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Audit and score blog posts on a 5-category 100-point scoring system covering content quality, SEO optimization, E-E-A-T signals, technical elements, and AI citation readiness. Includes AI content detection (burstiness, phrase flagging, vocabulary diversity). Supports export formats (markdown, JSON, table) and batch analysis with sorting. Generates prioritized recommendations (Critical/High/Medium/Low) with specific fixes. Works with any format (MDX, markdown, HTML, URL). Use when user says "analyze blog", "audit blog", "blog score", "check blog quality", "blog review", "rate this blog", "blog health check".

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。

Audit and score blog posts on a 5-category 100-point scoring system covering content quality, SEO optimization, E-E-A-T signals, technical elements, and AI citation readiness. Includes advisory editorial style diagnostics (sentence-length variation, configured phrase lists, vocabulary sampling) that never infer authorship or affect scoring. Supports export formats (markdown, JSON, table) and batch analysis with sorting. Generates prioritized recommendations (Critical/High/Medium/Low) with specific fixes. Works with any format (MDX, markdown, HTML, URL). Use when user says "analyze blog", "audit blog", "blog score", "check blog quality", "blog review", "rate this blog", "blog health check".

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。

Post-writing SEO validation with pass/fail checklist covering title tag length and keyword placement, meta description quality, heading hierarchy and keyword density, internal/external link audit with anchor text analysis, canonical URL verification, Open Graph meta tags (og:title, og:description, og:image), Twitter Card validation, structured data presence and validity, URL structure optimization, and image alt text presence. Produces prioritized fix list with specific recommendations. Use when user says "seo check", "check seo", "validate seo", "blog seo", "seo validation", "on-page seo", "title tag check", "meta description check", "heading check", "link audit".

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。

AI citation readiness audit as part of SEO, covering classic Google search and AI search surfaces together. Use whenever the user wants their content to rank or be cited in ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com, Google AI Overviews, or Google AI Mode. AI citation optimization audit scoring blog posts for major answer surfaces. Evaluates evidence-backed citability, purpose clarity, entity clarity, structured data, and AI crawler accessibility. Suggests evidence-backed explanations and a 0-100 AI Citation Readiness score. Use when user says "geo", "ai citation", "ai optimization", "citation audit", "aeo", "perplexity optimization", "chatgpt citation".

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 aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks skills.

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。

Runs the loop from discovering a weakness to confirming it is fixed — scanning, triage, prioritization by real exploitability, remediation tracking, and patch policy. Use this to stand up or fix a vulnerability program, triage scanner output, decide what to fix first, set patch SLAs, handle a disclosure from an outside researcher, or report posture to leadership.

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。

Plans and delivers a single project — scope, estimation, scheduling, critical path, tracking, and recovering when it slips. Use this to plan a project, build or challenge a schedule, estimate credibly, track progress meaningfully, or recover a project that is late.

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。

Handles customer situations that have exceeded normal support — severity assessment, incident communication, executive escalation, and recovering a relationship after a failure. Use this when a customer issue is escalating or has gone to leadership, during a customer-affecting outage, when a major account is at risk, when a relationship needs repairing after a failure, or to design the escalation path itself.

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。

Universal UI/UX design principles covering visual hierarchy, interaction laws, typography foundations, and WCAG accessibility requirements. Use when making design decisions not covered by a specific design system, validating principle compliance, or resolving conflicts between aesthetics and accessibility. Design-system-agnostic and applies to every surface.

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。

Extract, define, and apply design DNA across three dimensions: design system (tokens), design style (qualitative feel), and visual effects (Canvas, WebGL, 3D, particles, shaders, scroll effects, etc.). Use this skill when: (1) a user wants to see the full 3-dimension design structure/schema, (2) a user provides images, screenshots, or URLs of reference designs and wants them analyzed into a structured JSON profile covering all three dimensions, (3) a user has a Design DNA JSON and content and wants a design generated from it, or (4) any combination of these phases. Triggers on "design DNA", "extract design style", "analyze design", "design tokens from reference", "generate design from JSON", "design system from screenshot", "design profile", "style guide JSON", "visual effects analysis", "design with effects", "3d design analysis".

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。

Comprehensive PostGIS spatial table design reference covering geometry types, coordinate systems, spatial indexing, and performance patterns for location-based applications

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 general PostgreSQL table design. **Trigger when user asks to:** - Design PostgreSQL tables, schemas, or data models when creating new tables and when modifying existing ones. - Choose data types, constraints, or indexes for PostgreSQL - Create user tables, order tables, reference tables, or JSONB schemas - Understand PostgreSQL best practices for normalization, constraints, or indexing - Design update-heavy, upsert-heavy, or OLTP-style tables **Keywords:** PostgreSQL schema, table design, data types, PRIMARY KEY, FOREIGN KEY, indexes, B-tree, GIN, JSONB, constraints, normalization, identity columns, partitioning, row-level security Comprehensive reference covering data types, indexing strategies, constraints, JSONB patterns, partitioning, and PostgreSQL-specific best practices.

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。

Template loading, variable filling, and scaffolding for all GSD artifacts. Manages 22+ templates covering every document type in the GSD system, from PROJECT.md to milestone archives.

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。

6-agent parallel code review orchestration covering architecture, security, performance, testing, quality, and documentation dimensions with weighted scoring.

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。

System and API design guidance covering component boundaries, data flow, integration patterns, and scalability considerations.

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。

6-phase iterative specification execution workflow covering implementation, testing, review, improvement, commit, and progress tracking with quality-gated convergence.

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。

**IMPORTANT**: Any change to React or Next.js code must read through this skill first. React and Next.js guidelines from Vercel Engineering covering visual instability, layout shifts, CLS, flickering, hydration issues, and font loading.

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。

Performs a structured five-stage code review covering requirements compliance, correctness, code quality, testing, and security/performance. Each stage uses targeted checklists and categorized feedback (Blocker/Major/Minor/Nit) with actionable suggestions and rationale. Use when the user asks for code review, PR feedback, pull request review, or wants their code checked for bugs, style issues, or vulnerabilities — triggered by phrases like "review my code", "check this PR", "review my changes", "pull request review", or "code feedback".

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。

Manages work transitions between team members or agents by creating structured handoff documents, summarizing project status, documenting key decisions, blockers, and open questions, and generating onboarding briefs. Use when someone needs to hand off, hand over, or transition a project; pass work to another person or agent; brief a colleague taking over; prepare a shift change summary; or onboard someone mid-task. Produces ready-to-use handoff documents covering current status, next steps, known issues, technical context, and communication templates for both planned and unplanned transfers.

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。

On-demand RAI planner reference pack covering Phase 1 capture, Phase 2 risk classification and the optional Mural board bootstrap, Phase 5 impact assessment, and Phase 6 review and backlog handoff.

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 PR reference XML with commit history and unified diffs between branches, with extension and path filtering. Use when creating pull request descriptions, preparing code reviews, analyzing branch changes, discovering work items from diffs, or generating structured diff summaries.

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。

Authoring conventions for exploratory data analysis notebooks and analytical dashboards, covering section sequence, visualization selection, scale thresholds, caching and state, and dashboard validation budgets. Use when composing or reviewing an EDA notebook, an analytical dashboard, or a dashboard test pass.

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 evaluation datasets and supporting documentation for AI systems and agents, covering the scoping interview, difficulty distribution, dataset contract, sample review, and metric and tooling selection. Use when building or reviewing an evaluation set for a conversational agent, assistant, or retrieval-grounded AI system.

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 Thinking learning curriculum covering nine progressive modules across the full Problem, Solution, and Implementation Space methods plus a shared manufacturing reference scenario for teaching and practice

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。