1,130 件 ・ 関連度順
概要と使いどころ
Use when working with Algolia — algolia index management, search relevance tuning, analytics review, and configuration optimization. Covers index settings, ranking criteria, faceting configuration, synonyms, rules, API key management, and usage quotas. Read this skill before any Algolia operations.
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 Algolia Deep — advanced Algolia search platform management including indices, search analytics, A/B tests, query rules, synonyms, API key audit, and infrastructure monitoring. Covers search performance metrics, relevance tuning, click-through analysis, indexing health, and usage quota tracking.
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 Anyscale — anyscale platform management covering Ray clusters, services, model deployments, jobs, and usage analytics. Use when monitoring deployed services, analyzing cluster utilization, reviewing job status, managing model endpoints, or troubleshooting Anyscale deployments.
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 Appwrite — appwrite backend platform management covering project inventory, database collections, user authentication stats, storage bucket usage, function deployments, webhook configuration, and platform health. Use for comprehensive Appwrite project assessment and resource monitoring.
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 Gcp Firestore Deep — google Cloud Firestore document and collection analysis, index management, security rules review, usage monitoring, and performance diagnostics via gcloud CLI.
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 AWS API Gateway — analyzing REST or HTTP APIs, debugging latency or 5xx errors, auditing throttling and stage configuration, reviewing usage plans, or checking authorization setup. Covers API inventory, CloudWatch metrics, stage audits, and integration health for both REST (v1) and HTTP (v2) 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。
Use when working with Dynamodb — amazon DynamoDB table analysis, capacity mode evaluation, GSI/LSI usage, item access patterns, and cost optimization.
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 Aws Billing — analyze, break down, and report AWS costs and bills. Covers cost breakdown by service, account, or usage type; monthly/daily billing trends; cost anomaly detection; RI/SP utilization; cost forecasting; credit/discount analysis; and multi-account cost comparison. Uses anti-hallucination rules, mandatory currency/credit detection workflow, and reusable Cost Explorer functions.
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 Firestore — google Cloud Firestore collection analysis, index management, security rules review, usage metrics, and query optimization.
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。
As a heavy Claude Code user, Simon estimates his own usage at the equivalent of 4,400 "typical queries" to an LLM, for an equivalent of around $15-$20
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 for Vercel cost and performance optimization on deployed projects, especially Next.js, SvelteKit, Nuxt, and limited Astro apps. Collect Vercel metrics, usage, project config, and code scan results first; investigate only metric-backed candidates; produce ranked recommendations grounded in verified files and version-aware Vercel/framework docs. Trigger for Vercel bill reduction, slow or expensive routes, caching opportunities, Function Invocations, Build Minutes, Fast Data Transfer, Core Web Vitals, Bot Management, Fluid compute, or cost breakdown 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。
NVIDIA NeMo Retriever deployment and usage guidance for local retrieval services, corpus ingestion, and grounded question-answering 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。
Use this skill when building applications with Gemini API hosted models, including Gemini and Gemma 4, working with multimodal content (text, images, audio, video), implementing function calling, using structured outputs, or needing current model specifications. Covers SDK usage (google-genai for Python, @google/genai for JavaScript/TypeScript, com.google.genai:google-genai for Java, google.golang.org/genai for Go), model selection, and API capabilities.
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。
**MANDATORY prerequisite** — you MUST invoke this skill BEFORE every `create_new_file` tool call. NEVER call `create_new_file` directly without loading this skill first. Trigger whenever the user wants a new blank Figma file — a new design, FigJam, or Slides file — or when you need a fresh file before calling `use_figma`. Usage — /figma-create-new-file [editorType] [fileName] (e.g. /figma-create-new-file figjam My Whiteboard, /figma-create-new-file slides Q3 Review)
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。
Expert en documentation d'agents IA (README, API docs, exemples d'usage, troubleshooting, changelog)
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 improving prompts for any LLM. Applies proven prompt engineering techniques to boost output quality, reduce hallucinations, and cut token usage.
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。
Palette roles, restraint, semantic consistency, and when fewer colors means better design. Use when building color palettes, evaluating color usage, or diagnosing color-related design problems.
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 improving prompts for any LLM. Applies proven prompt engineering techniques to boost output quality, reduce hallucinations, and cut token usage.
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 a concise and structured PR description from commit history with minimal token usage
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。
When addressing slow application endpoints, high database CPU usage, or standardizing data access 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。
Reviews Swift code for concurrency correctness, modern API usage, and common async/await pitfalls. Use when reading, writing, or reviewing Swift concurrency code.
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。
Guidance for using asc cli in this repo (flags, output formats, pagination, auth, and discovery). Use when asked to run or design asc commands or interact with App Store Connect via the CLI.
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 improve SwiftUI runtime performance. Use when diagnosing slow rendering, janky scrolling, high CPU, memory usage, excessive view updates, layout thrash, body evaluation cost, identity churn, view lifetime issues, lazy loading, Instruments profiling guidance, and performance audit 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。
Access research-grade sensor data using SensorKit. Use when reading ambient light levels, accelerometer data, rotation rates, device usage patterns, keyboard metrics, or media events for approved research studies. Requires SensorKit entitlement and research study authorization.
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。