1,310 件 ・ 関連度順
概要と使いどころ
Codex-native Amazon PPC keyword research using LaunchFast MCP. Use when the user wants competitor keyword discovery, keyword tiering, or a ready-to-upload Amazon Sponsored Products bulk file from 1-15 ASINs. Requires the LaunchFast MCP tool `amazon_keyword_research`.
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。
Codex-native multi-keyword Amazon opportunity scan using LaunchFast MCP. Use when the user wants to research 1-10 keywords, compare niches, or decide whether a product idea is a GO, INVESTIGATE, or PASS. Requires the LaunchFast MCP tool `research_products`.
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。
Basecalls raw Oxford Nanopore signal (POD5/FAST5) into reads with Dorado, choosing the chemistry-matched model and accuracy tier (fast/hac/sup), requesting modified bases (5mCG_5hmCG, 6mA, m6A) at basecall time, and handling duplex, demultiplexing, trimming, and HERRO read correction. Covers why the model+version is an irreversible analysis decision, why methylation cannot be recovered later, and why downstream polish/variant models must match the basecaller. Use when converting POD5/FAST5 to reads, picking a Dorado model for R9/R10 or RNA004, enabling methylation calling, basecalling duplex, demultiplexing barcoded runs, or correcting reads for assembly.
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。
Download raw sequencing reads from NCBI SRA using sra-tools (prefetch, fasterq-dump, vdb-validate) or the ENA mirror. Use when pulling FASTQ for SRR/ERR/DRR accessions, deciding between SRA-direct, ENA mirror, or AWS/GCP cloud mirror (STRIDES), handling --include-technical for 10x and other single-cell records, validating with MD5/vdb-validate, navigating SRR/SRX/SRS/SRP/PRJNA hierarchy, or finding accessions via pysradb. Encodes SRA cloud-egress economics, the fasterq-dump uncompressed-scratch trap, and the --max-size default that silently truncates large prefetches.
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。
FastAPI web framework patterns. Triggers on: fastapi, api endpoint, dependency injection, pydantic model, openapi, swagger, starlette, async api, rest api, uvicorn.
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 production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.
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。
Fast PARALLEL wiki lookup engine over wiki/techniques + wiki/payloads + wiki/tools + wiki/cheatsheets for a surface/service/vuln-class. Two modes - quick (one qmd search, cheap, fire constantly) and deep (4 parallel subagents, one per area, merged ready-to-use arsenal card, cached). This is the fast path arsenal that `arsenal` delegates to; the hunt-* skills each inline their own qmd_query and can hand off here for a parallel lookup. Use for "what do I use against <surface>", "arsenal for <X>", "deep/full arsenal", "tool + payload + technique + cheatsheet for <X>", "fast wiki lookup", "parallel wiki search", any "how do I attack/exploit <service|vuln-class>" where you want the documented tooling + payloads before hand-rolling.
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。
Fix Fastly Compute Rust build failures caused by edition2024 dependencies. Use when: (1) cargo build fails with "feature `edition2024` is required", (2) wit-bindgen or wasip2 crates fail to download/parse, (3) Fastly SDK pulls in incompatible transitive dependencies, (4) Build worked before but fails after dependency update. Solves by pinning wit-bindgen, wasip2, and related crates to pre-edition2024 versions.
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。
Fix silent failures in Fastly Compute@Edge when using send_async for fire-and-forget requests. Use when: (1) Background tasks (migrations, webhooks, notifications) triggered from Compute never complete, (2) send_async PendingRequest is immediately dropped, (3) Fire-and-forget pattern works locally but fails in production, (4) An async trigger "logs as triggered" but the target service never receives the request (especially after renaming backend constants — the URL hostname is cosmetic, the backend name determines routing, so a global rename can silently reroute calls to the wrong service). Also covers silent backend-not-found errors when FALLBACK_BACKENDS or other backend constants reference names not configured in the Fastly dashboard.
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。
Bun — fast all-in-one JavaScript runtime, bundler, test runner, and package manager. Use when speeding up Node.js projects, using Bun as a drop-in Node replacement, bundling with Bun, or running tests faster. Covers runtime APIs, package management, bundling, and Bun-specific features like Bun.serve, Bun.file, and Bun.sqlite.
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。
You are an expert in Biome, the Rust-based toolchain that replaces ESLint and Prettier with a single, fast tool. You help developers configure linting, formatting, and import sorting for JavaScript, TypeScript, JSX, JSON, and CSS — achieving 100x faster execution than ESLint+Prettier with zero configuration, unified diagnostics, and IDE 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。
把任意主题做成「讲解/科普视频」并渲染成 MP4：调研→审查→解说词→字幕→配音（edge-tts，或火山引擎语音合成 2.0）→**主题驱动风格编排**→并行构建 HTML 场景→确定性逐帧渲染→成片后出多画幅封面。**画面语言内置 23 个模板风格 / 8 个类别**（大胆信号卡、奢华极简、NYT 数据图表、瑞士网格、故障艺术、胶片漏光、流体 Hero、Logo 收尾、东方柔和有机、VFX 文字光标…共 23 种风格，含每种的画布/配色/字体/时间轴规范，见 references/style-catalog.md）。**v2.0 新增**：①**动效库**（`assets/motion.js`，5 组共 40+ 动作词汇：弹簧/进出场/承接/接触/运镜/环境光）；②**4K60 + 快门运动模糊**（`--quality / --fps / --profile`，线性光积分 + 三级快门闸门 `--shutter-only` / `--motion-hold`）；③**渲染提速**（多浏览器进程级并行 / `--png-fast` / `--jpeg` / `--resume` 断点续渲），且**渲染前必须先问用户选哪条中间帧通道**（`png-fast` / `jpeg q95` / `png` / `jpeg q82`，**默认推荐 `png-fast`**，四条都要列全并附速度对比与描述；未拍板由 `gate_check.py --phase render` 挡下）；④**主题驱动模板编排**（`scripts/style_director.py`，按内容类型/情绪/节奏/受众挑风格、混用与局部替换、动态开头与转场，不再一片一模板）。流程规范与音画同步体系承自 anything2explainer（词边界字幕、两级时钟、语速标定、多 agent 分工与 QC 判据），渲染层为自研 seek 式渲染器。**封面默认 16:9 + 3:4 两张**：抖音主封面 1920×1080 + 兼容主页栅格 3:4 的 1440×1080（独立重排，防切字）；**竖版投放再加 9:16 的 1080×1920**（左右并置必须改上下堆叠、上下边距让开平台 UI 层）。独立可移植：GSAP 内置、playwright-core 随包、ffmpeg 走 imageio-ffmpeg 回退、浏览器自动探测 Chrome/Edge；**不依赖 html-video / anything2explainer 任何代码或目录**。触发场景：要做科普/讲解/教学/知识/产品类视频、"讲一下 X 做成视频"、要用 html-video 那种模板化画面但更稳的音画同步、要挑某种视觉风格（极简/数据/赛博/电影感/品牌）出片、要出抖音封面/竖版封面/9:16 封面、anything2explainer 换 HTML 渲染、或提到 html-explainer / HTML 讲解视频 / explainer video / MG 视频。
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。
语音转文字 (ASR) (语音转文字 (ASR / 语音识别 / Speech-to-Text) 应用工程 (从业者视角) — 选型、集成、优化语音转文字能力，尤其面向移动端、低成本、快速识别的场景。覆盖: (a) 引擎/API 地图 — 云端 API(OpenAI gpt-4o-transcribe / Whisper API、Deepgram、AssemblyAI、Google / Azure / AWS Transcribe、讯飞、字节火山、阿里、腾讯、百度) vs 开源模型(Whisper / faster-whisper / whisper.cpp / distil-whisper、NVIDIA NeMo Parakeet / Canary、阿里 FunASR / Paraformer / SenseVoice、Moonshine、Vosk、Kaldi / k2 / icefall) vs 端侧·移动 SDK(whisper.cpp + CoreML / Metal、iOS Speech framework、Android SpeechRecognizer、Picovoice、SenseVoice 端侧); (b) 准确率(WER / CER) × 延迟(RTF / 流式) × 成本 三角权衡与选型决策树; (c) 成本优化 playbook — 端侧免费 / 批量折扣 / VAD 裁静音 / 量化(int8 / ggml) / 蒸馏 / 自托管 break-even; (d) 移动端集成 — 端侧 vs 云、流式 vs 批量、断点检测(endpointing)、隐私 / 离线; (e) 后处理 — 标点 / 数字规整(ITN) / 说话人分离(diarization) / 时间戳。学派分歧: 云 API vs 端侧自托管、通用大模型(Whisper) vs 专用流式(RNN-T / Conformer)、闭源 API vs 开源、准确率派 vs 成本派、英文优先 vs 中文 ASR(FunASR / SenseVoice / 讯飞)。不含: 文字转语音(TTS / 语音合成，方向相反)、声纹识别 / 说话人验证为主业、语音 agent / 对话式 AI、ASR 模型训练科研深水区。) Master OS — automated mastery of 语音转文字 (ASR / 语音识别 / Speech-to-Text) 应用工程 (从业者视角) — 选型、集成、优化语音转文字能力，尤其面向移动端、低成本、快速识别的场景。覆盖: (a) 引擎/API 地图 — 云端 API(OpenAI gpt-4o-transcribe / Whisper API、Deepgram、AssemblyAI、Google / Azure / AWS Transcribe、讯飞、字节火山、阿里、腾讯、百度) vs 开源模型(Whisper / faster-whisper / whisper.cpp / distil-whisper、NVIDIA NeMo Parakeet / Canary、阿里 FunASR / Paraformer / SenseVoice、Moonshine、Vosk、Kaldi / k2 / icefall) vs 端侧·移动 SDK(whisper.cpp + CoreML / Metal、iOS Speech framework、Android SpeechRecognizer、Picovoice、SenseVoice 端侧); (b) 准确率(WER / CER) × 延迟(RTF / 流式) × 成本 三角权衡与选型决策树; (c) 成本优化 playbook — 端侧免费 / 批量折扣 / VAD 裁静音 / 量化(int8 / ggml) / 蒸馏 / 自托管 break-even; (d) 移动端集成 — 端侧 vs 云、流式 vs 批量、断点检测(endpointing)、隐私 / 离线; (e) 后处理 — 标点 / 数字规整(ITN) / 说话人分离(diarization) / 时间戳。学派分歧: 云 API vs 端侧自托管、通用大模型(Whisper) vs 专用流式(RNN-T / Conformer)、闭源 API vs 开源、准确率派 vs 成本派、英文优先 vs 中文 ASR(FunASR / SenseVoice / 讯飞)。不含: 文字转语音(TTS / 语音合成，方向相反)、声纹识别 / 说话人验证为主业、语音 agent / 对话式 AI、ASR 模型训练科研深水区。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on 语音转文字 (ASR / 语音识别 / Speech-to-Text) 应用工程 (从业者视角) — 选型、集成、优化语音转文字能力，尤其面向移动端、低成本、快速识别的场景。覆盖: (a) 引擎/API 地图 — 云端 API(OpenAI gpt-4o-transcribe / Whisper API、Deepgram、AssemblyAI、Google / Azure / AWS Transcribe、讯飞、字节火山、阿里、腾讯、百度) vs 开源模型(Whisper / faster-whisper / whisper.cpp / distil-whisper、NVIDIA NeMo Parakeet / Canary、阿里 FunASR / Paraformer / SenseVoice、Moonshine、Vosk、Kaldi / k2 / icefall) vs 端侧·移动 SDK(whisper.cpp + CoreML / Metal、iOS Speech framework、Android SpeechRecognizer、Picovoice、SenseVoice 端侧); (b) 准确率(WER / CER) × 延迟(RTF / 流式) × 成本 三角权衡与选型决策树; (c) 成本优化 playbook — 端侧免费 / 批量折扣 / VAD 裁静音 / 量化(int8 / ggml) / 蒸馏 / 自托管 break-even; (d) 移动端集成 — 端侧 vs 云、流式 vs 批量、断点检测(endpointing)、隐私 / 离线; (e) 后处理 — 标点 / 数字规整(ITN) / 说话人分离(diarization) / 时间戳。学派分歧: 云 API vs 端侧自托管、通用大模型(Whisper) vs 专用流式(RNN-T / Conformer)、闭源 API vs 开源、准确率派 vs 成本派、英文优先 vs 中文 ASR(FunASR / SenseVoice / 讯飞)。不含: 文字转语音(TTS / 语音合成，方向相反)、声纹识别 / 说话人验证为主业、语音 agent / 对话式 AI、ASR 模型训练科研深水区。 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「语音转文字」「语音识别」「asr」「speech to text」「stt」
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 for any Python backend work in this project: building FastAPI endpoints, writing service functions, defining Pydantic/SQLModel schemas, running Alembic migrations, or debugging 422 errors. Essential for authentication and authorization patterns — setting up get_current_user, is_superuser checks, admin-only guards, role-based access, and dependency injection chains like Depends(). Also covers middleware, background tasks, async SQLAlchemy sessions, ORM relationship loading, and request/response design. Activate whenever the question involves Python API code, FastAPI patterns, or backend architecture in this codebase. Not for frontend, Docker, CI/CD, or infrastructure.
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。
Python/FastAPI coding standards including async patterns, Pydantic v2, SQLAlchemy 2.0, and project structure. Use when writing Python code, reviewing FastAPI projects, or learning FastAPI conventions.
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。
Search and filter TikTok global e-commerce products based on FastMoss data, supporting keyword search, multi-dimensional filtering (category, shop type, commission rate, sales, creator count, etc.) and sorting. Trigger when users mention TikTok product selection, TikTok product search, TikTok product data, TikTok creator sales, TikTok commission rate, TikTok bestseller tracking, TikTok GMV analysis, TikTok product search, TikTok product research, TikTok creator sales, TikTok commission rate, TikTok GMV analysis, FastMoss. Even if the user does not explicitly mention "FastMoss", trigger this skill whenever their need involves searching product data or analyzing product performance on TikTok.
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。
Query TikTok global e-commerce market top-selling product rankings via FastMoss data, supporting daily/weekly/monthly dimensions and category-level analysis. Trigger when users mention TikTok bestseller chart, TikTok hot product ranking, TikTok sales ranking, TikTok GMV ranking, TikTok category bestsellers, TikTok weekly product report, TikTok top-selling rankings, TikTok bestseller charts, TikTok GMV ranking, TikTok category hot sellers, TikTok weekly product report, FastMoss. Even if the user does not explicitly mention "FastMoss", trigger this skill whenever their need involves viewing TikTok platform bestseller rankings or sales rankings by time dimension.
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。
使用 FastMCP 在 Python 中构建、测试、检查和部署 MCP 服务器。当创建新的 MCP 服务器、将 API 或数据库包装为 MCP 工具、暴露资源或提示，或为 Claude Code、Cursor 或 HTTP 部署准备 FastMCP 服务器时使用。
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, test, inspect, install, and deploy MCP servers with FastMCP in Python. Use when creating a new MCP server, wrapping an API or database as MCP tools, exposing resources or prompts, or preparing a FastMCP server for Claude Code, Cursor, or HTTP deployment.
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 FastAPI code for routing patterns, dependency injection, validation, and async handlers. Use when reviewing FastAPI apps, checking APIRouter setup, Depends() usage, or response models.
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。
Query and invoke tools on MCP servers using fastmcp list and fastmcp call. Use when you need to discover what tools a server offers, call tools, or integrate MCP servers into 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。
Set up Fastlane for iOS/macOS app automation
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。
Speed up Rust development iteration when `cargo build --release` takes 10+ minutes even for one-line edits. Use when: (1) every release rebuild takes minutes despite tiny edits, (2) Cargo.toml has `lto = "fat"` and/or `codegen-units = 1` in `[profile.release]`, (3) you want a production- shaped build for testing without paying the LTO cost on every iteration, (4) you suspect cargo is re-linking the whole binary instead of doing incremental work, (5) developing rfdb/grafema-orchestrator/large Rust binaries where full LTO is enabled for shipped builds. Adds a `fast` profile that inherits release but disables LTO, raises codegen-units, enables incremental compilation — first build still slow because it populates a fresh `target/fast/` dep cache, but every subsequent rebuild drops from ~13 min to ~13 s (≈60×).
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。
Filter and select sequences by criteria (length, ID, GC content, N content, motifs, patterns, description) using Biopython, streaming so large files never load into RAM. Use when subsetting a FASTA/FASTQ file, removing unwanted or low-quality records, or selecting records by specific criteria. Use the paired-end-fastq skill instead whenever the input is paired R1/R2 reads.
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。