「recording」の検索結果

Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership skills.

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 server-side call automation workflows including IVR systems, call routing, recording, and AI-powered interactions.

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。

Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration

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。

Queries myvariant.info BioThings aggregator for ClinVar, gnomAD, dbSNP, dbNSFP, COSMIC, CADD, and CIViC annotations in batched, version-tracked requests. Use when annotating variant lists from multiple databases simultaneously without managing per-source APIs, and when reproducibility-grade analyses require recording source data versions via _meta.

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 this skill when the user asks about refrigerated (reefer) trucking — cold chain compliance, FDA temperature recording requirements, Sanitary Transportation Rule (STR / 21 CFR 1.900-1.934), commodity-specific temperature ranges, refrigeration unit operation, temperature recorder calibration, and how a load is challenged if temperature deviates. Cite 21 CFR Part 1 Subpart Q.

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。

Execute a bounded Coval evaluation on explicit test cases, then audit conversation, recording and metric evidence. Use when the user requests a run; does not automatically rerun, tune an agent or schedule evaluations.

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。

Connect a customer agent to Coval and complete a small first evaluation with inspected voice or chat results. Use when no usable evaluation exists yet; reuse existing recordings and resources when available.

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 server-side call automation workflows including IVR systems, call routing, recording, and AI-powered interactions.

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。

Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration

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。

Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership skills.

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 this skill when the user wants to convert a lecture, presentation, or talk video into text formats (transcript, outline, or article). Trigger when user mentions processing video recordings, creating transcripts from lectures, or generating articles from recorded presentations.

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。

Analyze a screen recording of a manual process and produce targeted, working automation scripts. Extracts frames and audio narration from video files, reconstructs the step-by-step workflow, and proposes automation at multiple complexity levels using tools already installed on the user machine.

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 sources must stay traceable across sessions and years, when each reference needs a passport recording where it was found, how it was verified, and where it is cited, or when a draft needs a cited-but-unverified sweep before submission.

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。

Requires OFOX_API_KEY — create one at https://app.ofox.ai, plus two real screenshots of one interface in two states. Animates the transition between them as one video — both go into a single Ofox video job, and the model cross-fades the values that changed while the rest of the layout holds. Use when a user wants a short software demo animation out of captures they already have, e.g. "turn these two screenshots into a demo clip", "show the dashboard going from the free plan to the paid one", "animate this settings change for the docs", or "make a clip of the counter going from 3 to 25". Do not use for a pair that is not a user interface (see keyframe-animation), for a screen recording (record it instead), or when only one screenshot exists.

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Architects the encryption, codec/transport, and premium-feature layer of a group video-calling product, on top of an already-chosen SFU topology. Covers E2EE patterns (Zoom leader-fan-out, Signal pairwise-fan-out, MLS/TreeKEM as in Discord's DAVE, Telegram's ledger consensus), the SFrame (RFC 9605) + WebRTC Insertable Streams mechanism letting an SFU relay encrypted frames it can't read, codec/transport choice (AV1/VP9 SVC vs. simulcast, Opus, congestion control, Media-over-QUIC, WHIP/WHEP), and which "premium" features (virtual backgrounds, live captions, recording, breakout rooms, spatial audio) survive E2EE vs. structurally can't. Use when adding E2EE to an SFU-based call, choosing a 2026 codec/SVC strategy, or deciding which features to disable under E2EE mode. NOT for call topology/platform choice (see webrtc-adhoc-video-chat, which this composes with). NOT for one-way broadcast (see managed-video-streaming-pipeline). NOT for the DM/group-chat text backend (see realtime-messaging-backend-architecture).

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。

Expert in turning data visualizations into compelling video and animation content for social media and marketing. Activate on: data commercial, animated chart, data storytelling video, chart animation, Remotion data video, Motion Canvas, Flourish animation, D3 animation, data viz social media, dashboard recording, data narrative video. NOT for: static data visualization (use data-viz-2025), general video editing (use video-processing-editing), full video production pipeline (use ai-video-production-master).

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。

Performs claim-evidence verification for agent outputs, recording supported, contradicted, or insufficient-evidence results alongside source accessibility. Activate on 'detect hallucination', 'fact check', 'verify claims', 'check accuracy', 'find fabrications'. NOT for validation (use dag-output-validator) or confidence scoring (use dag-confidence-scorer).

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。

Verify every visual-evidence artifact (screenshot/GIF/recording) attached to a PR carries a provenance manifest binding it to real daemon-backed truth — daemon port, run id, transcript head hash, agent node id, commit, and an honest real/fixture/mock source label — and that operator-control-panel PRs cover the required set of proof states. Use when gating a PR's proof artifacts before merge, auditing whether a "LIVE" visual claim is backed by a real daemon run versus a reused or fixture-backed mock, or defining the required state-coverage set for a control-panel change. NOT for the capture technique itself — headless Playwright, `screencapture -x -l`, non-interruptive capture (use port-daddy-agent-skill's visual-evidence doctrine), designing the receipt body schema an artifact attaches to (use agent-work-receipt-designer), or the broader dogfood/Potemkin product-quality bar (use multi-agent-authoring-product-bar).

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。