1,165 件 ・ 関連度順
概要と使いどころ
Use when working with Bunny Cdn — bunny.net CDN management covering pull zones, storage zones, edge rules, cache configuration, and bandwidth analytics. Use when managing Bunny CDN pull zones, analyzing traffic patterns, configuring edge rules, monitoring cache hit ratios, or managing Bunny storage for origin content.
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Use when working with Azure Cdn — azure CDN management covering profiles, endpoints, custom domains, caching rules, origin groups, and performance analytics. Supports Microsoft, Verizon, and Akamai CDN providers within Azure. Use when managing Azure CDN endpoints, analyzing cache performance, configuring delivery rules, or troubleshooting content delivery.
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Expert en performance des agents IA (optimisation latence, streaming, caching, traitement par lots)
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 optimisation des coûts d'agents IA (sélection modèle, caching, compression prompts, budgeting tokens)
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 improving read performance and reducing database load.
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。
Glibc heap exploitation — tcache poisoning, unsorted bin leak, IO_FILE FSOP
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。
HTTP Request Smuggling — frontend/backend HTTP parser uyumsuzluğunu sömürerek auth bypass, cache poisoning, internal endpoint erişimi
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。
iOS ビルドキャッシュクリア・シミュレータ管理を XcodeBuildMCP 経由で実行。「clean」「DerivedData」「cache」「キャッシュ」「クリーン」「simulator boot」「simulator reset」「シミュレータ起動」「シミュレータリセット」などのキーワードで自動適用。
Analyze Chromium-based browser artifacts using Hindsight to extract browsing history, downloads, cookies, cached content, autofill data, saved passwords, and browser extensions from Chrome, Edge, Brave, and Opera for forensic investigation.
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Performance optimization, profiling, caching strategies, lazy loading, bundle optimization, and monitoring. Use when optimizing application performance, reducing bundle size, implementing caching, or analyzing bottlenecks.
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Explore and diagnose a PostHog endpoint's execution logs — error messages, failed runs, cache misses, slow runs, or unexpected row counts during endpoint invocations. Use when the user says "my endpoint is failing", "show me the logs for endpoint X", "what error did endpoint Y produce", "why did endpoint Z return no rows", "is this endpoint hitting cache", or "check the last N runs". Focused on a single named endpoint's runtime log entries, not project-wide auditing or query performance profiling.
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Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).
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Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.
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Authors reproducible Nextflow DSL2 pipelines built on reactive dataflow, where processes communicate only through channels and execution order is not guaranteed. Use when deciding channel/dataflow (Nextflow) vs rule-based (Snakemake) authoring; wiring queue vs value channels and fixing shared-reference exhaustion with .first(); composing DSL2 modules and subworkflows with take/main/emit; selecting container/conda profiles and pinning images by digest for portability across local/SLURM/LSF/AWS Batch/Google Batch/Kubernetes executors; diagnosing why -resume misses the cache (nondeterministic input order, mtime on network filesystems, mutable :latest tags) with cache 'lenient' and -dump-hashes; managing work/ vs publishDir and dynamic retry escalation; and choosing whether to adopt an nf-core community pipeline or author from scratch.
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Use when you need to design, review, or improve REST APIs with Quarkus REST (Jakarta REST) — including resource classes, HTTP methods, status codes, request/response DTOs, Bean Validation, exception mappers, optional runtime OpenAPI exposure (SmallRye), contract-first generation from OpenAPI, content negotiation, pagination, sorting and filtering, API versioning, idempotency (Idempotency-Key), optimistic concurrency (ETag / If-Match), HTTP caching (Cache-Control), API deprecation (Sunset / Deprecation headers), RFC 7807 Problem Details, ISO-8601 for time in contracts, and security-aware boundaries. This should trigger for requests such as Review or improve JAX-RS resources in a Quarkus project; Design HTTP APIs with validation and error handling on Quarkus; Add API versioning, idempotency, ETag concurrency, or deprecation headers; Implement pagination, sorting, or RFC 7807 Problem Details error responses. Part of cursor-rules-java project
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Windows アプリ開発 (DirectX / WinRT メディア) のグラフィックス・メディア API リファレンス。 Direct2D (ID2D1Factory, ID2D1DeviceContext, ID2D1Bitmap1, ID2D1Effect, ID2D1PathGeometry), DirectWrite (IDWriteFactory, IDWriteTextLayout, DWriteCore), Direct3D 11/12 (D3D11CreateDevice, DXGI SwapChain, HLSL, D3D12 PipelineState), Microsoft.UI.Composition (Compositor, Visual, ExpressionAnimation, InteractionTracker, MicaController), カメラ・画面キャプチャ (MediaCapture, MediaFrameReader, GraphicsCaptureSession, Windows Studio Effects)、音声・動画再生 (MediaPlayer, MediaPlaybackSession, AudioGraph, MediaTranscoder, AdaptiveMediaSource)。
Windows アプリ開発 (Windows App SDK / WinRT) のファイル・設定・データ保存リファレンス。 Windows.Storage の StorageFile, StorageFolder, IStorageItem, FileIO, PathIO, CachedFileManager, DataReader, DataWriter。ApplicationData, ApplicationDataContainer, ApplicationDataCompositeValue によるローカル・ローミング設定。 FileOpenPicker, FileSavePicker, FolderPicker, StorageApplicationPermissions, KnownFolders によるファイルアクセスと権限。JsonObject, JsonArray, XmlDocument, SQLite, EF Core によるシリアライズと永続化。
Turborepo (高速モノレポビルドシステム) リファレンス。 turbo.json、turbo run、タスク依存 (dependsOn)、キャッシュ (local / remote)、 workspaces (pnpm / npm / yarn / bun)、--filter、Remote Cache (Vercel)、 parallel 実行、turbo gen (コード生成)、watch モード、 エラーメッセージ診断 (recursive turbo invocations, missing root task, invalid env prefix)。
OpenAI API (developers.openai.com) の Responses API リファレンス。 text generation, embeddings, structured outputs, streaming, function calling, programmatic tool calling, conversation state, prompt caching, reasoning models, background mode, compaction, multi-agent。 webhooks, error codes, rate limits, Realtime API server controls。 公式 SDK (Python / JavaScript(JS) / .NET / Java / Go / Ruby), openai CLI。 Chat Completions / Assistants からの移行, deprecations。
OpenAI API (developers.openai.com) の運用・管理リファレンス。 Administration API, admin API keys, RBAC, roles, groups, invites, projects, spend limits, audit logs, usage / costs API, Terraform provider (openai/openai), workload identity federation (WIF, OIDC/SPIFFE), safety best practices, moderation API, production best practices, deployment checklist, cost / latency optimization, fast mode, Realtime API costs。rate limits・error handling・prompt caching は openai-api-core、Codex のワークスペース 管理は openai-codex が担当。
Hono (軽量・マルチランタイム対応 Web フレームワーク) リファレンス。 Hono App, Context (c.req / c.json / c.text / c.html), HonoRequest, Routing, HTTPException、 Built-in Middleware (JWT, CORS, CSRF, Cache, Logger, Compress, Basic / Bearer Auth)、 Helpers (Cookie, Streaming, WebSocket, Factory, RPC)、 Cloudflare Workers / Deno / Bun / Node.js / AWS Lambda 対応。
Next.js App Router 専用 API リファレンス（Pages Router / getStaticProps 等は非対応）。 Server Components, Server Actions, layout/page/loading/error ファイル規約, dynamic routes, parallel/intercepting routes, Metadata API (generateMetadata), use cache, revalidatePath/revalidateTag, next/image・next/font・next/link, cookies/headers, useRouter, next.config.js, proxy（旧 middleware）。 プラットフォーム実行基盤・CDN・ISR インフラは vercel スキル参照。
Claude API (platform.claude.com) の Messages API リファレンス。 streaming (SSE), thinking (adaptive) / effort, prompt caching, context editing, compaction, structured outputs, Message Batches, token counting, vision / PDF, Files API, citations, embeddings (Voyage AI)。 errors, rate limits, beta headers, Amazon Bedrock / Vertex AI / Foundry 経由アクセスを含む。
Use when implementing caching, session storage, rate limiting, or any Redis integration. Covers cache-aside pattern, key naming, TTL strategy, and serialization config.
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。