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Assess a website’s readiness for AI search alongside traditional SEO. Review content citation readiness, crawler access, brand mentions, technical foundations, and structured data, then generate scores, prioritized recommendations, and client reports.
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Build or update a Figma design system from existing code. Organize shared colors, spacing, and typography, create reusable components with states and theme bindings, document their use, and check differences between the implementation and Figma.
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Build or update a Figma design system to match a codebase. Organize reusable design values, light and dark themes, component variants, and documentation, then check component structure, appearance, and bindings to those values.
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Search academic sources and encyclopedia entries, read full papers or selected sections, and follow references and citing papers. Use these connections to find related research, identify foundational studies, and trace later developments beyond keyword search.
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Find recent arXiv papers on EEG, or brainwave, decoding and brain-computer interfaces. Evaluate candidates using five quality criteria, select the top three, and save structured Chinese and English reviews with paper metadata as Markdown files.
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Master discrete mathematics, logic, formal proofs, and computational thinking. Build the mathematical foundation for all computer science.
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苹果FoundationModels框架用于设备上的LLM——文本生成、使用@Generable进行引导生成、工具调用，以及在iOS 26+中的快照流。
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Apple FoundationModels 框架，用于设备端大语言模型（LLM）—— iOS 26+ 中的文本生成、配合 @Generable 的引导式生成、工具调用以及快照流式传输。
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Scaffold a research grant proposal (NSF, NIH, ERC, or foundation) by composing existing primitives — pulls identification strategy from an `/interview-me` spec, delegates the data-management plan to `/data-management-plan` and the facilities statement to `/capture-environment`, and emits a funder-requirements checklist. Use when user says "draft a grant", "write a proposal", "NSF proposal", "NIH aims", "ERC application", "foundation grant", "specific aims", or "scaffold a grant proposal". NOT a submission tool — produces a draft the user uploads to the sponsor's portal themselves.
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Design Thinking coaching foundation knowledge: coach identity and philosophy, quality and fidelity constraints, method sequencing, coaching state schema, and the canonical deck workflow
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Foundational Python best practices, idioms, and code quality fundamentals
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Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.
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Use when the user mentions Foundation Models review, on-device AI audit, LanguageModelSession issues, or @Generable/guardrail problems — including missing availability checks, manual JSON parsing, and guardrail handling.
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AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns.
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Foundation framework skills — AttributedString patterns for rich text formatting, alignment, selection, and SwiftUI integration. Use when working with styled text or AttributedString APIs.
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On-device LLM integration using Apple's Foundation Models framework. Use when implementing AI text generation, structured output, or tool calling.
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Foundational platform engineering knowledge from key references -- Continuous Delivery, SRE, Accelerate, Team Topologies, Chaos Engineering, and Secure Delivery. Load when contextual grounding in platform engineering theory is needed.
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Maintain or assess Workflow Foundation-based solutions on .NET Framework, especially where long-lived process logic or legacy designer artifacts still matter. USE FOR: WF activities and workflows; designer-backed process logic; long-lived workflow state and persistence; keep, isolate, or replace decisions. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.
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Project memory workflow for init/upgrade, profile-aware setup, end session, normal/full close, file organization, document archiving, language switching, versioned update logs, rule review, status, wiki sync, and context recovery. Use for /project-butler, setup/初始化, foundation setup, profile setup, end session/收工, normal close, full close, foundation repair, organize files/整理文件, change language/切换语言, continue/接着上次, continue full context/全面回顾, review claude, sync wiki, status. Maintains project memory files. Runs version freshness check before trigger routing.
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Design architecture foundations before implementation. Use when asked to design or refactor architecture, choose Rust/Go crate, package, module, runtime, workflow, or service boundaries, compare mature project architecture, prevent stacked one-off PRs, audit migration debt in existing repos, or produce a spec before coding.
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options foundations, Greeks analysis, volatility surface, put call parity, options pricing, delta gamma theta vega rho, Black Scholes, options theory, derivatives basics, options mechanics
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Structure prediction using Chai-1, a foundation model for molecular structure. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For ESM-based analysis, use esm.
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Applies the Neoplatonic and mathematical reasoning of Hypatia of Alexandria. Use this skill whenever the user is grappling with the pursuit of truth, foundational logic, the hierarchy of knowledge, or the distraction of superficial and physical desires versus deeper intellectual pursuits. Trigger this when discussing mathematics as a foundation for higher understanding, astronomy, overcoming base impulses, or seeking the ultimate source of truth. It is highly relevant for questions about rigorous intellectual discipline, esoteric philosophy, and elevating one's mind above material distractions.
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Foundational concepts in nutrition science — macronutrients, micronutrients, energy balance, Atwater factors, reference intakes (DRI/RDA/AI/UL), food composition tables, and the methods of human nutrition research. Grounds the rest of the department in a shared vocabulary and in the measurement limits of the field, including controlled-trial history, observational-study limits, and the biochemical basis for calorie accounting. Use when a question asks what something "is" nutritionally, how energy and nutrients are measured, or what the reference numbers mean.
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