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概要と使いどころ
Design, create, inspect, update, attach, detach, preview, execute, and verify reusable Vapi Structured Outputs through public API or Server SDK workflows. Use for post-call extraction, typed call artifacts, AI-versus-regex extraction, JSON Schema design, backfilling existing calls, or retrieving structured results programmatically.
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。
PostgreSQL database patterns for query optimization, schema design, indexing, and security. Based on Supabase 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。
Database migration best practices for schema changes, data migrations, rollbacks, and zero-downtime deployments across PostgreSQL, MySQL, and common ORMs (Prisma, Drizzle, Kysely, Django, TypeORM, golang-migrate).
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。
Choose the right serialization format for .NET applications. Prefer schema-based formats (Protobuf, MessagePack) over reflection-based (Newtonsoft.Json). Use System.Text.Json with AOT source generators for JSON scenarios.
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。
Classifies raw pilot request → structured JSON {task_type, class, artifact, budget_estimate, confidence, routing_tag, resolution_path, schema_version, result_type, expected_result_kind, result_kind_resolution, hypothesis_relation}. Keyword-fast (<200ms) or Haiku fallback (<60s). Does NOT create WP or call executor.
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 content generation with OpenAI and Claude, callAIWithPrompt usage, prompt storage in app_settings, structured outputs, response format validation, multi-criteria scoring, rate limiting, JSON schema, and AI API best practices. Use when generating content, creating prompts, scoring articles, or working with OpenAI/Claude APIs.
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。
Automatically applies when designing multi-agent systems. Ensures proper tool schema design with Pydantic, agent state management, error handling for tool execution, and orchestration patterns.
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 or implement MotherDuck analytical schemas and transformation models, including grain, types, and materialization.
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。
Run SQL against Google BigQuery and browse its catalog — submit queries (sync or async), poll job status, page through results, list datasets/tables, and read table schemas. Use this whenever the user wants to query a BigQuery table, ask "what's in this dataset", check a BigQuery job's status, or mentions bigquery.googleapis.com or a `project.dataset.table` path. Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.
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 a diffusers pipeline from a terminal (one-off generation, batch jobs, smoke-testing a new model), run on HF Sandbox hardware via `--remote`, introspect a pipeline's input schema before calling it, or attach a LoRA at inference time. Prefer this over writing ad-hoc Python scripts for generation 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。
The branded entry point to ServiceGraph — use whenever the user explicitly names **ServiceGraph** — "use ServiceGraph to…", "what datasets does ServiceGraph have", "search ServiceGraph for…", "look this up in ServiceGraph", "pull contacts from ServiceGraph for these domains", "how many credits do I have on ServiceGraph". ServiceGraph is a multi-dataset platform of metrics-enriched business data for founders — where to launch, who to email, who to hire. This skill explains how to drive the API (api.servicegraph.co / mcp.servicegraph.co) against ANY dataset — discover what datasets exist, discover a dataset's schema and filters, search free brief rows, and unlock contact + metric detail with credits. Dataset-agnostic by design — it discovers everything through the API and never assumes which datasets or fields exist. When the user describes an intent WITHOUT naming ServiceGraph (e.g. "find a PR agency in NY"), defer to the matching specific skill (find-pr-agency, find-marketing-agency, find-law-firm, …); this skill is for explicit ServiceGraph requests and for datasets no specific skill covers yet. Skip non-US firms, consumer/personal services, and individual freelancers.
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。
Activate Specification Know-How: load the SpecBook format contract, the SpecBook schema configuration of the project, and the list of specification (SPEC) artifacts into the context, so the specification can be read, queried, explained, and edited ad-hoc in plain conversation. Use *automatically* whenever the user wants to work with the specification files -- asks what the "spec" or "specification" says, wants to "look up", "query", "check", or "explain" specification content, or wants a small ad-hoc change to it -- and no dedicated specification skill (`ase-spec-edit`, `ase-sync-import`, `ase-sync-reconcile`, `ase-sync-export`) is invoked, as those activate the know-how implicitly.
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 designing UI with OpenPencil — creating layouts via op CLI, batch design DSL, sandboxed JS scripts, or MCP tools. Covers PenNode schema, semantic roles, typography, color, spacing, and common component patterns.
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 SEO and AEO optimized blog posts from source content. Supports feature announcements, product launches, thought leadership, cookbooks/tutorials, report conversions, and news analysis. Outputs complete posts with TLDR, FAQ, schema markup, and internal linking recommendations.
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。
Transform any research material into SEO+GEO-optimized blog posts for Knox Hub. Takes any input (URL, article text, notes, video transcript, competitor post, topic idea) and rewrites it as natural human-sounding content, then fills in all fields for the Knox Hub post editor: title, excerpt, SEO settings, FAQ schema, table of contents, sticky sidebar, and full article content in Markdown. Output is ready to copy-paste into your blog/CMS post editor. Use when asked to "write a post", "rewrite this", "сделай пост", "напиши статью", "перепиши это для блога", "create blog post", or any content creation task.
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。
Icarus Figures produces publication-grade figures for scientific papers — reproducible matplotlib/seaborn data figures and original TikZ method/framework/architecture figures, held to a four-axis quality bar (depth · elegance · unimpeachable · visible-gap) and exported as editable PDF+PNG+SVG with a runnable critique gate. Use this whenever a figure is destined for a paper, journal, conference submission, thesis, or report — making a plot/chart publication- or journal-ready, building a method/architecture/framework diagram or 技术路线图 (the hero figure a paper opens with), a schematic or workflow figure, or cleaning up a matplotlib/seaborn/TikZ figure that currently looks like a homework plot. Trigger even when the user never says "publication": if the figure goes into a written scientific document, this skill applies.
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 an AEO-first topical map optimized for AI search citation — entity clusters, query fan-out coverage, information gain scoring, and multi-platform distribution. Produces entity map, content node architecture, schema blueprint, and 90-day publishing calendar. Use when "topical map", "content architecture", "site structure", "topic clusters", "pillar content plan", "AEO map", "AI search architecture", "entity map", "what content should we build", or any request to plan a site's topical structure for AI search visibility.
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。
One-click technical SEO audit of a website. Runs the full technical SEO audit SOP — crawlability, indexation, Core Web Vitals, schema markup, internal linking, mobile UX, and content quality. Outputs a prioritized fix list. Use when "SEO audit", "technical SEO", "site audit", "crawl issues", "indexation problems", "why aren't we ranking", "SEO health check", or any request to diagnose SEO issues on a website.
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 structured content brief using the marketing harness brief schema. Selects persona, defines angle, sets quality targets. Use when "create a brief", "content brief", "plan this content", "brief for [topic]", "what persona should I use", or before any content creation to define the strategy. Outputs a brief that /kai-write and /kai-email-system consume.
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 高教社杯/CUMCM 数学建模任务需要变量、假设、公式、约束、算法、验证设计、鲁棒性规划、灵敏度分析、结果 schema，或基于题面和本地证据的代码交接。
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。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for WebSocket and SSE handshakes, auth material, subscription state, realtime message schemas, reconnect behavior, and frame-driven runtime effects. Use when the user asks to inspect a WebSocket or SSE handshake, decode frames, trace subscriptions, follow reconnect logic, inspect auth material sent during realtime setup, or explain how live frames change rendered or persisted state. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
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, refine, validate, and render diagrams from natural language, notes, code snippets, schemas, tables, or existing diagram source. use for flowcharts, swimlanes, sequence diagrams, state diagrams, er diagrams, class diagrams, architecture/c4-style diagrams, dependency graphs, gantt charts, mind maps, user journeys, sankey-style flows, org charts, network graphs, and other visual models. supports mermaid by default, graphviz dot for complex graph layout, plantuml for uml-heavy engineering diagrams, and svg output when direct markup is more reliable.
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。
实现表单 / 校验 / 提交 / 错误处理时使用。覆盖 React Hook Form + Zod / TanStack Form / Formik / VeeValidate / Formily / Angular Forms。融合受控/非受控 + Schema 校验 + 异步校验 + 防重复提交。
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。
端到端功能交付时使用。适用于从 PRD 到部署的一人闭环：Schema → API → UI → 联调 → 测试 → 部署。融合全栈交付清单 + 垂直切片 + 增量交付。
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。