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Found 1,747 Skills
Generates performance-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Performance Optimization pillar of the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify performance requirements, and provide actionable recommendations for resource allocation, modular design, and elasticity.
Generates sustainability-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify environmental impact requirements, and provide actionable recommendations to build, deploy, and manage the workload sustainably in Google Cloud.
Serve a quantized or unquantized LLM checkpoint as an OpenAI-compatible API endpoint using vLLM, SGLang, or TRT-LLM. Use when user says "deploy model", "serve model", "start vLLM server", "launch SGLang", "TRT-LLM deploy", "AutoDeploy", "benchmark throughput", "serve checkpoint", or needs an inference endpoint from a HuggingFace or ModelOpt-quantized checkpoint. Do NOT use for quantizing models (use ptq) or evaluating accuracy (use evaluation).
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.
Refactor Scikit-learn and machine learning code to improve maintainability, reproducibility, and adherence to best practices. This skill transforms working ML code into production-ready pipelines that prevent data leakage and ensure reproducible results. It addresses preprocessing outside pipelines, missing random_state parameters, improper cross-validation, and custom transformers not following sklearn API conventions. Implements proper Pipeline and ColumnTransformer patterns, systematic hyperparameter tuning, and appropriate evaluation metrics.
Apply startup execution wisdom to product, strategy, and business decisions. Use for feature prioritization, build-vs-buy decisions, go-to-market planning, pricing, hiring, scope/timeline reality checks, or when evaluating whether an idea has product-market fit potential.
Senior/Lead Developer Bun.js + Docker dengan pengalaman 20 tahun. Skill ini digunakan ketika: (1) Membuat project Bun.js baru dengan Docker, (2) Code review dan refactoring untuk clean code, (3) Debugging complex issues, (4) Optimisasi performa dan scalability, (5) Arsitektur aplikasi production-ready, (6) Memilih library yang tepat dan terpercaya, (7) Setup CI/CD dan deployment. Trigger keywords: "bun", "bunjs", "bun.js", "docker", "typescript backend", "clean code", "scalable", "maintainable", "debugging", "performance".
LLM-based deep iterative search and reasoning service. Specializes in handling complex problems, automatically decomposing queries, conducting multi-round iterative retrieval, evaluating and verifying information, and finally generating comprehensive and structured deep analysis reports.
Use when you need to generate many creative options before systematically narrowing to the best choices. Invoke when exploring product ideas, solving open-ended problems, generating strategic alternatives, developing research questions, designing experiments, or when you need both breadth (many ideas) and rigor (principled selection). Use when user mentions brainstorming, ideation, divergent thinking, generating options, or evaluating alternatives.
Calculate, understand, and improve the unit economics of a solopreneur business. Use when figuring out if the business is actually profitable per customer, when CAC or LTV numbers are needed, when evaluating whether a pricing or acquisition strategy is sustainable, or when making data-driven decisions about marketing spend and pricing. Covers CAC, LTV, payback period, contribution margin, and the feedback loops between them. Trigger on "unit economics", "CAC", "customer acquisition cost", "LTV", "lifetime value", "payback period", "is my business profitable", "contribution margin", "am I making money per customer", "should I spend more on marketing".
Facilitates solution ideation with clear trade-offs and a final recommendation. Use when exploring architectural decisions, evaluating technology choices, or comparing implementation approaches before writing code.
Use when evaluating ICP fit, buying intent, and routing priority for new leads.