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Found 372 Skills
Use when the user is doing AI/ML work in a scientific domain — biology, chemistry, physics, astronomy, climate, genomics, materials science, medicine, ecology, energy, conservation, engineering, mathematics, scientific reasoning, drug discovery, protein design, weather modeling, theorem proving, single-cell, PDE solving, or anything similar. Hugging Science (huggingscience.co) is a curated catalog of scientific datasets, models, blog posts, and interactive Spaces; the `hugging-science` org on Hugging Face hosts community datasets, models, and demo Spaces. This skill helps you discover the right resource AND actually use it — loading datasets via `datasets`, running models via `transformers` or the HF Inference API, calling Spaces like BoltzGen via `gradio_client`, and citing blog posts for methodology. Trigger this skill whenever a user mentions a scientific ML task, asks for "a dataset/model for X" where X is a scientific topic, wants to fine-tune on scientific data, asks about protein / molecule / genome / climate / materials / astronomy / pathology / weather ML, or needs AI tools for research — even if they never say "Hugging Science" explicitly. The catalog is purpose-built for LLM agents (it ships an `llms-full.txt`); prefer it over generic web search for these tasks.
Transform vague prompts into precise, well-structured specifications using EARS (Easy Approach to Requirements Syntax) methodology. This skill should be used when users provide loose requirements, ambiguous feature descriptions, or need to enhance prompts for AI-generated code, products, or documents. Triggers include requests to "optimize my prompt", "improve this requirement", "make this more specific", or when raw requirements lack detail and structure.
Jeffrey Emanuel's comprehensive markdown planning methodology for software projects. The 85%+ time-on-planning approach that makes agentic coding work at scale. Includes exact prompts used.
McKinsey Consultant-style Problem Solving System. Starting from business problems, it generates McKinsey-style research reports and PPTs through hypothesis-driven structured analysis methods. It integrates Problem Solving methodology, MECE principles, Issue Tree decomposition, Hypotheses formulation, Dummy Page design, intelligent data collection, and professional PPT generation capabilities.
Systematic methodology for debugging bugs, test failures, and unexpected behavior. Use when encountering any technical issue before proposing fixes. Covers root cause investigation, pattern analysis, hypothesis testing, and fix implementation. Use ESPECIALLY when under time pressure, "just one quick fix" seems obvious, or you've already tried multiple fixes. NOT for exploratory code reading.
Convert text with private context or internal dependencies into generic, unbiased expressions. Use for project decontextualization (handoff, open-source prep), methodology abstraction, cross-team sharing, anonymization. Includes path strings and file/folder names as they appear in text.
Required methodology for planning, ideating, and delivering features or tasks. Use when user asks to "plan", "break down", "continue", or "figure out steps".
TDD-style testing methodology for skills using fresh subagent instances to prevent priming bias and validate skill effectiveness. Use when validating skill improvements, testing skill effectiveness, preventing priming bias, measuring skill impact on behavior. Do not use when implementing skills (use skill-authoring instead), creating hooks (use hook-authoring instead).
Customer-obsessed design methodology. Use when designing features, validating problems, choosing research methods, or measuring design success.
Universal web design implementation methodology — BEM, responsive, accessibility, CSS architecture, spacing systems, dark mode. The HOW of building production-grade HTML/CSS.
Four-phase debugging methodology with root cause analysis. Use when investigating bugs, fixing test failures, or troubleshooting unexpected behavior. Emphasizes NO FIXES WITHOUT ROOT CAUSE FIRST.
Partition-first log analysis methodology. Use for log searches, error analysis, pattern finding across Datadog, CloudWatch, or Kubernetes logs.