「lms」の検索結果

Coordinate SEO, LLM-readable site architecture, UX, content, monitoring, security, and external authority work as a task-plan-driven sequence. Use this skill when the user asks to plan, execute, validate, or route multi-skill website growth work; asks what to do next across semantic core, URL structure, internal linking, schema, llms.txt, UX journeys, analytics, citation monitoring, or white-hat link placement; or wants a central architect that preserves evidence, alarms, handoffs, and tests.

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Design human-visible, machine-readable website layers for AI assistants, RAG, and agentic search. Use this skill when the user asks for llms.txt, LLM discovery plans, visible TLDR or direct-answer blocks, source trails, entity pages, markdown alternates, AI crawler policy, topic-to-URL maps, assistant-readable public content, or LLM-friendly architecture that must preserve canonical SEO strategy without hidden bot-only facts or duplicate story bodies.

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 credential-free public website monitoring evidence from server access logs, HTTP fetches, robots/llms/sitemap checks, and exported summaries while protecting raw IP and private query data. Use this skill when the user asks whether crawlers or AI bots reached a site, whether public SEO/LLM discovery files respond, or how to separate observed facts from unknown Search Console, rank, analytics, or citation claims.

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。

Select an OCR/HTR engine by benchmarking candidates on the same labelled samples — source ground truth from open datasets (e.g. Hugging Face PAGE-XML corpora) or hand-transcription, wrap every engine behind a transcribe(image_path) -> str adapter (vision-LLMs via any OpenAI-compatible endpoint, dedicated HTR REST APIs, cloud OCR), and score with zero-dependency raw CER, lenient CER, WER, and a critical name/date token diff. Use when choosing an engine for handwriting or historical-document transcription, when published accuracy claims need honest verification on your own material, or when a records workflow must surface confident-but-wrong names and dates that a low aggregate CER hides.

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。

Builds, measures, and improves an Answer Engine Optimization program across ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews, and Copilot. Use for AEO, GEO, LLMO, AI search visibility, AI citations, brand mentions, answer-engine monitoring, llms.txt, AI crawler audits, or questions like 'what does AI say about us?' and 'why are competitors cited instead of us?'. Produces a durable evidence report and prioritized gameplan, not a one-shot content checklist.

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。

Use when the user explicitly asks for the Inngest REST API v2, raw HTTP, OpenAPI, API docs, API authentication, or an endpoint that the Inngest CLI does not expose. Covers api-docs.inngest.com, llms.txt, the OpenAPI v2 spec, Bearer authentication with API keys or signing keys, production and local base URLs, raw curl/fetch requests, request-shape discovery, pagination, secret redaction, and when to prefer the `inngest-api-cli` skill 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。

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

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Use when needing 100% type-safe, schema-valid data extraction from LLMs. Keywords: structured output, JSON schema, Pydantic, Zod, type-safe, constrained decoding, validation, tool use.

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。

Agentic workflow patterns for autonomous LLM reasoning. Use when building ReAct agents, implementing reasoning loops, or creating LLMs that plan and execute multi-step 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。

Fix generic frontend designs by applying aesthetic upgrades across the 5 design dimensions

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 distinctive, production-grade frontends by composing design dimensions and tech-specific implementations

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。

Fix generic React component designs by applying aesthetic upgrades across the 5 design dimensions (typography, color, motion, spatial composition, backgrounds)

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 test scaffolding for modules with proper structure, fixtures, and mock configurations.

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。

Fix generic HTML/CSS designs by applying aesthetic upgrades across the 5 design dimensions (typography, color, motion, spatial composition, backgrounds)

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 project assessment markdown documents from JSON data with WHY/WHO/WHAT sections and GO/NO-GO decision matrix.

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 comprehensive integration tests for component interactions, workflows, and system boundaries. Use for testing API endpoints, database operations, and multi-component flows.

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。

Fix generic Svelte component designs by applying aesthetic upgrades across the 5 design dimensions (typography, color, motion, spatial composition, backgrounds)

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。

Assess identified vulnerabilities for exploitability, impact, and risk. Provide CVSS scoring and remediation strategies. Use when analyzing security 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。

Auto-activates when managing agent-to-agent communication for validation failures and recursive fix cycles between validation specialists and main 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。

Create distinctive, production-grade Vue 3/TypeScript frontends with exceptional design quality

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。

Permission test. Use for testing.

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。

Fix generic Vue component designs by applying aesthetic upgrades across the 5 design dimensions (typography, color, motion, spatial composition, backgrounds)

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。

Verify compliance with OWASP Top 10 2021 security standards. Use when performing OWASP compliance checks and security certification.

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。