「apt」の検索結果

AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.

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Build image analysis applications with Azure AI Vision SDK for Java. Use when implementing image captioning, OCR text extraction, object detection, tagging, or smart cropping.

日本語の概要は準備中です。原文の説明を表示しています。

Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Create or refresh FriendlyBet Story of the World Cup image assets and app overlays. Use when generating new 9:16 World Cup story artwork, replacing story-assets PNGs, writing image-generation prompts, checking real player shirt numbers, placing Hebrew/English story captions, or validating that story text does not cover player faces or get cropped in WhatsApp previews.

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Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

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Capture and classify new scope, write entry-point updates, and determine downstream cascade scope

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Apply Gang of Four design patterns to solve architectural problems in TypeScript. Use when refactoring code architecture, implementing extensible systems, decoupling components, or following SOLID principles. Covers all 22 GoF patterns: Creational (Factory Method, Abstract Factory, Builder, Prototype, Singleton), Structural (Adapter, Bridge, Composite, Decorator, Facade, Flyweight, Proxy), and Behavioral (Chain of Responsibility, Command, Iterator, Mediator, Memento, Observer, State, Strategy, Template Method, Visitor).

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Use when working with Azure Eventhubs — azure Event Hubs partition analysis, checkpoint management, capture status, consumer group monitoring, and throughput analysis.

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Use when working with Gcp Cloud Armor — google Cloud Armor security policy management, WAF rule configuration, adaptive protection analysis, and edge security policy management via gcloud CLI.

日本語の概要は準備中です。原文の説明を表示しています。

Use for auto error capture, categorization and skill gen.

日本語の概要は準備中です。原文の説明を表示しています。

Use at the end of every important response. Save your decision-making thoughts as a signed A2A insight so they become training data for future fine-tuning.

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Join a Google Meet call, transcribe live captions, optionally speak in realtime, and do the followup work afterwards. Use when the user asks the agent to sit in on a meeting, take notes, summarize, respond in-call, or action items from it.

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{"tool": "web_search", "params": {"query": "Continuous Learning Loop: Fehler-Capture +

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Train an OpenAmer-specific small model from the collected brain data (QLoRA on NVIDIA, CPU smoke-test on laptop). 'The organism becomes its own brain' — build a fine-tune dataset, train, eval, export.

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I-A LLMs Large Language Models (LLMs) are extensive, general-purpose models that can be pre-trained and adapted for specific tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the agent needs recursive reasoning, episodic memory retrieval, internet learning, or world-model prediction. Mini-OpenAmer: the 2B hybrid Mamba core with 9 tools, running 24/7 on Damir's laptop.

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LoRA · Hugging Face — We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition …;

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Playwright automation, Chrome DevTools debugging, and browser interaction testing. Use for E2E/unit tests, capturing screenshots, inspecting network/console logs, or validating user flows in web applications.

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Systematic writing framework for philosophy and interdisciplinary academic papers from optimized outline to submission-ready manuscript. Use when users want to: (1) write a paper from a detailed outline, (2) ensure quality control during writing, (3) maintain consistency across chapters, (4) prepare a submission-ready manuscript, or (5) systematically execute a planned paper. Triggered by phrases like 'write the paper from this outline,' 'compose the full manuscript,' 'execute the outline,' or when users have completed strategic planning (academic-paper-strategist skill) and are ready to write. Takes optimized outline as input; outputs complete manuscript with iterative quality checks.

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Capture a self-contained static HTML snapshot from a running app or mock component so it can be reviewed or uploaded to Stitch.

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Investigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.

日本語の概要は準備中です。原文の説明を表示しています。

Create Remotion walkthrough videos from Stitch screen exports with ordered assets, transitions, captions, and render checks.

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Deploy and configure web frameworks on Netlify — build settings and SSR/edge adapters plus local platform emulation and env vars. Use when setting up or fixing a framework deploy (Next.js / Astro / Nuxt / SvelteKit / Remix / React Router / TanStack Start / SolidStart / Gatsby / Angular / Vite / Express / Hydrogen / Hugo / Eleventy / Vue / React), adding SSR or edge functions or middleware wired to Netlify context, fixing SPA redirect and catch-all rules, setting a build command or publish directory, or debugging "why isn't my env var updating" and framework build failures.

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Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: "best model for X", "what model should I use for", "top models for [task]", "which model runs on my laptop/machine/device", "recommend a model for", "what LLM should I use for", "compare models for", "what's state of the art for", or any question about choosing an AI model for a specific use case. Always use this skill when the user wants model recommendations or comparisons, even if they don't explicitly mention HuggingFace or benchmarks.

日本語の概要は準備中です。原文の説明を表示しています。