2,869 件 ・ 関連度順
概要と使いどころ
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。
Use when adding a new docs section or product area, editing llms.ts / the llms.txt or llms/[...slug] / llms-full.txt routes / get-llm-text / skill.md / .well-known endpoints, or working on the "agent score", "llms.txt", or anything "agent-ready" in the docs and site apps. Explains the invariants the Mintlify agent-readiness audit measures and how to hold them.
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 基础设施 (LLM agent infra) Master OS — automated mastery of LLM agent infra: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on LLM agent infra problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「agent framework」「LLM agent」「agent infra」「multi-agent orchestration」「agent runtime」
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。
Serve LLMs com alta throughput usando PagedAttention do vLLM e continuous batching. Use ao fazer deploy de APIs LLM em produção, otimizar latência/throughput de inferência, ou servir modelos com memória GPU limitada. Suporta endpoints compatíveis com OpenAI, quantização (GPTQ/AWQ/FP8) e 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。
アプリの本番品質を保証する統合スキル。セキュリティ監査・例外/エラー処理設計・攻撃別対処・ サーバーコスト最適化・パフォーマンス最適化を1つのゲートで検査する。 「セキュリティチェックして」「リリースしていい？」「本番公開前の確認」「脆弱性診断」「監査して」 「エラー処理を設計して」「例外処理を入れて」「攻撃対策」「レート制限」「コストを抑えたい」 「無料枠に収めたい」「パフォーマンス改善」「重い」などの文脈で必ず使用する。 また、認証・ユーザー入力・API・決済・機密データを扱うコードを書いた直後、およびデプロイ前には ユーザーが明示しなくても本スキルの該当セクションを適用する。 デプロイ手順自体は Skill cloudflare-secure-deploy、認証実装は Skill better-auth-google-gate、 LLMコストは Skill llm-api-integration / llm-cost-simulator を併用する。
Busabase-backed App-in-Skill dashboard for a shared LLM gateway's cost and model governance — daily spend trend, cost breakdown by service/model, a canary-rollout status board, and a deterministic cost/error anomaly list. Use when the user invokes $kelly-llm-gateway or /kelly-llm-gateway, or wants to review LLM gateway spend, model routing, canary rollouts, rollback readiness, or cost/error anomalies for services routed through one shared gateway to multiple LLM providers/models. Human actions (promote/rollback/hold a rollout, acknowledge an anomaly) write directly onto the route's own Busabase record — this skill never changes a real routing 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。
AIエージェントの不調を、指示・記憶・ツール実行・画面表示など12の層から調べるスキル。コードやログを根拠に原因を整理し、重要度順の指摘と修正案をまとめます。
LLMの出力をJSONや正規表現、Pydanticの型定義に合わせて生成するスキル。文章からの情報抽出や分類結果を、後続の処理で扱いやすい形式に整えます。
重みが公開されたLLMの回答拒否に関わる仕組みを分析し、再学習なしで重みを変更する実験を支援。計算環境に合うモデルや手法の選択、変更前後の品質評価まで扱います。
Detect and defend against indirect prompt injection hidden in web pages, documents, and images consumed by an agent, via content extraction (HTML/PDF/OCR), normalization, and scanning with LLM Guard's PromptInjection scanner or Hugging Face Prompt Guard 2. Use when an agent ingests untrusted external content and you need to screen it for injected instructions before the LLM processes it.
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。
Wires Promptfoo and DeepTeam into CI/CD for automated, repeatable red-teaming of LLM apps against OWASP LLM Top 10, OWASP Agentic, and MITRE ATLAS presets, failing the build when jailbreak or injection vulnerabilities regress. Use for continuous adversarial testing in CI/CD, a merge-blocking security gate, or comparing model/prompt versions for compliance reporting.
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。
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm 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。
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。
Stand up vLLM or SGLang serving on Jetson, using upstream vLLM on Thor and Orin JetPack 7.2+, and NVIDIA-AI-IOT vLLM on older Orin.
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。
Build and maintain a structured LLM-generated wiki for any codebase. Use when the user asks to analyze/understand/document a codebase, build a code wiki, create project documentation from source, or update an existing .llm-wiki. Triggers on phrases like "build wiki", "analyze this codebase", "document this project", "update wiki", "llm-wiki", or when entering an unfamiliar project that has no .llm-wiki yet.
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 and AI application security testing — prompt injection, jailbreak resistance, OWASP LLM Top 10 (2025), RAG and agent/tool-use security, model supply chain, and AI red teaming for authorized assessments
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。
Multi-LLM collaborative brainstorming and planning. Use when user explicitly requests consultation with multiple AI models (ChatGPT, Gemini, other LLMs) before presenting an implementation plan, or asks to "consult the council", "ask other models", or "get perspectives from other AIs". Queries external LLM APIs, synthesizes their perspectives, and presents an adapted implementation plan.
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。
Enhancement overlay — version the WHOLE deployable LLM-app artifact as one bundle: prompts + compiled programs + model snapshot pins + retrieval config + eval-set version, versioned together so a deploy is reproducible and rollback is atomic. Activate when preparing to deploy an LLM app, when asking "what exactly is running in prod right now?", when a deploy must be reproducible months later, or when an incident needs a clean rollback. The core reframe: an LLM app artifact is NOT an ML model — it is a manifest over many independently-mutable parts, not one weights file. Do NOT activate for one-off prompt edits with no deploy, for a single-component demo, or where a vendor owns the whole prompt lifecycle. For versioning ONE compiled prompt use [[agentsop-per-model-artifacts]]; for the CI comparison mechanism use [[agentsop-regression-gate]]. Search keywords: prompt versioning, reproducible deploy, what is running in prod, rollback LLM app, model pinning, prompt registry, version prompts and 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。
Operational knowledge for the daily-brief digest pipeline (this project). RSS/API fetchers, pluggable LLM enrichment (default claude CLI on Max; also anthropic/openai/deepseek/minimax API), trading section, HTML rendering, cross-platform scheduler integration (Windows Task Scheduler / macOS launchd / Linux cron). Load when the user asks about running daily / regenerating sections / debugging a failed run / adding or disabling sources / LLM quota / scheduler / why a tab shows wrong data / why a source failed / switching LLM backend. Always prefer the documented npm commands over re-implementing logic. Diagnose by reading logs/daily-*.log first, then logs/llm-calls.jsonl for LLM-side issues.
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。
Redpanda's Agentic Data Plane: governance infrastructure for building, running, and governing AI agents and MCP servers, plus a proxying AI Gateway for LLM providers, operated via `rpk ai` and the ADP API. Use when creating or managing AI agents (managed or self-managed) via `rpk ai agent` or `AgentRegistryService`; configuring MCP servers (remote or managed catalog, code mode, auth); setting up LLM providers or querying models via `rpk ai llm`/`rpk ai model` or the AI Gateway proxy; or configuring budgets, guardrails, or Cedar access-control policies through the governance APIs. Also covers reading agent transcripts and spending insights, and wiring OAuth clients or providers to the aigw Authorization Server. For the separate rpk cloud mcp control-plane MCP server, see `/redpanda:rpk-cloud`.
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。
Comprehensive security auditing framework for LLM applications covering OWASP Top 10 for LLMs, threat modeling, penetration testing, and compliance with NIST AI RMF and ISO 42001Use when "security audit, llm pentest, ai security assessment, compliance audit, vulnerability assessment, security, audit, compliance, penetration-testing, owasp, llm" mentioned.
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。
Security vulnerabilities in AI-generated code and LLM applications, covering OWASP Top 10 for LLMs, secure coding patterns, and AI-specific threat modelsUse when "ai code security, llm vulnerabilities, ai generated code review, owasp llm, secure ai development, security, ai, llm, owasp, code-review, vulnerabilities" mentioned.
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。