1,824 件 ・ 関連度順
概要と使いどころ
Use when the user says 'build me an app', 'create a project from this spec', 'scaffold a new repo', 'generate a starter', 'turn this idea into code', 'bootstrap a project', 'I have requirements and need a codebase', or provides a natural-language project specification and expects a complete, runnable repository. Stack-agnostic: Next.js, FastAPI, Rails, Go, Rust, Flutter, and more.
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 Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.
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 manage dependencies across multi-language projects. Identifies vulnerabilities, license conflicts, transitive dependency risks, and safe-upgrade paths. Use when auditing third-party packages before release, investigating a CVE, planning a major version bump, or running a license-compliance review. Examples: 'audit our npm dependencies', 'do we have GPL contamination', 'plan the upgrade to React 19'.
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。
Mentor Swedish language learners by selecting YouTube video clips and podcast episodes by CEFR level and skill (listening, reading, writing, speaking), and building a simple learning path. Use when the user asks about a Swedish learning path, YouTube clips or podcasts for Swedish, SFI videos, level assessment for svenska, or requests for Peter SFI / Lätt Svenska med Oskar / Radio Sweden på lätt svenska / Klartext-style 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。
Generate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation. Use when writing SQL, building data reports, exploring databases, or translating business questions into queries.
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。
Downloads YouTube video transcripts/subtitles and cover images by URL or video ID. Supports multiple languages, translation, chapters, and speaker identification. Caches raw data for fast re-formatting. Use when user asks to "get YouTube transcript", "download subtitles", "get captions", "YouTube字幕", "YouTube封面", "视频封面", "video thumbnail", "video cover image", or provides a YouTube URL and wants the transcript/subtitle text or cover image extracted.
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。
Player-facing patch notes from git history and changelogs. Translates developer language into player communication.
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。
Explain the last message, or a supplied file, link, or passage, in plain language a person follows on the first read. Use when the user invokes wtf because they did not follow something. Use ce-explain to investigate how or why code works, and ce-noslop to rewrite prose.
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。
Clean and finalize authorized natural-language text intended for readers by auditing suspicious invisible Unicode and rewriting prose while preserving facts, meaning, and the writer's voice. Use when the user asks to clean, humanize, polish, or finalize articles, manuscripts, reports, documentation, emails, product copy, UI text, Markdown, or HTML prose, or when a project rule or instruction file explicitly requires this workflow. Don't use for code-only tasks or undisclosed authorship evasion; leave code, commands, identifiers, paths, APIs, formulas, citations, required disclosures, and verbatim quotations unchanged.
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。
Turn a rough bug report, feature request, support note, or pull request into a short, plain-language issue focused on the problem and desired behavior. Use when a contributor asks to simplify an issue, explain what a PR is for, create the corresponding issue for a PR, remove implementation detail from a report, or invokes /simple-issue-description.
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 adding user-facing text to Handsontable, creating new language constants, updating translation files, or working with RTL layouts and internationalization
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 a Gemini LiveAPI client service class in the user's chosen programming language. Use when the user wants to build, scaffold, or integrate a client that connects to the Gemini Enterprise LiveAPI websocket endpoint, handles session setup/resumption, bearer token refresh, and sending/receiving `ClientMessage`/`ServerMessage` protos. Don't use for general (non-live, non-bidirectional) Gemini API usage such as one-shot `generateContent`, embeddings, image/video generation, or fine-tuning — use the `gemini-api` skill for those.
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 natural language questions and perform analysis on GKE cluster and workload costs using BigQuery billing exports, cost allocation data, and live cluster monitoring metrics. Use when querying GKE costs across projects, namespaces, or workloads, analyzing billing reports in BigQuery (`bq`), checking cluster cost budgets (`gcloud billing`), or diagnosing cost drivers like pod requests vs. actual utilization (`kubectl top`). Don't use for applying cost optimization changes, creating rightsizing manifests (VPA/MPA), or selecting ComputeClasses (use gke-cost-optimization 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。
Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.
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 developers through downloading, configuring, and installing the official open-source Google Ads MCP Server. Use this skill when a user wants to connect their AI assistant (such as Gemini, Claude Code, or Cursor) to their Google Ads account to query campaigns or retrieve reporting metrics using natural language.
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。
Weekly metrics reporting loop. Queries the metrics tables in Postgres read-only, compares this week against recent weeks, writes plain-language commentary on what moved and why, and posts one report to {{report_channel}}.
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。
Reusable-session release-notes runbook for {{target_repo}}. Finds the range since the last release covered, reads every PR merged in it, groups them by area, writes plain-language notes in the house voice, and opens a PR against {{changelog_path}} only when a new release has actually shipped.
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。
Export raw OpenTelemetry traces from an AI application to Confident AI's Observatory. TRIGGER when the user wants to send OpenTelemetry or OTLP traces/spans from an LLM app, agent, RAG pipeline, or chatbot to Confident AI; configure the Confident AI OTLP endpoint; set confident.span.* or confident.trace.* attributes; export AI-app traces without the deepeval Python package; wire an OTLPSpanExporter, OpenTelemetry Collector, or vendor-neutral OTel SDK to Confident AI; or pick the US vs EU OTLP endpoint. Language-agnostic: the mechanism is OTLP attribute keys plus an exporter endpoint. DO NOT TRIGGER for building DeepEval pytest eval suites, datasets, goldens, metrics, or deepeval test run (use the `deepeval` skill); for instrumenting with the DeepEval SDK's @observe decorator or framework integrations (use the `deepeval-tracing` skill); or for non-AI software such as web servers, CRUD backends, or infrastructure: the confident.* attributes describe AI components only.
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 working with iii SDK APIs across Node.js, browser, Python, or Rust: package installation, worker initialization, function/trigger registration, invocation, channels, logging, OpenTelemetry, and language-specific caveats.
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。
This skill should be used when the user asks to "share memory between agents", "KV cache compaction for multi-agent", "orchestrator worker context", "latent briefing", "reduce worker tokens", "cross-agent memory without summarization", or discusses Attention Matching compaction, recursive language models with workers, or token explosion in hierarchical agents.
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。
LLM Agent 多语言注入规范。在修改 Agent 提示词、添加新的 Agent 端点、处理用户可见的后端消息（message_code）时使用。
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。
Automate time tracking workflows in Clockify -- create and manage time entries, workspaces, and users through natural language commands.
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。
Automate Cloudinary media management including folder organization, upload presets, asset lookup, transformations, and usage monitoring through natural language commands
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。
Automate Capsule CRM operations -- manage contacts (parties), run structured filter queries, track tasks and projects, log entries, and handle organizations -- using natural language through the Composio MCP integration.
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。