1천건 · 관련도순
Systematic methodology for eliciting, representing, and operationalizing expert cognitive skills—pattern recognition, situation assessment, mental simulation—from practitioners without requiring research training. Bridges academic rigor and practical usability.
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When the user requests information about academic benchmarks, datasets, or research papers, particularly in machine learning, deep learning, or logical reasoning domains. This skill enables systematic research of academic benchmarks by searching web sources, downloading and analyzing arXiv papers, extracting key metadata (number of tasks, training availability, difficulty levels), and compiling comparative summaries. It triggers on requests involving dataset comparisons, benchmark analysis, or academic paper research for table creation.
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Interactive training for the GitHub Copilot CLI. Guided lessons, quizzes, scenario challenges, and a full reference covering slash commands, shortcuts, modes, agents, skills, MCP, and configuration. Say "cliexpert" to start.
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专为追求倍速增长的初建学校设计的教师选、育、用、留四环节体系。基于科德高中融合部真实管理经验，结合十部AI Agent架构，将人才管理从校长个人经验升级为可继承、可协同、可沉淀的组织能力。
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你的班主任成长教练。2026年3月4日到5月22日，Estelle 用66天完成了从心虚到被学生依赖的全过程。现在她把方法、心法、拆过的墙、哭过的夜、踩过的坑，全部放进这个技能里。 - school-management - teacher-tools - international-school - education-technology
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Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>512 tokens), encountering GPU memory issues with attention, or need faster inference. Supports PyTorch native SDPA, flash-attn library, H100 FP8, and sliding window attention.
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Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
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Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.
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Design and execute a social engineering penetration test including phishing, vishing, smishing, and physical pretexting campaigns to measure human security resilience and identify training gaps.
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Generate comprehensive documentation for content authors taking over an AEM Edge Delivery Services project. Use when onboarding content authors, training content managers, or creating author-focused handover documentation — analyzes project structure and produces a complete authoring guide with blocks, templates, configurations, and publishing workflows.
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Fetch up-to-date, version-specific documentation for any major library (Next.js 16, Sanity, LangGraph, React, Tailwind CSS, Vitest, Framer Motion) via the Context7 MCP server. Use when writing code against unfamiliar or recently-changed framework APIs, when training data may be stale, when resolving deprecations or version-specific syntax, or when the repo warns about framework drift (e.g. the nextjs-agent-rules block in portfolio-v1/AGENTS.md). Invokes the resolve-library-id and get-library-docs MCP tools.
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Creates comprehensive developer onboarding documentation and materials including step-by-step setup guides, first-task assignments, expected time per step, common troubleshooting, team introductions, and code walkthrough tours. Use when preparing "new developer onboarding", "first day setup", "junior dev training", or "team member onboarding".
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Converts documents into clean, chunked datasets suitable for embeddings and vector search. Produces chunked JSONL files with metadata, deduplication logic, and quality checks. Use when preparing "training data", "vector datasets", "document processing", or "embedding data".
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Full exploit development course roadmap and syllabus: weekly topics, recommended reading, lab setup, and learning path from vulnerability classes through advanced exploitation. Use to structure exploit dev training or onboard new researchers.
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AI/LLM security offensive checklist: prompt injection, jailbreaking, model extraction, training data poisoning, adversarial inputs, LLM-assisted attack automation, and AI system reconnaissance. Use when assessing AI/ML systems, red-teaming LLMs, or researching AI attack vectors.
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Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.
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