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概要と使いどころ
Modelo de reconhecimento de fala de propósito geral da OpenAI. Suporta 99 idiomas, transcrição, tradução para inglês e identificação de idioma. Seis tamanhos de modelo, de tiny (39M parâmetros) a large (1550M parâmetros). Use para speech-to-text, transcrição de podcasts ou processamento de áudio multilíngue. Melhor para ASR robusto e multilíngue.
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。
Skill de pesquisa profunda alimentado por NotebookLM MCP. Conduz pesquisa estruturada em múltiplas fontes (análise de mercado, inteligência competitiva, análise de tendências, pesquisa de prospects) usando Google NotebookLM como mecanismo de pesquisa, depois entrega briefs formatados e artefatos opcionais de estúdio (slides, podcasts de áudio, vídeos, infográficos, relatórios, mapas mentais).
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。
Load this skill whenever the project contains audio or video content, media players, podcasts, video embeds, or any <audio>/<video> elements. Under no circumstances publish audio or video without captions, transcripts, and audio descriptions where required. Absolutely always apply WCAG 1.2 criteria for time-based media.
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。
Get structured data from catalogued providers instead of scraping pages. Firecrawl Alexandria is a library of official APIs, licensed publishers, and Firecrawl indexes (financial series, company data, government contracts and law, podcasts, listings, research). Use this skill when the user needs records, listings, prices, filings, transcripts, or a dataset, says "is there an API for", "find a data source for", "pull the records", "get the dataset", or when assembling the answer by scraping would take many pages that one provider call returns directly. Discover with search, inspect with find_tools, execute with scrape.
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。
Distributed machine learning, data mining, and iterative HPC with Exasol. Covers end-to-end ML pipelines (DISTRIBUTE BY + SET scripts + BucketFS), per-entity federated training with partial_fit and ctx.reset(), batch inference, map-reduce ensemble training, distributed ensemble and SON algorithm for frequent itemset mining (Apriori, FP-Growth, association rules, market-basket analysis), Lua execute script orchestration for iterative algorithms (k-means, SGD, gradient descent), scikit-learn model training, parallel hyperparameter search, per-entity forecasting, anomaly detection, model lifecycle in BucketFS (pickle/joblib/ONNX versioning), GPU acceleration via CUDA SLCs (PyTorch/TensorFlow/RAPIDS), and ML-specific performance tuning (skew, OOM, multi-pass chunking).
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 voice generation, text-to-speech, and voice synthesis via inference.sh CLI. Models: Kokoro TTS, DIA, Chatterbox, Higgs, VibeVoice for natural speech. Capabilities: multiple voices, emotions, accents, long-form narration, conversation. Use for: voiceovers, audiobooks, podcasts, video nar...
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。
Summarize or extract text/transcripts from URLs, podcasts, and local files (great fallback for “transcribe this YouTube/video”).
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。
Comprehensive data science workflow skill for analyzing tabular and time series data. Use when conducting exploratory data analysis, building predictive models, performing feature engineering, or generating data-driven insights. Supports end-to-end analysis from data understanding through model deployment, following industry best practices for regression, classification, time series forecasting, and statistical analysis.
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。
Full type inference philosophy (never cast, never annotate inferred values), Register module declaration, from narrowing on hooks and Link, strict:false for shared components, getRouteApi for code-split typed access, addChildren with object syntax for TS perf, LinkProps and ValidateLinkOptions type utilities, as const satisfies pattern.
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。
Econometrics skill for time series analysis. Activates when the user asks about: "time series", "stationarity", "unit root test", "ADF test", "KPSS test", "ARIMA", "ARMA", "autocorrelation", "ACF", "PACF", "VAR model", "VECM", "Granger causality", "cointegration", "impulse response function", "forecast", "seasonal decomposition", "ARCH", "GARCH", "时间序列", "平稳性检验", "单位根", "自回归", "格兰杰因果", "协整", "脉冲响应", "预测", "向量自回归"
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。
Knowledge base from the NIST/SEMATECH e-Handbook of Statistical Methods (NIST HB 151) — the practical statistics reference for engineering, metrology, and quality. Use for: exploratory data analysis and the four univariate assumptions (4-plot); measurement process characterization including bias/precision, calibration designs, gauge R&R, and ISO/GUM uncertainty budgets; production process characterization (stability vs. capability); process modeling and regression (LS/WLS/NLS/LOESS); design of experiments (screening, fractional/factorial, response-surface, Taguchi); statistical process control (Shewhart/CUSUM/EWMA charts, capability indices, acceptance sampling); product/process comparisons (hypothesis tests and confidence intervals for 1/2/3+ groups, ANOVA, multiple comparisons); and reliability (lifetime & repair-rate models, accelerated testing, reliability growth). Scope limits: this is applied frequentist statistics for measurement and quality — it does NOT reproduce the per-distribution formula galleries, worked case studies, datasets, plot images, or Dataplot/R code of the original web Handbook (those are described, not copied); it is thin on modern machine learning, Bayesian methods beyond conjugate reliability priors, time-series/forecasting, and Bayesian experimental design.
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。
Knowledge base from the NASA Schedule Management Handbook (2024 edition, Revision 2). Use for program/project schedule management of aerospace and large engineering efforts: the five sub-functions (Planning, Development, Assessment & Analysis, Maintenance & Control, Documentation & Communication); the Integrated Master Schedule (IMS) and the WBS/OBS/CBS breakdown structures; the Schedule Management Plan (SMP) and Basis of Estimate (BoE); Critical Path Method, logic, durations, float and margin; tiered schedule assessment (Requirements/Health/Basis checks, the Shock Test) and the Dimensions of Schedule Reliability; Schedule Risk Analysis (SRA), Integrated Cost-Schedule Risk Analysis (ICSRA), Monte Carlo, correlation, merge bias, and the Joint Confidence Level (JCL); baselining, replan vs. rebaseline, EVM schedule metrics (SPI, BEI, CEI, HMI, Earned Schedule, CPLI), and status/progress/forecast reporting. This is NASA's scheduling doctrine grounded in NPR 7120.5 and the GAO Schedule Assessment Guide. Thin on the underlying mechanics of CPM, statistics, and EVM (it points to companion handbooks); aerospace/space-flight-centric; anchored to the 2024 second edition.
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。
Answers weather questions for a location or coordinates via wttr.in (free, no API key). Current weather + 3-day forecast. Location comes from the user request or preferences; optional short cache.
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。
Reviews a proposal, business case, deck or plan in character as a Chief Business Officer archetype, covering commercial model, partnerships, deal structure, market positioning, strategic fit and opportunity cost, and returns a DRAFT review with a verdict, findings that cite the exact passage, commercial risks and five interrogation questions. Use when the user asks to "run a CBO review", "pressure-test this partnership", "check the deal structure" or "what would a commercial executive say about this". Do not use for pipeline, quota, sales-motion or forecast reviews, use cro-reviewer instead. Drafts for human review; never approves, authorises or signs off.
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。
Reviews a proposal, business case, deck or plan in character as an Investor archetype and returns a DRAFT review in the chat with a verdict whose conditions read as terms, findings that cite the exact passage, capital-allocation risks, what would change the verdict and five interrogation questions. Use when the user asks to "run an investor review", "what would an investor say about this", "pressure-test the capital case" or "prepare me for the investment committee". Do not use for internal budget, cost or finance-function review, use cfo-reviewer instead; for revenue forecast and pipeline, use cro-reviewer. Drafts for human review; never approves, authorises or signs off.
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。
Organize user-scoped CLEC episodes into traceable Traditional Chinese Obsidian pages. Use when Codex must group X, pCloud, Google Drive, Bilibili, Odysee, and RSS or Podcast sources by five-digit episode number; obtain and review transcripts through media-transcript; clean readable transcripts with the confirmed CLEC lexicon; anonymize students; or build the standard three-layer episode page.
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。
Recast a supplied everyday photo as a witty illustrated product instruction sheet with a large Chinese title and concise English annotations. Use for 日常说明书、宠物使用说明、生活物品冷幽默说明图 or a fictional everyday object manual poster.
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。
Публикует готовый контент на личный YouTube-канал Антона [аккаунт]: вычерпывает новое из NotebookLM и очереди, рендерит аудио в видео, пишет описание с фрагментом расшифровки для SEO и авторским блоком Антона, грузит и читает статус обратно. Триггеры: /youtube-publish, /yt, выложи на ютуб, залей на youtube, что ждёт выгрузки. НЕ /teach-podcast и не /notebooklm — те ПРОИЗВОДЯТ, этот ПУБЛИКУЕТ.
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 aircraft and vessels from public ADS-B and AIS broadcasts using ADS-B Exchange, Flightradar24, FlightAware,… Use when following a tail number or flight, looking up an ICAO 24-bit hex code, registration or callsign, tracing a ship by IMO number or MMSI, checking a flag of convenience or port-call history, finding who owns a private jet or vessel
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。
Drive NotebookLM over a programmatic CLI with no browser: turn a vault slice into an audio overview, study guide or mind map, and fold the artifacts back into the vault. Browser automation is the fallback if CLI auth breaks. Triggers: "/notebooklm", "make an audio digest of <topic>", "podcast from the vault".
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。
Design and review native DTOs in WordPress plugins without requiring better-data - immutable data carriers, explicit from_array hydration, strict coercion instead of unchecked casts, WP_Error validation failures, sensitive-field discipline, nested DTO arrays, and clear separation from repositories, WP models, presenters, REST controllers, and HTML views. Use when a plugin introduces FooDto, request DTOs, settings DTOs, value objects, admin-row data shapes, REST response source objects, or when reviewing code that passes raw arrays, stdClass, WP_Post, WC_Order, $_POST, post meta, or option arrays through multiple layers. Mentions better-data only as an optional higher-level library; this skill is for native implementations.
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。
Builds native Android apps with the Android team's guides — Jetpack Compose UI, adaptive layouts, edge-to-edge, Compose styles, migrating XML views to Compose, Navigation 3 and predictive back; Android Gradle Plugin 9 upgrades, R8 keep rule analysis, profiling with Android Studio and Perfetto, and the Android CLI; Google Play policy compliance, Play Billing upgrades and the Engage SDK; Credential Manager restore and verified email; CameraX and Media3 Cast; App Functions and the ML Kit GenAI Prompt API; Wear OS, Android TV and display glasses. Use when writing, upgrading or auditing native Android (Kotlin) code for any of these topics, even if the user does not name the library.
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。
Senior Kotlin specialist code review — framework-agnostic. Use whenever the user asks to review, audit, critique, or get feedback on Kotlin code, a file, a class, a function, a module, a diff, a branch, a commit, or a pull request. Judges Kotlin language idioms, logic correctness, architecture and layering, concurrency (coroutines, Flow, structured concurrency, cancellation, race conditions), null safety, exception safety, testability, performance, and security. Detects force-unwraps, unchecked casts, GlobalScope, leaked mutability, broken structured concurrency, hidden side effects, weak abstractions, untestable seams, allocation hot spots, weak crypto, input-validation gaps, and PII logging. Outputs prioritized findings with file:line anchors and minimal-diff fixes. No assumptions about DI framework, UI framework, or build system.
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 Personal Assistant network skill. Use when: contacting another PA, coordinating with peer agents, scheduling meetings between owners, broadcasting messages, looking up contacts from the local PA directory, or performing any cross-PA task. Reads contact data from data/pa-directory.json in the workspace. Does NOT contain hardcoded contact data — each PA maintains their own directory file.
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。