「llm」の検索結果

AI 产品经理 (LLM 应用 / 生成式 AI / Agent 产品 / Copilot 嵌入 — 跨模型评估 / 工作流设计 / 产品-工程协同) (AI Product Manager — LLM application / generative AI / agent product 的产品经理实战) Master OS — automated mastery of AI Product Manager — LLM application / generative AI / agent product 的产品经理实战: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on AI Product Manager — LLM application / generative AI / agent product 的产品经理实战 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「AI 产品经理」「AI PM」「LLM 产品」「Generative AI product」「RAG 产品」

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。

Suede-affiliated AI search optimization discipline. Use when the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' 'optimize for Claude/Gemini,' 'llms.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' or 'agent-readable site.' The durable job: make content structurally extractable, citable, and agent-readable so AI systems surface and cite it. NOT FOR: traditional technical SEO audit (use suede-seo-audit) or launch-appeal grading (use suede-visibility-grader).

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 calling LLM APIs from Python code. When connecting to llamafile or local LLM servers. When switching between OpenAI/Anthropic/local providers. When implementing retry/fallback logic for LLM calls. When code imports litellm or uses completion() 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 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。

Test for indirect prompt injection vulnerabilities where malicious instructions are injected through external data sources (websites, emails, documents, database records) that the LLM processes. Use this skill when assessing LLM-integrated applications that process user-generated content, retrieve web pages, parse emails, or read documents. Covers injection via web content, email bodies, user profiles, and database records that are fed to LLM context.

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 the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' 'optimize for Claude/Gemini,' 'llms.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' or 'agent-readable site.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema.

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。

Especialista em Langfuse - plataforma de observabilidade LLM de código aberto. Cobre rastreamento, gerenciamento de prompts, avaliação, datasets e integração com LangChain, LlamaIndex e OpenAI. Essencial para depuração, monitoramento e melhoria de aplicações LLM em produção. Use quando: langfuse, observabilidade llm, rastreamento llm, gerenciamento de prompts, avaliação llm.

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。

任意のアプリのコードベースを解析して LLM API 呼び出し箇所を洗い出し、「1 回の処理(フロー)で どれだけのトークン/費用がかかるか」「N 件・DAU 規模でいくらか」を根拠付きで試算するスキル。 プロンプト/ツール定義/RAG/エージェントループ/キャッシュ/思考トークンまで合算し、無料 or 確認付き 有料の実測でグラウンディングする。成果物は Markdown レポート + 対話 HTML 計算機 + コスト JSON。 「このアプリの API コストはいくら」「モデル別/件数別の費用」「LLM の ROI 試算」「トークン数を数えて」 「1リクエストいくら」等で起動。既定スタックの必須スキルではない optional 群であり、AI 機能を含む要件で明示された場合、 または app-orchestrator が v1 前・AI 機能変更時の試算として指示した場合のみロードする。

Wire Promptfoo and DeepTeam into CI/CD for automated regression red-teaming of LLM apps against OWASP LLM Top 10 and OWASP Agentic presets, failing the build when jailbreak or injection vulnerabilities regress.

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。

Register a custom WooCommerce shipping provider — extend AbstractShippingProvider and add it via the woocommerce_fulfillment_shipping_providers filter (WC 10.1+). Distinct from shipping methods (rate at checkout) — a provider is a carrier identity (UPS, FedEx, DHL, your custom carrier) used by the Fulfillments system for tracking URLs, tracking-number pattern detection, country-pair support, and the orders-list filter. Required methods get_key / get_name / get_icon / get_tracking_url, with optional shipping-from / shipping-to / try_parse_tracking_number. Registration accepts class names (resolved via the DI container) or pre-built instances, keyed by get_key. Use when integrating a private carrier, fulfillment service, or any tracking-aware shipping identity. Triggers on AbstractShippingProvider, woocommerce_fulfillment_shipping_providers, FulfillmentUtils::get_shipping_providers, "shipping provider" in WooCommerce context, or carrier tracking 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。

自動メトリクス、人間によるフィードバック、およびベンチマークを使用して、LLMアプリケーションの包括的な評価戦略を実装します。LLMのパフォーマンスをテストし、AIアプリケーションの品質を測定し、または評価フレームワークを確立する場合に使用します。

Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.

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 in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.

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。

Diffie-Hellman key exchange ve DLP saldırıları — Pohlig-Hellman, Pollard rho/lambda, BSGS, small subgroup confinement, weak parameter detection

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。

Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.

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。

構造化テキストの解析において正規表現とLLMのどちらを選択するかの判断フレームワーク — まず正規表現で始め、低信頼度のエッジケースにのみLLMを追加する。

Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.

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 in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.

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。

vLLM: high-throughput LLM serving, OpenAI API, quantization.

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 an llms.txt file from scratch based on repository structure following the llms.txt specification at https://llmstxt.org/

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。

Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.

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。

Unified LLM torch-profiler triage skill for `sglang`, `vllm`, `TensorRT-LLM`, and `TokenSpeed`. Use it to inspect an existing `trace.json(.gz)` or profile directory, or to drive live profiling against a running server when supported and return one three-table report with kernel, overlap-opportunity, and fuse-pattern tables.

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。

Detects prompt injection using regex signature matching, heuristic scoring for structural anomalies, and DeBERTa-based transformer classification, flagging direct injections (system-prompt overrides, role-play escapes) and indirect injections (encoded payloads, obfuscation) per OWASP LLM Top 10 (LLM01:2025). Use for input validation layers in chatbots/agents/RAG pipelines, or for retrospectively classifying injection attempts in logs or incident investigations.

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。

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

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。