「apt」の検索結果

Adaption AI SDK for synthetic data augmentation and dataset adaptation. Use when building data pipelines with the Adaption Python SDK, uploading datasets (local files, Hugging Face, Kaggle), running augmentation/adaptation jobs, configuring brand controls (hallucination mitigation, safety categories, length), recipe specifications (reasoning traces, deduplication, preference pairs, prompt rephrase), evaluating dataset quality, downloading results, or any workflow involving `pip install adaption`, `from adaption import Adaption`, Adaptive Data, or the adaptionlabs.ai API. Also trigger when the user mentions synthetic data generation for fine-tuning, dataset augmentation pipelines, DPO preference pair generation, or grounding-based hallucination reduction on training data.

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Hunt CAPTCHA Bypass — 6 distinct patterns: (1) CAPTCHA field simply omitted from the request (server-side validation absent), (2) CAPTCHA token replayed from a solved challenge (no single-use enforcement), (3) CAPTCHA response accepted on a different endpoint than it was solved on (no binding to action/session), (4) static or predictable CAPTCHA values accepted (e.g. '0', 'null', empty string), (5) audio/accessibility CAPTCHA trivially solvable programmatically, (6) CAPTCHA only enforced after N failures (first N requests bypass it). Detection: intercept a successful form submission, remove the CAPTCHA field entirely, replay — if it still succeeds, server-side validation is absent. Medium severity standalone; High when it removes the only rate-limit gate protecting a login, registration, or payment endpoint.

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。

Screenplay format, page craft, the writing process and adaptation (剧本格式/写作流程/改编) — merged from Wendy Henson (spec format rules), Richard Walter (page craft, revision), Neill Hicks (selling vs shooting script, draft chain), Syd Field (master shot, card method, adaptation as original work), Robert McKee (step outline → treatment → screenplay), Julian Hoxter (format elements, adaptation four questions), Diamond & Weissman (one-page nine-step) and Eric Bork — plus an exact typographic grid, element conventions (V.O./O.S., MORE/CONT'D, dual dialogue), a machine-writable output contract (Fountain, with forced markers for Chinese), and Asian industry formats (Chinese 场号制 影视剧本格式, Japanese 柱・ト書き・セリフ / 決定稿). Use when formatting a spec script, choosing the output file format, deciding what goes in action lines, planning drafts and outlines, cutting pages, adapting a novel, play or true story, writing a Chinese TV/film or Japanese-style script page, or preparing a submission-ready draft.

Write algorithm-optimized captions/descriptions for social media posts and reels. Supports Instagram (Reels, Feed, Carousel), TikTok, Threads, Facebook, and YouTube Shorts. The user sends material (text, image, script, competitor post, screenshot) and gets ready-to-use captions for ALL platforms at once, in English. Use when asked to "write a caption", "write a description", "caption for instagram", "описание к посту", "подпись для рилса", "описание для тикток", "caption for reels", "social media post", or any caption/description writing task. Also triggers when user sends an image, screenshot, or text and says something like "сделай описание", "напиши подпись", "caption this", "описание для этого".

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。

Overlay doctrine for the embedded-captions workflow — the caption MODEL (drop / rail / embed) and the rule that captions are an OVERLAY composited on top of the film, never a reserved bottom band you shift content up to avoid. Load when adding captions/subtitles to a talking-head or launch video, when deciding whether a phrase should be dropped, ride the verbatim rail, or be promoted to a scarce embedded climax, when laying out a composition that will carry captions (do NOT reserve a keep-out band), or when centering a composition on the true frame center under captions. Quotes the rail+embed model from embedded-captions and constraint

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 multi-platform chat bots with Chat SDK (`chat` npm package). Use when developers want to (1) Build a Slack, Teams, Google Chat, Discord, Telegram, GitHub, Linear, or WhatsApp bot, (2) Use Chat SDK to handle mentions, direct messages, subscribed threads, reactions, slash commands, cards, modals, files, or AI streaming, (3) Set up webhook routes or multi-adapter bots, (4) Send rich cards or streamed AI responses to chat platforms, (5) Build or maintain a custom adapter or state adapter. Triggers on "chat sdk", "chat bot", "slack bot", "teams bot", "google chat bot", "discord bot", "telegram bot", "whatsapp bot", "@chat-adapter", "@chat-adapter/state-", "custom adapter", "state adapter", "build adapter", and building bots that work across multiple chat platforms.

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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。

Overlay doctrine for the embedded-captions workflow — the caption MODEL (drop / rail / embed) and the rule that captions are an OVERLAY composited on top of the film, never a reserved bottom band you shift content up to avoid. Load when adding captions/subtitles to a talking-head or launch video, when deciding whether a phrase should be dropped, ride the verbatim rail, or be promoted to a scarce embedded climax, when laying out a composition that will carry captions (do NOT reserve a keep-out band), or when centering a composition on the true frame center under captions. Quotes the rail+embed model from embedded-captions and constraint

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。

🇺🇸 SEO & GEO Dual-Engine Playbook 2026 — Rank on Google AND get cited by AI search (ChatGPT, Perplexity, Claude, Gemini). Battle-tested from AFFiNE (60k GitHub stars in 24 months) and 150+ AI startup consultations. E-E-A-T writing voice system, keyword funnel strategy, JSON-LD schema templates, comparison page SOP, IndexNow setup, Generative Engine Optimization content patterns. **Optimized for dev tools / OSS / B2B SaaS. 2C products (education, apps, games): see 2c-adaptation guide in references/.** 🇨🇳 SEO & GEO 双引擎增长手册 —— 同时拿下 Google 排名和 AI 搜索引用（ChatGPT、Perplexity、Claude、文心一言）的完整方法论。AFFiNE 60k stars 与 150+ AI 创业公司咨询实战验证。E-E-A-T 写作声音系统、关键词漏斗、JSON-LD 结构化数据、竞品对比页 SOP、IndexNow 推送、生成式引擎优化内容模式。**默认适用于开发者工具 / 开源项目 / B2B SaaS，2C 产品（教育/应用/游戏）请参考 references/2c-adaptation.md。** 🇯🇵 SEO & GEO デュアルエンジングロースプレイブック — Google検索とAI検索（ChatGPT、Perplexity、Claude）の両方で評価される手法。AFFiNE 60k starsと150社以上のAIスタートアップコンサルティングで検証。E-E-A-Tライティング、キーワードファネル、Schema.org構造化データ、比較ページSOP。**デフォルトは開発者ツール／OSS／B2B SaaS向け。2C製品（教育・アプリ・ゲーム）はreferences/2c-adaptation.mdを参照。** 🇰🇷 SEO & GEO 듀얼 엔진 성장 플레이북 — 구글 검색과 AI 검색(ChatGPT, Perplexity, Claude) 양쪽 모두에서 인용되는 방법론. AFFiNE 60k 스타와 150+ AI 스타트업 컨설팅으로 검증. E-E-A-T 글쓰기, 키워드 퍼널, Schema.org 구조화 데이터, 비교 페이지 SOP. **기본값은 개발자 도구/OSS/B2B SaaS용. 2C 제품(교육·앱·게임)은 references/2c-adaptation.md 참조.** Triggers: "SEO" | "GEO" | "Generative Engine Optimization" | "AI search optimization" | "ChatGPT SEO" | "Perplexity SEO" | "Claude SEO" | "search optimization" | "content SEO" | "technical SEO" | "schema markup" | "JSON-LD" | "structured data" | "IndexNow" | "E-E-A-T" | "keyword strategy" | "keyword funnel" | "comparison page" | "programmatic SEO" | "SaaS SEO" | "搜索优化" | "AI 搜索" | "内容 SEO" | "GEO 优化" | "生成式引擎优化"

Guides exploration of $autocapture events captured by posthog-js to understand user interactions, find CSS selectors (especially data-attr attributes), evaluate selector uniqueness, query matching clicks ad-hoc, and create actions. Use when the user asks about autocapture data, wants to find what users are clicking, needs to build actions from click events, asks about elements_chain, wants to build a trend or funnel filtered by clicks or other autocapture interactions, asks which properties autocapture sends, or asks how to filter $autocapture events. Only applies to projects using posthog-js autocapture.

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。

图片理解与数据提取 skill。当图片文件（.png/.jpg/.jpeg/.gif/.webp/.bmp）是主要输入且用户需要理解、提取数据或分析图片内容时使用。提供预配置的 caption 脚本（scripts/caption.py），通过 vision 模型将图片转为文本描述，无需额外配置 API Key。覆盖：(1) 通过 scripts/caption.py 对图表/表格/截图/流程图进行 caption，(2) 将 caption 文本解析为结构化 DataFrame，(3) 基于提取数据重新生成可视化图表，(4) 导出为 Excel/CSV。**遇到以下任一情况就主动使用本 skill，不要自行猜测图片内容**：①用户出现触发词：图片分析 / 图表提取 / 表格识别 / OCR / 图片描述 / 截图分析 / 图表数据 / 提取图片中的数据 / 图片转表格 / 识别图片 / image caption / extract data from image / chart analysis / table OCR；②用户上传或指定了图片文件（.png / .jpg / .jpeg / .gif / .webp / .bmp）并要求理解、提取数据或分析内容；③任务需要从图表截图、表格截图、UI 截图、流程图中提取结构化信息；④用户要求将图片中的数据转为 Excel/CSV 或重新生成可视化图表。仅不用于：图片编辑（裁剪、滤镜、缩放）、图片生成、不含数据的风景/人物照片描述。

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Cultural adaptation for translated content. Run AFTER blog-translate completes. Adjusts brand examples, CTAs, legal references, and formality for the target market (German, French, Japanese, Spanish, etc.). Deep cultural adaptation of translated blog posts. Goes beyond translation to swap brand examples, adapt CTAs, substitute legal references, localize statistic sources where possible, and adjust formality (Sie/du, tu/vous, formal/informal). Built-in profiles for DACH, Francophone, Hispanic, and Japanese markets, plus a custom-locale template. Makes content feel locally authored, not translated. Use when user says "localize blog", "blog localize", "cultural adaptation", "adapt for Germany", "adapt for France", "lokalisieren", "localiser", "adaptar".

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Use when a video needs its spoken words on screen through Scenario via MCP: burned-in styled captions for a TikTok, Reels, or Shorts cut, ad captions for sound-off feeds, YouTube subtitles, an SRT sidecar, transcription of a clip's audio, captions translated into another language, karaoke or word-by-word styles, or restyling and correcting an existing transcript. Keywords: captions, subtitles, SRT, transcribe, karaoke, word-by-word, burn in, closed captions, translate video, caption style.

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。

This skill should be used when the user asks to "write a chapter", "next chapter", "chapter outline", "draft chapter", "continue the story", "write a scene", "outline a chapter", or wants to write prose for a story project. NOT for planning a scene's structure, subtext, or outcome (use scene-craft), drafting without an outline (use discovery-drafting), or revising existing chapters (use revision-continuity).

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。

Cultural adaptation for translated content. Run AFTER blog-translate completes. Adjusts brand examples, CTAs, legal references, and formality for the target market (German, French, Japanese, Spanish, etc.). Deep cultural adaptation of translated blog posts. Goes beyond translation to swap brand examples, adapt CTAs, substitute legal references, localize statistic sources where possible, and adjust formality (Sie/du, tu/vous, formal/informal). Built-in profiles for DACH, Francophone, Hispanic, and Japanese markets, plus a custom-locale template. Makes content feel locally authored, not translated. Use when user says "localize blog", "blog localize", "cultural adaptation", "adapt for Germany", "adapt for France", "lokalisieren", "localiser", "adaptar".

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 coding skill for the full MapTiler platform — Cloud REST APIs, MapTiler SDK JS (built on MapLibre GL JS), native mobile SDKs, on-premise infrastructure, and vector tile schemas. USE WHEN the user wants to add a map to a web or mobile app, show locations or routes, display geographic data, build a store locator, create data visualizations on maps, add geocoding or address search, do reverse geocoding, look up elevation, do IP geolocation, fetch weather data, transform coordinates between CRS/EPSG, embed static map images, work with vector tilesets (Planet v4, Buildings, Contours, Outdoor, Ocean, Landcover, Cadastre), write MapLibre style expressions or data-driven styling, render 3D terrain, globe view, or 3D buildings extrusion, use satellite imagery, add markers, popups, heatmaps, or clustering, integrate maps in React, Vue, Svelte, Angular, Next.js, Leaflet, OpenLayers, Cesium, or deck.gl, build native maps on iOS (Swift), Android (Kotlin), Flutter, or React Native, self-host with MapTiler Server or generate tiles with MapTiler Engine CLI, or upload/manage tilesets via the Admin API. Also USE WHEN the user mentions MapLibre GL JS — the SDK extends MapLibre with built-in cloud services, helpers, and session billing.

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Capture outbound HTTP and telemetry from a CLI harness at runtime. Covers capture-channel selection (transcript file vs verbose-fetch stderr vs outbound proxy vs on-disk state), hook-driven per-event capture vs long-running session capture, JSONL output format for diff-friendly artifacts, and the observability table that maps each target to the cheapest channel that captures it. Use when a static finding needs runtime confirmation, when a payload shape is needed for a client re-implementation, or when dark-vs-live disambiguation requires watching what the binary actually sends.

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。

Burn timed, styled captions into a finished video on the local machine, without re-rendering the source. This is the manual fallback to the API's captioning endpoint, POST /v1/captions, which stays the default path: come here when the style the user wants is outside the API's presets, when the wording has to be hand-corrected before it is burned into the pixels, or when the source was rendered with audio disabled and the API refuses it. Transcribes the audio with Whisper, groups words into reading phrases, renders the captions in a real browser via HyperFrames, and composites them over the source with ffmpeg using a real alpha channel. Use when the user has an MP4 with speech and asks to add captions, subtitles, burned-in text, TikTok-style captions or word-by-word text to it, and the API path has been ruled out. Runs out of band, so it costs no credits and works on any video file whoever generated it. Not for generating video (use the video skills) and not for a separate .srt/.vtt sidecar file.

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Use when enabling, configuring, or monitoring Change Data Capture (CDC) entity selection, channel enrichment, and delivery usage limits from an admin perspective. Covers the PlatformEventChannel and PlatformEventChannelMember metadata, the standard ChangeEvents channel, custom __chn data channels, enrichedFields, filterExpression, destructive deselection, and PlatformEventUsageMetric / EventBusSubscriber monitoring. Trigger keywords: change data capture, CDC, entity selection, change event, platform event channel, channel member, enrichment, event delivery allocation. NOT for writing an Apex change-event trigger — use apex/change-data-capture-apex. NOT for subscribing an external system over Pub/Sub or CometD — use integration/change-data-capture-integration.

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The frontend Error Inspector capture system (captureError, lib/diagnostics/**). Use when wiring a new CapturedErrorSource or adapter, calling captureError from a new site, picking a red/orange/yellow tier or errorTierRules downgrade, or turning a console.error or swallowed catch into a captured error.

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1. GET the form/endpoint that shows a CAPTCHA to understand its field name (usually g-recaptcha-response, captcha, captcha_token, captcha_answer, h-captcha-response)

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。

動作確認やPR用の録画に字幕、注目箇所のズーム、再生速度の変更を加え、元動画を証拠として残しながら説明付きのMP4に仕上げるスキル。

• PR用の録画に説明字幕を付けたいとき
• UIの操作箇所を拡大したいとき
• 動作確認動画の待機時間を短縮したいとき

Sysinternals DebugView CLI (DbgViewCli) for capturing and analyzing usermode and kernel-mode Windows debug output from the command line. USE FOR: capturing OutputDebugString output, kernel DbgPrint/KdPrint capture, boot-time debug logging, remote debug monitoring, filtering debug output by PID or process name, crash dump analysis, automated debug capture with bounded execution. DO NOT USE FOR: non-Windows platforms, application-level logging frameworks (log4j, serilog), Azure Monitor or cloud telemetry, ETW tracing (use WPR/xperf instead), user-mode crash dumps (use WinDbg). Triggers: "debug output", "DbgView", "DebugView", "kernel debug", "capture debug logs", "boot logging", "OutputDebugString", "DbgPrint", "KdPrint", "remote debug monitor", "debug capture CLI".

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Smart triggered RF capture on NI USRP radios with Wireless Testbench — record only when a signal of interest appears, not continuously (triggered spectrum sensing). Use energy detection to capture when signal power rises above the noise floor, or preamble detection to cross-correlate against a known sequence (WLAN L-LTF, 5G NR PSS/SSS, LTE PSS, Zadoff-Chu, custom protocols) and capture only when that protocol's preamble is detected. Use when implementing triggered (wake-on-signal) capture or spectrum sensing, capturing protocol-based signals via cross-correlation, calibrating detection thresholds (plotDetectionSignals / plotThreshold), scanning frequency bands for activity, or building transmit-then-detect workflows.

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。