3,176 件 ・ 関連度順
概要と使いどころ
End-to-end system for creating supervised fine-tuning datasets from books and training style-transfer models. Covers text extraction, intelligent segmentation, synthetic instruction generation, Tinker-compatible output, LoRA training, and validation.
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 build LLM-powered projects from ideation through deployment. Use when starting new agent projects, choosing between LLM and traditional approaches, or structuring batch processing pipelines.
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 wants to run SQL - especially analytical SQL - on local files (parquet/csv/json), URLs, S3 paths, or remote databases (Postgres, MySQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake) without setting up a server. Provides chDB - embedded ClickHouse SQL in Python with 1000+ functions, Session for stateful multi-step pipelines, parametrized queries, and cross-source joins via `s3()`, `mysql()`, `postgresql()`, `iceberg()`, `deltaLake()`, `remoteSecure()` table functions. TRIGGER when: user wants SQL on parquet/csv/files or across remote analytical sources; uses ClickHouse SQL features (window functions, windowFunnel, geoToH3, JSON path ops, Session, parametrized queries); imports `chdb` or calls `chdb.query()`. SKIP this skill for pandas-style DataFrame method-chaining (use chdb-datastore instead) or ClickHouse server administration.
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。
把确认版模块 HTML、整页 HTML 中指定的 Section，或既有 Elementor 页面中用户明确批准的简单强参考模块，以克制字段实现为插件式 Elementor Widget。用于新增或迁移单个已确认 Widget；不用于设计探索、复杂参考图直做、批量整页实现、旧 Widget 排错、主题开发或发布。
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 a lean, mobile-aware Product Requirements Document for a NowStack Mobile app through interactive discovery, then save it to .agents/plan/PRD.md. Use for "write a PRD", "ns prd", "define the product", "plan my app", or as the first planning step before ns-architecture and ns-tasks. Mobile-flavored — personas, onboarding/paywall flow, platforms (iOS/Android/web), IAP vs subscription monetization, store scope. Feeds ns-init-project (site-config) and the rest of the planning pipeline.
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。
Programmatic React video best practices for Remotion. Use this skill whenever the user is writing, debugging, or rendering Remotion code — compositions, useCurrentFrame animations, interpolate, spring, Sequence timing, <Img>/<Video>/<Audio> from @remotion/media, staticFile, calculateMetadata, the Studio preview, `npx remotion render`, captions, subtitles, voiceover, audio visualization, ffmpeg, transitions, light leaks, transparent video, Lottie, 3D scenes, Mapbox flyovers. Triggers especially on HtmlInCanvas / "html in canvas" requests, CRT terminal effects, glitch effects, video shaders, WebGL video, and any request to scaffold a new programmatic React video. Owns `remotion render` and everything about Remotion code. Do NOT trigger for finished MP4 ad assembly from existing slides (that is `/video-content`) or for the inspiration-to-MP4 reference-video pipeline (that is `/cmo-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。
Use when the user wants professional brand-quality product images via Higgsfield's mode-specific prompt enhancement pipeline. Entry point for any product visual with a specific format or platform target. Use whenever: "product photoshoot", "lifestyle product shots", "Pinterest pin", "hero banner", "ad pack", "virtual try-on", "studio shot", "carousel images", "Meta ads creative", "model wearing product", "levitating product", "splash shot", "CGI style product", "restyle product image", Shopify image, brand campaign visual. 10 modes: product_shot, lifestyle_scene, closeup_product_with_person, moodboard_pin, hero_banner, social_carousel, ad_creative_pack, virtual_model_tryout, conceptual_product, restyle. Backend assembles the prompt — never call gpt_image_2 directly for product shots. NOT for: one-off images without a product (use image-gen), branded video with avatars (use higgsfield-generate), Soul training (use higgsfield-soul-id). Requires Higgsfield CLI and authed account.
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。
Brand-grounded Remotion video pipeline. Take a brief, ground it in `brand/` (voice, audience, positioning, creative-kit), write a beat sheet, scaffold a fresh Remotion project, generate any required assets, compose with frame-driven animations, and bake a final MP4. Use this skill whenever the user wants to build a NEW Remotion video from scratch — product films, motion graphics, code-driven shaders, CRT/glitch effects, HTML-in-canvas demos, React video. Triggers on requests like "make a remotion video", CRT/glitch/scanline effects, html-in-canvas demos, video shaders, WebGL video, and frame-driven React video. This is the mktg-native sibling of the personal `/cmo-video` skill: `/cmo-video` recreates the *style* of an existing reference video; `/cmo-remotion` builds something new from a brief. Always load `remotion-best-practices` before stage 5 (compose).
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。
Debug pipeline: investigates a bug, diagnoses root cause, writes failing tests, implements fix via TDD, and reviews the result. Orchestrates bug-investigator -> bug-fixer -> code-reviewer.
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。
QA pipeline: explores the running app like a real user using a browser, then produces a QA report and Playwright E2E tests for regression. Orchestrates app-scout → qa-agent.
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。
Refactoring pipeline: analyzes a target file or directory for code quality issues, plans safe incremental improvements, optionally writes tests first as a safety net, implements the changes, and reviews the result. Orchestrates refactor-planner → (test-writer) → parallel-task-orchestrator → code-reviewer.
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。
Produce a complete narrated MYTHIC SHORT FILM end-to-end on Unsora MCP in the layered cut-paper diorama style — stacked matte cardstock worlds, a strict three-colour palette, exposure that swings, paper wipes hiding every seam, landing on a face. Animation-movie storytelling, not an explainer: gods, monsters, heroes, folklore, origin myths and transformations, told in ten-second story beats with a mythic narrator, native paper foley, and one assembled MP4. Use whenever the user wants a myth, legend, fable, folktale or fairy-tale short, a cut-paper or papercraft animated story, "tell the story of X" as a video, a stop-motion-look mythic film, or says "run the cardstock diorama myth animation" or "run the diorama myth pipeline" — even with no story given, since the skill proposes myths itself. NOT for fact-driven explainers with charts and statistics (that is vox-motion-graphics).
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。
Work with the @upstash/redis TypeScript/JavaScript SDK, a serverless HTTP-based Redis client for Next.js, Vercel, Cloudflare Workers, edge runtimes, and Node.js. Use when adding a cache (cache-aside, write-through, TTL and expiration strategies), session storage and user sessions, a key-value store, leaderboards and rankings with sorted sets, counters, distributed locks, queues with lists, streams and consumer groups, sparse index-addressed arrays and ring buffers (ARSET, ARINSERT, ARRING, ARGREP, AROP), embeddings and nearest-neighbour vector search stored inside Redis (VECTOR commands via redis.vector, separate from @upstash/vector), JSON documents, pipelines and MULTI/EXEC transactions, Lua scripting, read replicas, or full-text search, typo-tolerant search, facets, aggregations, and search over Redis stream entries with Upstash Redis Search (different from regular FT.SEARCH; also available for TCP clients via @upstash/search-redis and @upstash/search-ioredis). Also use when migrating from ioredis or node-redis, when a Redis connection is needed from a serverless function without connection pooling, when integrating @upstash/ratelimit, or when the user says Redis cache, KV store, session store, serverless Redis, or Upstash Redis. Also use when building AI agents on Redis with Upstash AgentKit (agent memory, chat history, RAG tools, tool caching, chat persistence, resumable streams, distributed locks) for the Vercel AI SDK, TanStack AI, or Vercel Eve. Supports automatic serialization/deserialization of JavaScript types.
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。
Convert PDF presentations to HTML slide templates using a visual reproduction approach. Pipeline: PDF → slide screenshots → Claude writes HTML matching each screenshot. Use when the user wants to convert a PDF to HTML slide templates, reproduce a presentation as HTML, or create reusable templates from existing decks. Triggers: 'pdf to html', 'convert pdf to template', 'reproduce this deck as html', 'create template from pdf'.
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 or refine open-source .NET code-quality gates for CI: formatting, `.editorconfig`, SDK analyzers, third-party analyzers, coverage, mutation testing, architecture tests, and security scanning. Use when a .NET repo needs an explicit quality stack in `AGENTS.md`, docs, or pipeline YAML.
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 document processing pipelines for extraction and routing. TRIGGERS - Use when user needs help with ai-document-processor related 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。
Build DPO training pipelines for AI alignment. TRIGGERS - Use when user needs help with ai-dpo-trainer related 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。
Build AI audio processing pipelines. TRIGGERS - Use when user needs help with ai-audio-processor related 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。
Build Solana applications with Helius infrastructure. Covers transaction sending (Sender), asset/NFT queries (DAS API), real-time streaming (WebSockets, Laserstream), event pipelines (webhooks), priority fees, wallet analysis, and agent onboarding.
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 any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
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。
批量简历生产线识别：在一批简历中检测版式、句式、项目组合、开源模式的结构性雷同，识别可能出自同一简历工具或培训模板的群体，产出的是"这些共性表述不具备区分度"的提问策略建议，绝不用于淘汰任何候选人。凡是同时面对多份简历要做批量判断就使用本技能——包括用户说"这批简历怎么都长一个样""这几个人是不是一个班出来的""帮我筛一下这十份简历"时。
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 any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
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。
Complete toolkit for programmatic video creation with Remotion + React. Covers animations, timing, rendering (CLI/Node.js/Lambda/Cloud Run), captions, 3D, charts, text effects, transitions, and media handling. Use when writing Remotion code, building video generation pipelines, or creating data-driven video templates.
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。
Chinese journal matcher — performs L3 6-dimensional matching (keywords, cited direction, trend, solicitation, institution, cross-discipline), calculates conditional acceptance probability with review cycle estimation, and outputs tiered submission strategy (aspiration-stable-safety) using 4-layer weighted ranking.
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。