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このリポジトリの skills
SkillsMP は FerroxLabs/wayland から 2,295 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
FerroxLabs/wayland収集済み skill 2,295 件中 40 件を表示しています。
Becomes a senior code reviewer who evaluates pull requests and code changes for correctness, security, performance, and maintainability. Use when the user asks for code review, PR feedback, code quality assessment, or merge readiness evaluation. Do NOT use…
原文の言語: 英語
Becomes a senior DevOps engineer who designs and implements CI/CD pipelines, infrastructure as code, monitoring systems, and deployment strategies. Use when the user needs build pipelines, container orchestration, cloud infrastructure, deployment automation,…
原文の言語: 英語
Becomes a senior frontend developer who builds accessible, performant user interfaces with modern web technologies. Use when the user needs UI components, responsive layouts, accessibility improvements, or frontend performance optimization. Do NOT use when…
原文の言語: 英語
Becomes a senior performance engineer who identifies bottlenecks, designs optimization strategies, and conducts load testing using systematic profiling and benchmarking methodology. Use when the user needs performance analysis, load testing, bottleneck…
原文の言語: 英語
Becomes a senior QA engineer who designs comprehensive test strategies, writes automated tests, and builds quality assurance processes for software projects. Use when the user needs test plans, test case design, automated test suites, coverage analysis, or…
原文の言語: 英語
Becomes a principal security engineer who conducts comprehensive security audits of applications, APIs, and infrastructure using threat modeling and vulnerability analysis methodologies. Use when the user needs a security review, threat model, vulnerability…
原文の言語: 英語
Becomes a senior technical writer who produces clear, accurate, and audience-appropriate documentation including API references, developer guides, changelogs, and onboarding materials. Use when the user needs documentation written, restructured, or improved…
原文の言語: 英語
Becomes a seasoned customer support specialist who triages issues, crafts empathetic responses, manages escalation workflows, and builds knowledge base articles for recurring problems. Use when the user needs help writing customer responses, classifying…
原文の言語: 英語
Becomes a senior data analyst who transforms raw data into actionable insights through rigorous methodology, clear visualization recommendations, and compelling data narratives. Use when the user needs data exploration, cohort analysis, trend identification,…
原文の言語: 英語
Becomes a senior HR specialist who designs interview frameworks, facilitates performance review cycles, builds compensation analysis models, and creates onboarding programs grounded in best practices. Use when the user needs interview guides, evaluation…
原文の言語: 英語
Becomes a senior operations manager who maps existing processes, identifies bottlenecks, designs improved workflows, creates standard operating procedures, and defines efficiency metrics. Use when the user needs SOP creation, workflow optimization, process…
原文の言語: 英語
Becomes a senior site reliability engineering lead who commands incident response by coordinating investigation, communication, and remediation across specialist agents. Use when the user faces a production outage, service degradation, security breach, or any…
原文の言語: 英語
Becomes a principal research scientist who orchestrates deep, multi-source research by coordinating evidence gathering, grading source quality, and synthesizing findings with explicit confidence levels. Use when the user needs a comprehensive research report…
原文の言語: 英語
Becomes a senior agile coach who facilitates sprint ceremonies, tracks velocity, and drives impediment resolution for software development teams. Use when the user needs sprint planning, daily standup facilitation, retrospective guidance, velocity analysis,…
原文の言語: 英語
Becomes a principal engineering manager who orchestrates multi-agent pipelines for complex, cross-functional deliverables. Use when the user needs to decompose a large request into subtasks for multiple specialist agents, coordinate parallel workstreams, or…
原文の言語: 英語
AI agent architecture covering agent patterns (ReAct, Plan-and-Execute, LATS), tool design, memory systems (short-term, long-term, episodic), multi-agent coordination, guardrails, LangChain and LangGraph patterns, and error recovery. Use when the user asks…
原文の言語: 英語
Guides expert-level ai agent patterns implementation: ai-ml and architecture decision frameworks, production-ready patterns, and concrete templates for ai agent patterns workflows. Use when the user asks about ai agent patterns, ai agent patterns…
原文の言語: 英語
Comprehensive guide to building AI-powered automation workflows using n8n, Zapier, Make, and custom agent frameworks covering integration patterns, cost optimization, error handling, and production-ready AI workflow design. Use when the user asks about ai…
原文の言語: 英語
Master guide to maximizing productivity with AI coding assistants including GitHub Copilot, Cursor, Claude Code, and similar tools covering effective prompting, context management, workflow integration, and advanced techniques for 10x development velocity.…
原文の言語: 英語
Guides expert-level ai deployment patterns implementation: ai-ml and devops decision frameworks, production-ready patterns, and concrete templates for ai deployment patterns workflows. Use when the user asks about ai deployment patterns, ai deployment…
原文の言語: 英語
Guides expert-level ai evaluation patterns implementation: ai-ml and testing decision frameworks, production-ready patterns, and concrete templates for ai evaluation patterns workflows. Use when the user asks about ai evaluation patterns, ai evaluation…
原文の言語: 英語
Expert-level guide to AI image generation across Midjourney, DALL-E, and Stable Diffusion covering prompt engineering for visual output, style control, composition techniques, upscaling workflows, and production pipelines for consistent, high-quality results.…
原文の言語: 英語
Advanced prompting techniques for any AI model covering system prompts, chain-of-thought reasoning, few-shot examples, structured output formats, context management, multi-turn conversation strategies, and a reusable prompt template library. Use when the user…
原文の言語: 英語
Organizational AI readiness assessment evaluating data quality, team capabilities, infrastructure, governance, and use case prioritization to produce an actionable readiness scorecard. Use when the user asks about ai readiness evaluation, related techniques,…
原文の言語: 英語
AI safety and alignment engineering covering guardrail implementation, red teaming methodologies, content filtering pipelines, output validation, prompt injection defense, toxicity detection, bias mitigation, and responsible deployment practices for…
原文の言語: 英語
Workflow automation design using Zapier, Make, and n8n covering trigger-action architecture, multi-step automation workflows, error handling strategies, personal versus business automation patterns, ROI calculation, and a library of common automation recipes.…
原文の言語: 英語
Strategic calendar management covering time blocking, meeting reduction strategies, energy-based scheduling, buffer time design, focus blocks, calendar audits, tool integration, and time tracking for maximum productivity and work-life balance. Use when the…
原文の言語: 英語
Hands-on computer vision development covering image classification with transfer learning, object detection with YOLO and Faster R-CNN, semantic and instance segmentation, OpenCV image processing, data augmentation strategies, model optimization for edge…
原文の言語: 英語
Computer vision implementation covering image classification, object detection (YOLO), image segmentation, OCR (Tesseract), face detection, image preprocessing, data augmentation, transfer learning, model deployment, and edge inference. Use when the user asks…
原文の言語: 英語
Email management mastery covering Inbox Zero methodology, filter and label systems, template libraries, batch processing workflows, unsubscribe strategies, email scheduling, follow-up tracking, and tool recommendations for reclaiming time from email. Use when…
原文の言語: 英語
Vector embedding specialist covering embedding model comparison, dimensionality reduction, similarity metrics (cosine, euclidean, dot product), embedding fine-tuning, multi-modal embeddings, embedding visualization, and index optimization. Use when the user…
原文の言語: 英語
Feature engineering covering numerical features (scaling, binning, log transforms), categorical encoding (one-hot, target, ordinal), text features (TF-IDF, embeddings), temporal features, feature selection, feature stores, and automated feature engineering.…
原文の言語: 英語
Model fine-tuning covering dataset preparation, LoRA and QLoRA, instruction tuning, RLHF and DPO, benchmarking, overfitting prevention, compute requirements, Hugging Face Trainer, and the fine-tuning vs prompt engineering decision. Use when the user asks…
原文の言語: 英語
Guides expert-level fine-tuning strategy implementation: ai-ml and optimization decision frameworks, production-ready patterns, and concrete templates for fine tuning strategy workflows. Use when the user asks about fine-tuning strategy, fine tuning strategy…
原文の言語: 英語
Knowledge graph engineering covering ontology design, graph database selection (Neo4j, Amazon Neptune, ArangoDB), entity and relation extraction from text, graph-based RAG (GraphRAG), knowledge graph embeddings, graph query optimization, and integration…
原文の言語: 英語
Large language model fine-tuning expertise covering LoRA and QLoRA parameter-efficient methods, full fine-tuning strategies, dataset preparation and curation, instruction tuning, DPO alignment, evaluation frameworks, Hugging Face TRL and PEFT libraries,…
原文の言語: 英語
Guides expert-level llm integration patterns implementation: ai-ml and backend decision frameworks, production-ready patterns, and concrete templates for llm integration patterns workflows. Use when the user asks about llm integration patterns, llm…
原文の言語: 英語
LLM API integration covering OpenAI, Anthropic, and Google API patterns, streaming responses, function calling and tool use, token counting, cost optimization, fallback strategies, rate limit handling, response caching, and multi-model routing. Use when the…
原文の言語: 英語
Machine learning operations covering MLflow experiment tracking and model registry, model versioning and reproducibility, model serving with TorchServe and Triton, A/B testing models in production, data and concept drift detection, feature stores, CI/CD for…
原文の言語: 英語
ML pipeline design covering feature engineering, model training workflows, hyperparameter tuning, cross-validation, experiment tracking (MLflow, W&B), model versioning, data versioning (DVC), reproducibility, and pipeline orchestration. Use when the user asks…
原文の言語: 英語