'foundation' 검색 결과

Integrate on-device AI using Foundation Models framework, Core ML, and open-source LLM runtimes on Apple Silicon. Covers Foundation Models (LanguageModelSession, @Generable, @Guide, SystemLanguageModel, structured output, tool calling), Core ML (coremltools, model conversion, quantization, palettization, pruning, Neural Engine, MLTensor), MLX Swift (transformer inference, unified memory), and llama.cpp (GGUF, cross-platform LLM). Use when building tool-calling AI features, working with guided generation schemas, converting models, or running on-device inference.

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Run a health check on an existing project: dependency audit, security scan, test runner detection, CI/CD evaluation, and missing configuration analysis. Maps the three execution gates (pre/in/post) from /10x-bootstrapper to an assessment framework for existing codebases. Reads optional context/foundation/stack-assessment.md from /10x-stack-assess to focus checks on identified gaps. Writes context/foundation/health-check.md with findings, prioritized fixes, and an agent-readiness verdict. Use when the user has an existing project and wants to verify its health before working with an agent. Trigger phrases: "health check", "check my project", "audit my project", "is my project healthy", "sprawdź projekt", "audyt projektu", "health-check", "project health". Use AFTER /10x-stack-assess (brownfield chain), BEFORE agent onboarding (m1-l4).

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Apple Intelligence (Foundation Models / Evaluations / Image Playground) の API リファレンス。 LanguageModelSession, SystemLanguageModel, PrivateCloudComputeLanguageModel (PCC), Generable, GenerationSchema, Tool, OCRTool, Transcript, DynamicInstructions, Attachment, Evaluation, ModelJudgeEvaluator, ToolCallEvaluator, ImagePlaygroundSheet, ImageCreator, Foundation Models SDK for Python (apple-fm-sdk)。

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RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.

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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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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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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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Create an Agent SKILL.md fallback for an existing find-XXXX finder that relies on a fragile discovery foundation — above all LLM_DECOMPILE (patterns C/D/E), which matches the decompiled shape of a predecessor function against a stored reference and breaks when a symbol is inlined or de-inlined in a way the reference does not cover. The generated fallback coexists with the preprocessor and runs only when it returns failure, recovering every target robustly by decompiling the predecessor and following the inline/de-inline boundary with semantic anchors. Use when a finder broke on a game update, or you want to durably backstop one before it does. The recipe generalizes to any finder foundation. Triggers: create agent skill fallback, add SKILL.md fallback, robust fallback for finder, backstop LLM_DECOMPILE finder, final guarantee skill

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Develops iOS/iPhone applications with XcodeGen, SwiftUI, and SPM. Triggers on XcodeGen project.yml configuration, SPM dependency issues, device deployment problems, code signing errors, camera/AVFoundation capture debugging, iOS version compatibility, or "Library not loaded @rpath" framework errors. Use when building iOS apps, fixing Xcode build failures, or deploying to real devices. Not for AVPlayer playback issues (seek/scrub/frame stepping → avfoundation-reference skill).

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Apple FoundationModels framework for on-device LLM — text generation, guided generation with @Generable, tool calling, and snapshot streaming in iOS 26+.

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Review stylesheets for a CSS reset or normalization layer that reduces differences in browser defaults. Check import order, framework provisions, and rendered layouts across screen sizes and interaction states, then suggest changes for a consistent styling foundation.

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• Investigating layout differences between browsers
• Choosing a reset for a new site's styles
• Checking reset coverage in a CSS framework

Helps agents build the foundations of a Three.js scene, including cameras, rendering settings, object grouping, and movement. Code examples cover coordinate systems, resizing, animation timing, resource cleanup, and basic performance improvements for browser-based 3D projects.

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• Set up a browser-based 3D scene
• Choose between perspective and orthographic cameras
• Move or rotate several objects as a group

Define the types and shared registrations needed to add a syncable entity to Twenty's server. Set up database entities, editable fields, action types, and relationships as the foundation for workspace migration implementation.

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• Define a new syncable entity in Twenty
• Create flat data and migration action types
• Register editable fields and comparison rules

Create and maintain a shared product marketing document covering the product, audience, differentiation, and brand voice. Draft it from existing project materials or guided questions, then preserve revisions and tested lessons for other marketing skills.

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• Establish shared context before starting marketing work
• Draft a product brief from existing project materials
• Align target customers, differentiation, and brand voice

Work with Waypoint models to analyze microbiome samples, the communities of microbes in a sample. Convert taxonomic abundance tables, generate numerical sample representations, configure further training for prediction tasks, and evaluate models on the Compass benchmark.

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• Convert microbiome abundance tables into Waypoint inputs
• Generate sample vectors for clustering and visualization
• Configure further training with sample labels

Design a role permission matrix showing which business areas each role should view or edit, including confidentiality levels. Get a review workflow and, on request, empty CSV templates, SQL table definitions, JSON Schema, or Notion mappings.

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• Defining viewing and editing permissions by role
• Planning access rules for confidential records
• Preparing a quarterly access review template

Establish consistent typography, colors, spacing, and icons for interface design. Define design tokens, reusable values for visual styles, and apply guidance on contrast, responsive text, and dark mode to improve readability and visual consistency.

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• Define design tokens for a new project
• Create typography and spacing guidelines
• Review color contrast and visual consistency

Build a consistent interface foundation with Tailwind CSS v4 using shared design values and reusable components. Covers responsive layouts, dark mode, accessibility, and component variations to help standardize UI patterns across a codebase.

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• Building a reusable UI component library
• Standardizing colors, corner radii, and themes
• Creating responsive and accessible components

Connect an AI agent to Power Automate through FlowStudio MCP. This skill covers authentication, discovering available tools, reusable Python or Node.js helpers, and extracting useful data from responses too large to display directly.

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• Set up authentication for a Power Automate connection
• Discover available tools for a task
• Build a reusable Python or Node.js helper

Forecast sales, demand, or sensor readings with Google's pretrained TimesFM model, without custom training. Check hardware and dataset memory needs, include external factors such as prices or holidays, and obtain forecasts with uncertainty ranges.

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• Forecasting future sales or demand from CSV data
• Including prices and holidays in sales forecasts
• Forecasting multiple stores' time series in batches

Plan game development by choosing specialized skills for the target platform, 2D or 3D graphics, and gameplay needs. Get guidance on shared foundations such as game loops, input handling, design patterns, and performance budgets.

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• Planning a browser-based 2D platformer
• Designing a mobile puzzle game
• Organizing development for a multiplayer VR game

Use SAM to identify object regions in images from points, boxes, or existing masks. The skill provides implementation examples for transparent cutouts, automatic mask generation, annotation tools, and model selection based on available resources.

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• Extracting objects with transparent backgrounds
• Building annotation tools with point or box selection
• Generating object masks automatically

Build table data processing with TanStack Table v9 while keeping data and column definitions stable. The skill guides type inference, which determines types automatically, row identity, optional features, and checks for missing APIs.

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• Set up the foundation for table data processing
• Troubleshoot column type inference
• Investigate missing APIs or optional features

Create a shared marketing context document covering the product, audience, competitors, customer language, and brand voice. Draft it from existing project materials or a guided interview, then update it and check completeness so related marketing skills can reuse the information.

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• Prepare shared context for a new marketing project
• Draft marketing context from existing project materials
• Document customer language and brand voice