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Store and retrieve unstructured objects, file uploads, and cache-like state on Netlify using the @netlify/blobs key/value API from Functions, Edge Functions, and Build Plugins. Use when a task involves saving user file or image uploads, persisting form or contact-form submissions, storing generated output from Background Functions (sitemaps/processed media/bulk-email results), building read-only asset stores, adding client-side blob expiration, or wiring file-based blob uploads at deploy time. Not for per-user, transactional, or relational data (counters/balances/sessions) — reach for Netlify DB there instead.

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 tasks involve reading, creating, or reviewing PDF files where rendering and layout matter; prefer visual checks by rendering pages (Poppler) and use Python tools such as `reportlab`, `pdfplumber`, and `pypdf` for generation and extraction.

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。

Draft, prepare, publish, and verify personal or project LinkedIn posts through the user's signed-in Chrome session, including audience review, public-safe media uploads, links, action-time confirmation, and post-publication checks.

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。

Next.js 16.2.4 with TypeScript — App Router, Server Components, use cache directive, Turbopack dev, Server Actions, ISR, SSR, SSG, MCP devtools, metadata API, route handlers, instrumentation.

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 task requires automating a real browser from the terminal (navigation, form filling, snapshots, screenshots, data extraction, UI-flow debugging) via `playwright-cli` or the bundled wrapper script.

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 implement context-fit frontend interfaces with deliberate art direction, accessible interaction, responsive behavior, complete states, and rendered verification. Use when creating or substantially reworking pages, components, product workspaces, dashboards, marketing sites, editorial surfaces, commerce flows, or justified immersive experiences.

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。

Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are about to call `iam:CreateRole`, or when an AccessDenied error mentions an IAM action. Never blindly call `iam:CreateRole` — always check for existing roles first. This skill prevents the most common SageMaker deployment failure: trying to create IAM resources from an SSO principal that has no IAM write permissions.

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。

Azure deployment for web apps — Static Web Apps, App Service, Blob Storage, Bicep/ARM, GitHub Actions CI/CD. Use when deploying Next.js/Vite to Azure or configuring Azure resources for full-stack apps.

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。

Git operations, shell scripting, CI/CD pipelines, and terminal automation. Use for conventional commits, PowerShell/Bash scripting, configuring GitHub Actions, or automating development tooling 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。

Deploy applications and websites to Vercel. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live", or "create a preview 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。

Copywriter IA — Expert en rédaction persuasive (landing pages, emails, ads, framework AIDA, psychologie de la persuasion).

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。

Backend Rust v2 — Actix, Axum, tokio, zero-cost abstractions, performance

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。

Backend Rust Specialist IA — Expert en développement backend Rust (Actix, Axum, Tokio, zero-cost abstractions, WASM)

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。

Guides creation and validation of custom dotnet new templates. Generates templates from existing projects and validates template.json for authoring issues. USE FOR: creating a reusable dotnet new template from an existing project, validating template.json files for schema compliance and parameter issues, bootstrapping .template.config/template.json with correct identity, shortName, parameters, and post-actions, packaging templates as NuGet packages for distribution. DO NOT USE FOR: finding or using existing templates (use template-discovery and template-instantiation), MSBuild project file issues unrelated to template authoring, NuGet package publishing (only template packaging structure).

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。

Validates custom dotnet new templates for correctness before publishing. Catches missing fields, parameter bugs, shortName conflicts, constraint issues, and common authoring mistakes that cause templates to fail silently. USE FOR: checking template.json files for errors before publishing or testing, diagnosing why a template doesn't appear after installation, reviewing template parameter definitions for type mismatches and missing defaults, finding shortName conflicts with dotnet CLI commands, validating post-action and constraint configuration. DO NOT USE FOR: finding or using existing templates (use template-discovery), creating projects from templates (use template-instantiation), creating templates from existing projects (use template-authoring).

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。

GitHub Actions, GitLab CI, Jenkins, and automated deployment 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。

Codified expertise for quality control, non-conformance investigation, root cause analysis, corrective action, and supplier quality management in regulated manufacturing.

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。

Improve AI application with evaluation-driven development. Define eval criteria, instrument the application, build golden datasets, observe and evaluate application runs, analyze results, and produce a concrete action plan for improvements. ALWAYS USE THIS SKILL when the user asks to set up QA, add tests, add evals, evaluate, benchmark, fix wrong behaviors, improve quality, or do quality assurance for any Python project that calls an LLM model.

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。

Decomposes a large implementation plan into small-model-sized tasks executed by supervised worker agents (e.g. Haiku) in dependency-ordered parallel waves, with a plan-first approval gate inside every task, file-based state for context compaction, and a hard escalation ladder. Use when a master plan or multi-workstream feature is too large for one session and should be executed cheaply by many small agents under frontier-model supervision.

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。

Solves UI lock-in. Reverse engineers your existing UI, strips all layout/token decisions, preserves every hook, handler, and API call untouched, then rebuilds the UI from scratch with a fresh design system. Use /ui-restructure to fully redesign, --style apple|linear|minimal|dashboard for named styles, --mode layout|theme|grid for partial rebuilds, --god-mode to redesign from the user's perspective using 100-user mindset simulation and 6 UX principles, --keep-tokens to preserve your token system, --prompt for custom redesigns. Applies mandatory world-class polish: spring motion, 5-state micro-interactions, 8pt spacing grid, WCAG accessibility. Supports Next.js, React, Vue 3, Tailwind, CSS Modules, styled-components, shadcn.

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。

Alternative Slowbar coffee intelligence — scientific analysis from seed to cup using First Principle + System Thinking. Covers: varieties, terroir, cultivation, disease diagnosis, processing, roasting physics/chemistry, extraction science, brewing, business strategy, storytelling. ALWAYS trigger for coffee-related questions requiring scientific depth. DO NOT trigger for simple recipes or general food questions. Style: science-driven, farmer-first, quality-obsessed.

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。

Study a merged or shipped tdmcp implementation and extract reusable learnings: code improvements, runtime/UX lessons, test gaps, docs updates, roadmap items, and harness changes. Use when the user asks to learn from a completed feature/project/PR/build, analyze what can be improved from an implementation, or turn a real installation experience into actionable tdmcp improvements. This is a post-implementation learning harness; it does not replace tdmcp-pipeline for building or tdmcp-quality-audit for broad repo audits.

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。

Web scraping inteligente multi-estrategia. Extrai dados estruturados de paginas web (tabelas, listas, precos). Paginacao, monitoramento e export CSV/JSON.

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。

Behavioral UX specialist applying choice architecture, friction audits, and commitment design to guide users toward intended actions without coercion. Use when optimizing conversion flows, onboarding, or checkout UX.

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。