 'neural network', 'PyTorch', 'transformer', 'fine-tuning', 'backpropagation', 'GPU training', 'loss function', 'overfitting', 'batch normalization', or 'transfer learning'.Votes: 0GitHub stars: 11
- Llm ArchitectUse when designing LLM systems for production, implementing fine-tuning or RAG architectures, optimizing inference serving infrastructure, or managing multi-model deployments.Votes: 0GitHub stars: 11
- Llm Engineering ExpertExpert-level LLM application engineering. Use when building LLM-powered apps, prompt engineering, RAG systems, embeddings, vector databases, function calling, agents, or evaluating LLM outputs. Also use when the user mentions 'RAG', 'LangChain', 'embedding', 'vector store', 'prompt engineering', 'AI agent', 'function calling', 'hallucination', or 'context window'.Votes: 0GitHub stars: 11
- Llm PatternsAI-first application patterns, LLM testing, prompt managementVotes: 0GitHub stars: 11
- Tensorrt LlmOptimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.Votes: 0GitHub stars: 11
- VllmServes LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.Votes: 0GitHub stars: 11
- Rag Systems ExpertBuild production-grade Retrieval Augmented Generation systems. Covers chunking, embedding, vector search, reranking, evaluation, and RAG pipelines.Votes: 0GitHub stars: 11
- Mlops EngineerUse this agent when you need to design and implement ML infrastructure, set up CI/CD for machine learning models, establish model versioning systems, or optimize ML platforms for reliability and au...Votes: 0GitHub stars: 11
- Mlops ExpertExpert-level MLOps and ML infrastructure. Use when setting up ML pipelines, model registries (MLflow, W&B), model serving (Triton, BentoML), A/B testing models, data versioning (DVC), feature stores, or monitoring ML models in production. Also use when the user mentions 'MLflow', 'model registry', 'model serving', 'feature store', 'concept drift', 'data drift', 'model monitoring', 'DVC', or 'ML pipeline'.Votes: 0GitHub stars: 11
- Data Engineering ExpertExpert-level data engineering. Use when building data pipelines, ETL/ELT processes, working with Apache Spark, Airflow, dbt, data warehouses (Snowflake, BigQuery, Redshift), data lakes, or streaming pipelines. Also use when the user mentions 'data pipeline', 'ETL', 'ELT', 'dbt', 'Airflow', 'Spark', 'data warehouse', 'data lake', 'streaming', 'partitioning', or 'data quality'.Votes: 0GitHub stars: 11
- Hf Hf CliHugging Face Hub CLI (`hf`) for downloading, uploading, and managing repositories, models, datasets, and Spaces on the Hugging Face Hub. Replaces now deprecated `huggingface-cli` command.Votes: 0GitHub stars: 11
- Hf Hf McpUse Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.Votes: 0GitHub stars: 11
- Hf Hugging Face Dataset ViewerUse this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.Votes: 0GitHub stars: 11
- Hf Hugging Face DatasetsCreate and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work alongside HF MCP server for comprehensive dataset workflows.Votes: 0GitHub stars: 11
- Hf Hugging Face EvaluationAdd and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations with vLLM/lighteval. Works with the model-index metadata format.Votes: 0GitHub stars: 11
- Hf Hugging Face JobsThis skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence. Designed for general-purpose compute workloads including data processing, inference, experiments, batch jobs, and any Python-based tasks. Should be invoked for tasks involving cloud compute, GPU workloads, or when users mention ...Votes: 0GitHub stars: 11
- Hf Hugging Face Model TrainerThis skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked f...Votes: 0GitHub stars: 11