「rsa」の検索結果

Use when drafting an ACM CCS rebuttal during the HotCRP author-response window, covering threat-model defenses, adaptive-attack objections, ethics and disclosure questions, anonymity constraints, adversarial reviewer pushback patterns, and decision-focused replies aimed at the meta-reviewer.

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 front end of an Academy of Management Journal (AMJ) manuscript needs to engage the right literatures and stake a clear position in an ongoing theoretical conversation. Positions the paper among theories; it does not derive the hypotheses (amj-theory-development) or write the contribution paragraph (amj-contribution-framing).

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。

Produce a decision-ready brief of current brand, product, category, and competitor conversations across social platforms, including sentiment drivers, questions, risks, and growth opportunities.

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。

Monitor brand mentions, competitor activity, and industry conversations across social media and the web

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。

Discover rising category conversations, formats, sounds, questions, and creator patterns across social platforms, then separate durable demand signals from short-lived noise.

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。

AI video conversations - create real-time video calls with AI personas

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。

Triages all inbound leads from a given period — demo requests, free trial signups, content downloads, webinar registrations, chatbot conversations. Classifies by urgency, qualifies against ICP, enriches with context, and produces a prioritized action queue with recommended response for each lead. Tool-agnostic — works with any CRM, form tool, or lead source.

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。

Track what key opinion leaders (KOLs) in your space are posting on LinkedIn and Twitter/X. Surfaces trending narratives, high-engagement topics, and early signals of emerging conversations before they peak. Chains linkedin-profile-post-scraper and twitter-mention-tracker. Use when a marketing team wants to ride trends rather than create them from scratch, or when a founder wants to know which topics are resonating with their audience.

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。

Lead qualification engine with conversational intake. Asks structured questions to understand your qualification criteria, generates a reusable qualification prompt, then batch-enriches leads via Apify LinkedIn scraping and scores them with parallel processing. Outputs qualified/disqualified verdicts with confidence scores and reasoning to CSV or whatever output format the user prefers. Supports calibration mode for prompt refinement.

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。

Render a two-host podcast ad from a brand config and dialogue script. Includes the working planner, full-frame and split-screen assembly, captions from measured character timings, brand end card, approved paid-step adapters, and 65 quality-check falsification cases. Use for short conversational ads with two stable hosts in one room.

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。

Render a configurable iMessage conversation inside a properly framed phone, then a brand end card. Uses the original send/receive sounds and a shared frame timeline for text, typing, scrolling and sound. Free local Playwright + ffmpeg assembly; optional image/music generation belongs to separate gated capabilities.

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。

Manages Fabric IQ Ontology items, including entity and relationship types, data bindings, and definition updates, plus schema, lineage, grounding, and graph-walk exploration. Use for ontology modelling and traversal. For natural-language questions over a Power BI report use fabriciq.

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。

Build presentation plans for PPT / slides / decks through a conversation-first workflow, then propose multiple visual directions with preview images before writing deck specs. Use when the user asks to create a PPT, presentation, deck, 答辩稿, 路演 deck, 产品介绍 PPT, 汇报 PPT, or when the user has only a topic or rough materials and needs help clarifying structure, style, and page planning before generation.

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。

Calls microsatellite instability from WES/WGS/targeted-panel with MSIsensor, MSIsensor-pro, MSIsensor-ct (panel-aware), mSINGS, and MANTIS for FDA pembrolizumab MSI-H pan-tumor / Lynch syndrome / dMMR ICI biomarker. Use when stratifying ICI eligibility (Le 2015), pairing MSI with TMB-H (Sha 2020 / Salem 2018), screening Lynch syndrome (universal IHC + MSI), or distinguishing MSI-H tumors from POLE-exo hypermutator with overlapping signatures.

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。

Implement SwiftUI navigation patterns including NavigationStack, NavigationSplitView, sheet presentation, tab-based navigation, and deep linking. Use when building push navigation, programmatic routing, multi-column layouts, modal sheets, tab bars, universal links, or custom URL scheme handling.

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。

Build and audit SwiftUI, UIKit, and AppKit accessibility for VoiceOver, Voice Control, Switch Control, Full Keyboard Access, Dynamic Type, focus restoration, labels/traits/actions, traversal, custom rotors, NSAccessibility, XCTest checks, adaptive system preferences, and App Store accessibility declarations. Use when implementing accessible UI, fixing an accessibility audit, testing assistive-technology behavior, or substantiating App Store Accessibility Nutrition Labels.

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。

Conversational search runtime: send messages, keep sessions consistent, and verify retrieval behavior and responses.

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。

Drives a multi-turn conversation with a LiveKit agent running locally to see what it does. Use when the user says "test my agent", "try my agent", "does this work", "why did it call that tool", "it says the wrong thing when I ask X", "test this change", or whenever you have edited an agent and need to check how it behaves. Wraps `lk agent debugger`: start the agent in text mode, send turns, read the tool calls, handoffs, errors and logs behind each reply, and restart after an edit. It runs without audio or a LiveKit room at one LLM call per turn, so it is the preferred way for a coding agent to live-test during development, and the default when the user says "test" without naming unit tests or simulations.

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。

Runs LiveKit agent simulations and acts on the results. Use when the user says "run my simulations", "regression test my agent before deploying", "run the scenarios", "use lk agent simulate", "did my agent pass", "why did this scenario fail", "run simulations in CI", "test the audio pipeline", "check turn-taking and interruptions", or wants to check whole-conversation behavior before shipping. Covers text and audio mode and what each catches, running against a local or deployed agent, degraded-audio flags, automating a pre-release run, and triaging failures with list, view and export. For writing the scenarios use writing-livekit-scenarios. Not the default for a bare "test my agent", which goes to debugging-livekit-agents. Use this skill when the user names simulations, scenarios, a run, CI, or shipping.

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。

Deploys and operates a LiveKit agent in production: shipping a version to LiveKit Cloud and rolling it back, secrets and configuration, the worker process model and prewarming, safe async inside worker processes, provider timeouts and degradation, graceful shutdown, SDK upgrades, and observability. Use when the user says "deploy my agent", "roll back the deployment", "tail the agent logs", "the first call after a restart is slow", "attached to a different loop / event loop is closed", "prewarm the VAD", "shut down without dropping calls", "upgrade livekit-agents safely", "correlate logs by session", or is changing an agent codebase that is already live. Not for designing the agent (building-livekit-agents) or reproducing one bad conversation locally (debugging-livekit-agents).

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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).

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。

Discover OPC UA servers, connect MATLAB clients, and browse/navigate server nodes using opcuaserverinfo, the Local Discovery Service (LDS), opcua, connect, setSecurityModel, certificate-trust functions, findNodeById, opcuanode, and Namespace/Children traversal. Use when finding OPC UA servers on the network, querying endpoints and security policies, connecting to a server, authenticating with username/password or certificates, configuring security modes, handling certificate-trust or hostname-mismatch errors, troubleshooting failed connections or empty discovery, inspecting an OPC UA certificate (.der/.pem), browsing an address space, finding nodes by name or NodeId, navigating node hierarchies, invoking method nodes, or batch-processing large namespaces. Trigger on: opcuaserverinfo, LDS, opcua, opc.ua.Client, setSecurityModel, opc.ua.trustServerCertificate, OPC UA certificate, findNodeById, opcuanode, browse OPC UA, find node, OPC UA namespace, OPC UA method node, Industrial Communication Toolbox.

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。

Export conversation MATLAB code to a clean, runnable .m script. Use when asked to save or export session work. TRIGGER: user asks to save, export, or generate a script from the current session's MATLAB code. Also when asked for a reproducible script or clean version of what was run.

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。

Model a Phase-Locked Loop (PLL) IC from its datasheet or system specs using Mixed-Signal Blockset. Without this skill, agents universally select the wrong solver and produce non-functional PLL models — 100% of unguided attempts fail. Covers Integer-N, Fractional-N, Dual Modulus architectures, loop filter design, lock time optimization, VCO phase noise configuration, and msbPllArchitectures/msbPllFoundation block assembly. Use when: PLL modeling, frequency synthesizer design, phase noise simulation, lock time analysis, charge pump design, loop filter tuning, datasheet-to-model, Mixed-Signal Blockset PLL, msbPllArchitectures.

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。