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概要と使いどころ
Installs and configures ktx, the open-source context layer for data agents — runs ktx setup non-interactively with hidden CLI flags, configures database connections and embeddings, installs agent integration, and verifies readiness. Use when the user asks an agent to add ktx to a project, connect data sources, install agent rules, ingest schema, or troubleshoot a local ktx install.
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Search the web, read source pages, and retrieve structured records with Firecrawl. Use for current information, finding sources, reading a supplied URL, or looking up data-provider capabilities.
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。
| Nuxt 4 production optimization: hydration, performance, testing with Vitest, deployment to Cloudflare/Vercel/Netlify, and v4 migration. Use when: debugging hydration mismatches, optimizing performance and Core Web Vitals, writing tests with Vitest, deploying to Cloudflare Pages/Workers/Vercel/Netlify, or migrating from Nuxt 3 to Nuxt 4.
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。
| Nuxt 5 production optimization: hydration, performance, testing with Vitest, deployment to Cloudflare/Vercel/Netlify, and migration from Nuxt 4. Use when: debugging hydration mismatches, optimizing performance and Core Web Vitals, writing tests with Vitest, deploying to Cloudflare Pages/Workers/Vercel/Netlify, or migrating from Nuxt 4 to Nuxt 5.
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。
Learn reusable 2D motion-generation knowledge from user-specified action resources with `/img2mo-learn <resource>`. Use when the user provides videos, extracted frame sequences, spritesheets, Spine assets, generated outputs, failed attempts, or reference motion folders and wants to summarize animation timing, pose beats, style traits, prompt patterns, extraction/cropping rules, or failure lessons into the project `img2mo-knowledge/` folder for later `/img2motion` generation.
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Think and work like an expert Biophysicist. Use when a task calls for Biophysicist judgment. Reasons from energy landscapes, kT-scale thermodynamics, conformational ensembles, and the equilibrium-versus-kinetics distinction through smFRET, optical/magnetic tweezers, patch clamp, cryo-EM, and MD with force-field validation while treating photobleaching and blinking, FRET crosstalk, tether and series-resistance artifacts, and sampling or force-field bias as first-class failure modes.
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。
Content-derived infographic poster — reads the source file and builds a statistical poster from the facts already in it. Not a dashboard, not a CV, not an AntV template. Uses common-scale encodings (Cleveland–McGill floor), ISOTYPE unit counts (Neurath), custom data glyphs (Lupi/Fragapane), and a lossless data table. Palette, glyphs, and composition are derived from the source's own content. No pies, donuts, gauges, 3D charts, or fabricated KPIs. Part of the reimagine-it Content-Derived Design suite.
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。
Production guidance for converting sourced data and approved claims into truthful, accessible, deterministic animated visualizations with D3. Use for data-driven charts, maps, networks, hierarchies, transitions, annotations, responsive video variants, frame-by-frame browser rendering, and visualization QA; not for generic dashboards, unsourced decorative charts, or ordinary motion graphics without a data contract.
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。
Provider-independent ecommerce product imagery production for marketplace listings, product pages, hero shots, lifestyle scenes, comparison charts, infographics, variant/packaging images, virtual try-on or placement mockups, ads, and AI-assisted product visuals. Use when an agent must plan, generate, edit, review, localize, package, or QA truthful commercial product images while managing SKU continuity, marketplace requirements, claims, rights, releases, accessibility, and approval.
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。
Provider-independent media quality control and delivery direction for generated images, video, audio, ads, product content, social clips, broadcast and streaming assets, localization packages, and mixed-source edits. Use when an agent must define final export specs, inspect technical and perceptual quality, check captions/audio/color/accessibility/provenance/rights, create delivery sheets, version filenames, checksums, manifests, package deliverables, set acceptance or rejection criteria, and perform final delivery QA before handoff to a platform, broadcaster, streamer, client, or ad trafficker.
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。
Generates 3D conformer ensembles using RDKit ETKDGv3 with knowledge-enhanced distance geometry, MMFF94/UFF force-field optimization, CREST + GFN2-xTB semi-empirical refinement, and macrocycle-aware torsion preferences. Provides explicit decision rules for single vs ensemble conformer use, RMSD pruning, energy windows, conformer count, and force-field choice. Use when preparing 3D ligands for docking, generating descriptor input for 3D QSAR, or sampling macrocycle/peptide conformational ensembles.
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 web pages and content similar to a given URL using Exa's neural similarity search. Ideal for competitor discovery, alternative source finding, related content research, and expanding research beyond known sources.
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, extract, and compare high-quality public sources with Exa through SandBase. Use when asked for deep web research, source discovery, current evidence, topic investigation, company research, or citation-ready findings.
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 and synthesize research papers, whitepapers, PDFs, technical reports, and academic sources with Firecrawl Research, using semantic paper search, related-paper expansion, and in-body verification over Firecrawl's paper index — largely biomedical and life-science literature from PubMed, bioRxiv, and medRxiv, plus arXiv preprints in CS, physics, and math. Use when the user wants a literature review, systematic review, survey of studies, paper summary, research landscape, or sourced synthesis from scholarly and industry publications, including clinical, drug, gene, disease, epidemiology, and public-health topics. Prefer this over a general web-research workflow whenever the evidence base is published papers rather than web pages.
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 Cypress test suites in TypeScript: E2E tests, component tests, custom commands, cy.intercept network control, cy.session login, Cypress Cloud, and CI integration. Covers retry-ability, the command queue, cross-origin flows with cy.origin, and data-driven testing with fixtures. Use when: "write E2E test in Cypress," "Cypress page object / custom command," "cy.," "cy.intercept," "Cypress component test," "Cypress Cloud," "cypress.config.ts." Not for: Playwright suites — use playwright-automation; flaky-test healing or quarantine — use test-reliability; bulk selector regeneration after a UI refactor — use selector-drift-recovery; Selenium-to-Cypress conversion — use test-migration. Related: playwright-automation, ci-cd-integration, visual-testing, unit-testing, test-reliability.
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。
Creates new Source of Truth (SoT) files when existing templates don't fit your needs. Triggers on requests to create a new SoT file, add a new artifact type, or when user says "I need to track [X] but there's no SoT for it", "create SoT", "new source of truth". Outputs a properly structured SoT.*.md file with ID prefix, frontmatter, and update protocol.
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。
Analyze an open-source project from What/Why perspective (not how-it's-implemented). Use when the user says "/analyze-oss", "분석해줘 이 오픈소스", "이 레포 뭐하는거야", "analyze this repo", "what does X do", "이거 왜 쓰는거야", "이 라이브러리 분석", provides a GitHub URL and wants understanding, or asks to deeply understand an OSS project's purpose, value, target users, and usage flow. Clones the repo to ~/opensource-analysis/<repo-name>/ (git pull if already exists), dispatches parallel subagents per analysis lens, then synthesizes a What/Why-focused report in chat. Supports optional user-specific follow-up 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。
知らない分野にキャッチアップするとき、汎用的な言葉から界隈の用語・中心の人・場（学会・雑誌・個人ブログ・動画・リポジトリ・一覧）を特定し、 引用・リンク・著者を芋づる式にたどって、当たりの原文（論文も個人の記事や動画の字幕も）を要約せずそのまま ~/research/<話題>/sources/ に落とす。 周ごとに地図（map.md）を育て、要る主張の全部に原文が付くか飽和したら止める。 見張りのモードでは、地図の場（Qiita・Zenn のタグ、note、YouTube、ブログ、arXiv）の新着を RSS で引いて原文の冒頭つきで並べる。 以下で使用:「芋づる式に調べて」「この分野をキャッチアップしたい」「界隈の用語と人から掘って」 「論文とか先行研究を漁って」「既存の技術を真似したいから元ネタを探して」「上手い人のやり方を集めて」「原文で落として」「/imozuru」 「〇〇って研究あるの？原典ほしい」「全然わからないから流れを掘って」「どういう界隈でどう呼ばれてるか知りたい」 「〇〇の新着見て」「今週の〇〇の記事まとめて」「キャッチアップ」「/imozuru watch」 "dig into this field", "find the canonical papers on X", "literature dive", "what's new in X this week"。 以下には使用しない: 答えが1つの事実の確認（年・有無だけ・仕様1つ → そのまま検索）／ 手元の PC の中の資料を探す／読んだ資料の要約や解説を書く。
Looker Studio (formerly Google Data Studio) expertise. Build dashboards, design data visualizations, connect data sources, and create marketing reports. Use when the user asks about Looker Studio, Data Studio, marketing dashboards, data visualization, report building, or connecting analytics data sources.
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 structured research briefs from RSS feeds and web sources on any topic. Synthesizes multiple sources into actionable analysis with key findings, data points, expert perspectives, and content angles.
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。
Shopify e-commerce platform marketing expertise. Audit store performance, optimize conversion funnels, configure tracking, and integrate marketing tools. Use when the user asks about Shopify, e-commerce optimization, Shopify analytics, store conversion, product feeds, or Shopify app integrations.
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。
Answer questions about the AI SDK and help build AI-powered features. Use when developers: (1) Ask about AI SDK functions like generateText, streamText, ToolLoopAgent, embed, or tools, (2) Want to build AI agents, chatbots, RAG systems, or text generation features, (3) Have questions about AI providers (OpenAI, Anthropic, Google, etc.), streaming, tool calling, structured output, or embeddings, (4) Use React hooks like useChat or useCompletion. Triggers on: "AI SDK", "Vercel AI SDK", "generateText", "streamText", "add AI to my app", "build an agent", "tool calling", "structured output", "useChat".
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Dokploy is an open-source, self-hosted platform (an alternative to Heroku, Vercel and Netlify) that deploys applications, Docker Compose stacks and databases on your own server with Docker Swarm and Traefik, a web dashboard, automatic Let's Encrypt certificates and an HTTP API. Use when a user asks to "install Dokploy on a VPS", "deploy an app with Dokploy", "deploy a docker-compose project to Dokploy", "add a domain with HTTPS", "trigger a Dokploy deployment from CI", "create a Postgres database in Dokploy" or "set up zero-downtime deploys".
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dlt (data load tool) is an open-source Python library that loads data from APIs, files and databases into warehouses and lakes, inferring the schema and tracking incremental state for you. Use when a user asks to build a data pipeline in Python, load a REST API into DuckDB, BigQuery, Snowflake or Postgres, set up incremental or merge loading, enforce a schema contract, or replace a hand-written ETL script or a hosted ELT connector.
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。