1.3万 件 ・ 関連度順
概要と使いどころ
Domain routing and boundary guidance for authorized source code security review, including dangerous function tracing, data-flow analysis, logic flaw detection, and dependency review. Use when a task belongs to the code audit domain and needs scope, evidence, pivot, or exit criteria.
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Use when the user wants to evaluate a creator, influencer, or brand audience using public profile signals, TikTok audience demographics, follower/following data, comments, geography, language, and content fit. Helps judge sponsorship and market fit.
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音频降噪：去除录音中的背景噪声、电流声、风噪、嗡嗡声，基于 ffmpeg 滤镜链（afftdn/highpass/lowpass）。 当用户说"降噪""去噪""去杂音""消除背景噪声""电流声""风噪""录音有杂音""音频降噪"时使用。 和 audio-editing 的区别：audio-editing 做剪辑/转码/音量等通用音频操作（内置 denoise 兜底），本 SKILL 专做降噪调参。
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。
音频可视化视频：把纯音频（播客片段、音乐、口播金句、电台）渲染成带动态波形/频谱的视频，配封面和标题，好发到抖音/B站/视频号等只收视频的平台。当用户说 音频可视化、音频转视频、播客做成视频、音频波形视频、音乐可视化、给音频配画面、声波视频、频谱视频、把音频发到视频平台、电台切片视频 时使用。基于 shared/scripts/audio_viz.py（ffmpeg showwaves/showcqt/showspectrum）。与 audio-mix 区别：那个输出音频，本 SKILL 输出视频；与 slideshow-video 区别：那个用图片，本 SKILL 用音频驱动画面。
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。
音频混合 / 混音：把旁白口播 + 背景音乐 + 音效混成一轨，BGM 自动循环补足并可闪避（旁白说话时自动压低 BGM 保证人声清晰）。当用户说 混音、音频混合、旁白加背景音乐、配音加BGM、人声和音乐混一起、加音效、音频叠加、BGM 压低、闪避、ducking、把配音和bgm合起来 时使用。基于 shared/scripts/audio_mix.py。与 audio-editing concat 区别：concat 是前后顺序拼接，本 SKILL 是同时叠加混音；与 video-editing bgm 区别：那个给视频配乐，本 SKILL 输出纯音频。
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。
Measure Jetson DRAM/NvMap usage and verify before/after memory reclamation with live audit data.
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 Earth2Studio prognostic (time-stepping forecast) model wrappers. Do NOT use for diagnostic models, data sources, or installation.
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 and validate Earth2Studio data source wrappers (DataSource, ForecastSource, DataFrameSource, ForecastFrameSource) from remote stores. Do NOT use for fetching data with existing sources, model inference, or installation 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。
Find Earth2Studio models, data sources, and examples for a weather/climate use case. Do NOT use for writing inference code, downloading data, or installation.
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 deterministic forecast scripts with Earth2Studio (model, data source, IO, inference). Do NOT use for ensemble, diagnostics, data-only fetch, or install.
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。
Guide installing Earth2Studio via uv or pip, selecting model extras, and configuring the environment. Do NOT use for writing inference code, choosing models, or PhysicsNeMo questions.
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。
Fetch weather/climate data via Earth2Studio data sources for specific variables and times. Do NOT use for inference pipelines, model discovery, or installation.
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。
[omh] Hidden or swallowed errors suspected: find swallowed errors, unsafe fallbacks, hidden UI/runtime failures, and missing propagation before they become false green status. Use when the user says: failure-signal-audit, failure signal audit, silent failure, silent failures, silent failure hunter, swallowed error, swallowed errors, empty catch.
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[omh] Pull request or changes to vet: bug-first review with evidence. Use when the user says: code-review, review, audit, find bugs, release gate, claim audit, evidence audit, README claim.
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When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," or "SEO health check." For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema-markup.
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 real-time voice AI applications using Azure AI Voice Live SDK (azure-ai-voicelive). Use this skill when creating Python applications that need real-time bidirectional audio communication with Azure AI, including voice assistants, voice-enabled chatbots, real-time speech-to-speech translation, voice-driven avatars, or any WebSocket-based audio streaming with AI models. Supports Server VAD (Voice Activity Detection), turn-based conversation, function calling, MCP tools, avatar integration, and transcription.
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。
Generate AI-powered podcast-style audio narratives using Azure OpenAI's GPT Realtime Mini model via WebSocket. Use when building text-to-speech features, audio narrative generation, podcast creation from content, or integrating with Azure OpenAI Realtime API for real audio output. Covers full-stack implementation from React frontend to Python FastAPI backend with WebSocket streaming.
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Use when auditing content quality, E-E-A-T, or publish readiness; runs a typed 80-item CORE-EEAT profile with evidence coverage, veto checks, and a fix plan. Not for structural tags/headers alone — use on-page-seo-checker; not for domain/citation trust — use domain-authority-auditor. 内容质量/EEAT评分
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Use when the user asks to "map what our buyers believe", "capture the objections we keep hearing", or "find the switching forces that move the beachhead"; produces a belief map of the beachhead — held beliefs and mental models, the recurring objections and their reframes, and the JTBD four forces (push of the problem, pull of the new, anxiety of switching, habit of the present) — each item sourced from interviews or win-loss notes (User-provided) and labeled Measured / User-provided / Estimated, with any unverified quote or comparative claim marked "[needs source]" and routed to the claims candidates, never adjudicated here. Not for demographic or persona profiling — use audience-mapper; not for the positioning canvas — use positioning-truth-tracer. 受众信念/异议地图/切换四力/流失语言
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 user asks to "package our proof points", "build reusable stat cards and case snippets", or "put proof where each pillar makes its claim"; turns claims-ledger-approved proofs into reusable proof modules — stat cards, case snippets, testimonial blocks, comparison proofs — each pinned to a message-house pillar and the ledger claim ID it substantiates, and flags any pillar making a claim with no approved proof behind it. Never adjudicates a proof: unverified or ledger-absent proofs are marked '[needs source]' and routed to the claims candidates. Not for adjudicating or substantiating claims — use offer-claims-registry; not for fabricating a benchmark to fill a gap — a missing proof is flagged, not invented; not for scoring narrative quality — use narrative-quality-auditor. 证据模块/证据卡/客户案例/主张对齐
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 user asks to "map our positioning", "name our competitive alternatives", or "pick a beachhead segment for the launch"; produces a Dunford-style positioning canvas — named competitive alternatives (including spreadsheet and status quo), unique attributes (verifiable, or routed to the claims ledger), value themes (attribute→benefit→value chains), a target beachhead segment scored on serviceability / pain intensity / reachability, and a one-sentence onlyness statement — the sole upstream of the message house and the entity-signal source for the canonical entity profile. Not for the message house or per-channel launch copy — use message-house-builder; not for audience/persona profiling itself — use audience-mapper; not for SEO keyword positioning — use keyword-research. 定位画布/竞争替代品/独特价值/滩头细分
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Use when auditing domain authority, trust, or citation credibility; runs a peer-relative 40-item CITE profile with evidence coverage and verified manipulation/penalty veto checks. Not for page-level content quality — use content-quality-auditor; not for backlink profiling alone — use offsite-signal-analyzer. 域名权威/网站可信度
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Use when the user asks to "is my ad fatiguing", "why is CTR dropping at scale", or "should I rotate creative / widen the audience"; reads frequency, CTR and CVR decay against an early-flight baseline and returns Rotate-creative / Widen-audience / Hold triggers with a per-ad-set fatigue read. Not for building the replacement creative — use ad-creative-builder; not for the RQS score or vetoes — use ad-account-auditor. 广告疲劳检测/频次管理/换素材还是扩人群
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。
Inventory and prioritise accumulated design inconsistencies across a product. Use when drift has built up over time. For token coverage specifically use `design-token-audit` (designer-toolkit); for WCAG gaps use `accessibility-audit` (design-systems).
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。