Results for “adaptive”

Design and implement adaptive filters using DSP System Toolbox System objects. Use when working with adaptive filtering, system identification, noise cancellation, echo cancellation, active noise control (ANC), channel equalization, inverse system identification, or adaptive prediction. Covers dsp.LMSFilter, dsp.RLSFilter, dsp.FilteredXLMSFilter, dsp.FrequencyDomainAdaptiveFilter, dsp.AffineProjectionFilter, dsp.BlockLMSFilter, dsp.AdaptiveLatticeFilter, dsp.FastTransversalFilter, maxstep(), and algorithm selection for adaptive filtering problems. Replaces deprecated adaptfilt.* objects (removed R2020a).

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Add Adaptive Card templates, structured visual layouts for API responses, to MCP-based plugins in Microsoft 365 Copilot. Configure shared or item-specific layouts, bind response fields, and prepare responsive cards with validation scenarios.

• Displaying project status and budgets as cards
• Using different layouts for debit and credit entries
• Providing a default card when no template is selected

Add Google Mobile Ads banners to Android, iOS, or Unity apps. Guides implementation from defining the ad view and choosing its size to handling load events, loading the banner, and checking the completed integration.

• Add a banner ad to an Android app
• Place an adaptive banner at the top or bottom of an iOS app
• Integrate a banner ad into a Unity app

Coordinate multiple AI agents by adjusting their communication structure and task assignments as workloads change. The skill describes performance monitoring, switching coordination patterns, forecasting agent capacity, and validating changes with rollback when performance worsens.

• Choosing a coordination structure for multiple agents
• Redistributing tasks when agent workloads become uneven
• Forecasting how many agents a workload needs

Designs adaptive clinical trials including group-sequential (O'Brien-Fleming, Pocock, Lan-DeMets spending), sample-size re-estimation (blinded Friede-Kieser, unblinded Cui-Hung-Wang, Mehta-Pocock promising zone), seamless Phase 2/3 with treatment-arm selection, population enrichment, and response-adaptive randomisation. Covers FDA 2019 Final Adaptive Designs Guidance, FDA 2022 Master Protocols, and ICH E20 Step 2b/3 draft (June 2025, NOT final). Use when planning interim analyses, sample-size re-estimation, or master/platform-trial designs.

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Designs adaptive clinical trials including group-sequential (O'Brien-Fleming, Pocock, Lan-DeMets spending), sample-size re-estimation (blinded Friede-Kieser, unblinded Cui-Hung-Wang, Mehta-Pocock promising zone), seamless Phase 2/3 with treatment-arm selection, population enrichment, and response-adaptive randomisation. Covers FDA 2019 Final Adaptive Designs Guidance, FDA 2022 Master Protocols, and ICH E20 Step 2b/3 draft (June 2025, NOT final). Use when planning interim analyses, sample-size re-estimation, or master/platform-trial designs.

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AI自适应学习 (AI Adaptive Learning) Master OS — automated mastery of AI Adaptive Learning: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on AI Adaptive Learning problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「自适应学习」「智能诊断」「自适应题库」「学习路径规划」「知识追踪」

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Designs adaptive clinical trials including group-sequential (O'Brien-Fleming, Pocock, Lan-DeMets spending), sample-size re-estimation (blinded Friede-Kieser, unblinded Cui-Hung-Wang, Mehta-Pocock promising zone), seamless Phase 2/3 with treatment-arm selection, population enrichment, and response-adaptive randomisation. Covers FDA 2019 Final Adaptive Designs Guidance, FDA 2022 Master Protocols, and ICH E20 Step 2b/3 draft (June 2025, NOT final). Use when planning interim analyses, sample-size re-estimation, or master/platform-trial designs.

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Designs adaptive clinical trials including group-sequential (O'Brien-Fleming, Pocock, Lan-DeMets spending), sample-size re-estimation (blinded Friede-Kieser, unblinded Cui-Hung-Wang, Mehta-Pocock promising zone), seamless Phase 2/3 with treatment-arm selection, population enrichment, and response-adaptive randomisation. Covers FDA 2019 Final Adaptive Designs Guidance, FDA 2022 Master Protocols, and ICH E20 Step 2b/3 draft (June 2025, NOT final). Use when planning interim analyses, sample-size re-estimation, or master/platform-trial designs.

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Designs adaptive clinical trials including group-sequential (O'Brien-Fleming, Pocock, Lan-DeMets spending), sample-size re-estimation (blinded Friede-Kieser, unblinded Cui-Hung-Wang, Mehta-Pocock promising zone), seamless Phase 2/3 with treatment-arm selection, population enrichment, and response-adaptive randomisation. Covers FDA 2019 Final Adaptive Designs Guidance, FDA 2022 Master Protocols, and ICH E20 Step 2b/3 draft (June 2025, NOT final). Use when planning interim analyses, sample-size re-estimation, or master/platform-trial designs.

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Designs adaptive clinical trials including group-sequential (O'Brien-Fleming, Pocock, Lan-DeMets spending), sample-size re-estimation (blinded Friede-Kieser, unblinded Cui-Hung-Wang, Mehta-Pocock promising zone), seamless Phase 2/3 with treatment-arm selection, population enrichment, and response-adaptive randomisation. Covers FDA 2019 Final Adaptive Designs Guidance, FDA 2022 Master Protocols, and ICH E20 Step 2b/3 draft (June 2025, NOT final). Use when planning interim analyses, sample-size re-estimation, or master/platform-trial designs.

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Use this skill when baseline NeMo Relay instrumentation exists and the user wants to configure or evaluate adaptive plugin behavior, including telemetry, state, adaptive_hints, tool_parallelism, ACG, hint consumption, or measured rollout.

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Intelligent network quality analysis with adaptive loading strategies. Detects connection type (2g/3g/4g), bandwidth, RTT, and save-data mode, then automatically triggers appropriate optimization workflows. Includes decision trees that recommend image compression for slow connections, critical CSS inlining for high RTT, and save-data optimizations (disable autoplay, reduce quality). Features connection-aware performance budgets (500KB for 2g, 1.5MB for 3g, 3MB for 4g+) and adaptive loading implementation guides. Cross-skill integration with Loading (TTFB impact), Media (responsive images), and Core Web Vitals (connection impact on LCP/INP). Use when the user asks about slow connections, mobile optimization, save-data support, or adaptive loading strategies. Compatible with Chrome DevTools MCP.

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Adaptive content summarization - auto-detect content type and produce relevant summary

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Instructions to make or update an app's UI so that it adapts to different Android devices including phones, tablets, foldables, laptops, desktop, TV, Auto and XR. It includes how to handle different window sizes, pointing devices (such as mouse) and text entry devices (such as keyboard) using the Compose MediaQuery API. It also covers multi-pane layouts using Navigation3 Scenes, adaptive UI components (such as buttons) with varying target sizes, and adaptive layouts (including navigation areas - nav rails and nav bars) using the Compose Grid and FlexBox APIs.

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Design adaptive learning systems. TRIGGERS - Use when user needs help with adaptive-learning-design related tasks.

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Plan adaptive reuse projects. TRIGGERS - Use when user needs help with adaptive-reuse-plan related tasks.

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Design and implement adaptive testing systems using Item Response Theory (IRT). Use when working with computerized adaptive tests (CAT), psychometric assessment, ability estimation, question calibration, test design, or IRT models (1PL/2PL/3PL). Covers test algorithms, stopping rules, item selection strategies, and practical implementation patterns for K-12, certification, placement, and diagnostic assessments.

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Implements responsive and adaptive layouts in Flutter using flutter_screenutil v5.9.x (pixel-perfect scaling from Figma), sizer v3.1.x (percentage-based viewport), and responsive_framework v1.1.x (breakpoint-driven structure changes). Use this skill when building apps for multiple screen sizes (phones, tablets, foldables, desktop, web), fixing layout overflow or sizing issues, implementing breakpoint-based adaptive UI, creating pixel-perfect designs from Figma specs, handling orientation changes, supporting Flutter web or desktop responsive layouts, or any time the words 'responsive', 'screenutil', 'sizer', 'breakpoint', 'adaptive layout', or 'screen size' appear in the request. Includes a decision matrix for selecting the correct package per scenario.

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Plan adaptive reuse projects. TRIGGERS - Use when user needs help with adaptive-reuse-plan related tasks.

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Design adaptive learning systems. TRIGGERS - Use when user needs help with adaptive-learning-design related tasks.

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Connect Kortix to Microsoft Teams and handle conversations with progress updates, final replies, and interactive questions. The skill also covers reading channel discussions, exchanging files, and posting to conversations the bot has already joined.

• Connect Kortix to Microsoft Teams
• Show task progress and deliver a final reply
• Receive attachments and share completed files

Prepare audio stems, the separate parts of a track, for mixing by matching their durations with silence, loops, or crossfades. Verify each generated and processed file before moving to the next production step.

• Match audio parts with different durations
• Extend drums and melodic parts using suitable methods
• Check audio specifications before mixing

Use this skill when designing, generating an MVP blueprint for, auditing, troubleshooting, refactoring, or explaining an agentic harness for any domain. Covers provider-neutral agent architecture for OpenAI, Anthropic, and OpenAI-compatible APIs: agent loops, tool design, record provenance, interactive presentation, user-memory lifecycles, environment-adaptive tools, speculative tool execution, late-bound capabilities, permissions, system prompts, planning, goals, always-on agents and durable runtime, adaptive agent teams, context compaction, memory, skills, MCP/external connectors, public-board communications, realtime voice agents, hardware agents and board deployment, self-refining recursive harnesses, programmable context, continual refinement, observability, evals, prompt caching, agent-legible environments, feedback loops, and safety.

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