「llms」の検索結果

Audits any domain's AI-readiness by using curl to directly probe robots.txt, llms.txt, and llms-full.txt, then scores each file against a structured checklist and delivers a formatted report with pass/warn/fail findings and actionable fixes. Use this skill whenever a user provides a domain or URL and wants to know if llms.txt or llms-full.txt is available, discoverable, or properly structured. Trigger on phrases like "check llms.txt for", "does this site have llms.txt", "find llms.txt", "check llms for this url", "audit llms.txt", "is llms-full.txt available", or any time a user shares a domain/docs URL and wants AI-readiness checked. Also trigger when the user wants to verify GEO/AEO readiness of a documentation site.

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Generate or audit an `/llms.txt` file at the site root that makes the site legible to LLMs and AI answer engines at inference time, following the llms.txt proposal (Jeremy Howard, 2024). Use this skill whenever the user asks to "build llms.txt", "create llms.txt", "generate llms.txt", "audit llms.txt", "make my site LLM-friendly", "expose the site to AI assistants", "let ChatGPT / Claude / Perplexity cite my site", "add an AI assistants index", or asks about `.md` companion pages for LLM consumption. Output is a valid `llms.txt` file (or an audit) plus optional `.md` companion page generation for top pages.

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Generate llms.txt files from project documentation following the llmstxt.org spec. Use when asked to create AI-friendly documentation indexes, generate llms.txt, or make a project discoverable by AI assistants.

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Generates llms.txt and llms-full.txt files for LLM-friendly project documentation following the llms.txt specification. Use when the user wants to create LLM-readable summaries, llms.txt files, or make their wiki accessible to language models.

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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.

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Generate, validate, or audit llms.txt files for AI search visibility. Crawls site structure, generates spec-compliant Markdown index for LLMs. Use when user says "llms.txt", "llm txt", "AI crawlers", "generate llms", "LLM file", "AI readability file".

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。

Generate llms.txt files from project docs per the llmstxt.org spec, for AI-friendly documentation indexes and discoverability. Use to create or update llms.txt for AI assistants.

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。

Analyzes and generates llms.txt files -- the emerging standard for helping AI systems understand website structure and content. Can validate existing llms.txt files or generate new ones from scratch by crawling the site.

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Searching internet for technical documentation using llms.txt standard, GitHub repositories via Repomix, and parallel exploration. Use when user needs: (1) Latest documentation for libraries/frameworks, (2) Documentation in llms.txt format, (3) GitHub repository analysis, (4) Documentation without direct llms.txt support, (5) Multiple documentation sources in parallel

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Generate and maintain llms.txt files for AI discoverability. Scans repos to create curated content maps that help AI answer engines surface your project accurately. Implements the llms.txt specification from Answer.AI.

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。

Searching internet for technical documentation using llms.txt standard, GitHub repositories via Repomix, and parallel exploration. Use when user needs: (1) Latest documentation for libraries/frameworks, (2) Documentation in llms.txt format, (3) GitHub repository analysis, (4) Documentation without direct llms.txt support, (5) Multiple documentation sources in parallel

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。

Analyzes and generates llms.txt files -- the emerging standard for helping AI systems understand website structure and content. Can validate existing llms.txt files or generate new ones from scratch by crawling the site.

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。

/LLMs решает текущий вопрос через ограниченный консенсус Claude, Codex, Cursor, Grok и других пиров. Передаёт тему и контекст сессии, читает только адресные сообщения этой темы, требует доказательства и закрывает ACCEPT или BLOCK. Триггеры: /LLMs, договорись с другими LLM, найди консенсус моделей

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Short alias for /llms: settle the current question through a targeted, bounded consensus of other LLMs. Use when you want outside models to agree or disagree with a plan before acting. All logic lives in the llms skill. Triggers: /LLLL, llll.

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Updates the llms.txt file to reflect changes in documentation. Use when editing repository details or specifications. For creating from scratch, see create-llms.

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 an llms.txt file from scratch based on repository structure. Use when configuring LLM discovery files. For updating existing configurations, see update-llms.

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。

Technical SEO for AI search engines. Checks AI crawler accessibility, llms.txt compliance, robots.txt configuration, server-side rendering. Outputs problems + fixes (generates llms.txt, robots.txt modifications). Use when user says "AI crawlers", "llms.txt", "GPTBot", "GEO technical".

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大規模言語モデルの知識・推論・コード生成を共通の課題で評価するスキル。複数モデルの比較表や学習途中の成績推移を作り、評価結果と個別の回答を保存します。

• MMLU・GSM8Kでのモデル評価
• 複数モデルの比較表作成
• 学習途中のモデルの成績追跡

Use when auditing public documentation portals, API references, help centers, SDK docs, or large knowledge bases. Check the final file served at `/llms.txt` and verify that the linked pages are stable, high-value, and accessible without relying on a JavaScript-only interface.

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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.

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Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.

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Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.

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。