「lms」の検索結果

安全创建、发布、Release、审计和维护 GitHub 仓库的规范协议：检查本地规则与锁文件，在首次 git add 前创建 .gitignore，运行隐私检查，准备 README/i18n/Banner/元数据，验证 release 标签与 GitHub releases，并更新组织 Profile、llms.txt 文件及注册表链接。

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Use when the user wants to send the same prompt to several LLMs and compare the answers - sequentially (stopwatch, clean per-lane timing) or in parallel (true race), with optional repetitions per model, judged by the starting model across quality, correctness, completeness, instruction fidelity and latency (time is only one dimension). Triggers on /compare-race, 'model race', 'vergleiche modelle', 'gleicher prompt an mehrere modelle'.

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Protocol for safely creating, publishing, releasing, auditing, and maintaining GitHub repositories: check local rules and locks, create .gitignore before the first add, run privacy checks, prepare README/i18n/banner/metadata, verify release tags and GitHub releases, and update organization profiles, llms.txt files, and registry links.

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Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

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Send one research question to as many independent LLMs as possible at once, each in its strongest reasoning mode with proof of that mode, then synthesize. Floor of 3 models, quorum of 4; late answers are collected later. Use for high-stakes questions where one model's view is not enough. Triggers: /hyper, hyper research, ask every LLM, max fan-out research.

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Fan one deep-research prompt out to several external LLMs at once through a logged-in browser, then collect the reports, archive the originals and synthesize a consensus. Use at the final stage of a research protocol, instead of hand-pasting one prompt into N sites. Triggers: "/dr-fanout", "fan out the DR", "distribute the research prompt".

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Synthesize WooCommerce coupons at runtime without creating shop_coupon posts in the DB — via the woocommerce_get_shop_coupon_data filter, the hidden-but-powerful virtual-coupon mechanism. Filter fires inside WC_Coupon::__construct, and a non-false return becomes a fully functional coupon read through read_manual_coupon. Plus custom discount types via woocommerce_coupon_discount_types and woocommerce_coupon_get_discount_amount, and the validation triple woocommerce_coupon_is_valid / _is_valid_for_cart / _is_valid_for_product. Use when implementing rule-driven coupons (auto-apply X% to logged-in users, code-pattern detection like LOYALTY-42, external CRM integration that hands out codes), without populating wp_posts. The AI-deficient pattern; most LLMs don't know dynamic coupons exist. Triggers on woocommerce_get_shop_coupon_data, woocommerce_coupon_discount_types, woocommerce_coupon_get_discount_amount, read_manual_coupon, virtual coupon, dynamic coupon in WC context.

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Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

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Optimize for AI search, answer engines, GEO/AEO/LLMO and conventional search together. Use for citability, AI crawler access, entity/brand evidence, llms.txt evaluation, platform readiness and AI-answer visibility.

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Generate structured AI agent skills (SKILL.md) and llms.txt from your TypeScript API documentation

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Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

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

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August 24, 2026 1 Introduction Reinforcement Learning (RL) has emerged as a highly effective approach for training Large Language Models (LLMs), yet i

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MegaTrain Full Precision Training of 100B+ Parameter LLMs on a Single GPU A RAM-centric architecture that stores parameters in host memory and treats

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Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs Published in arXiv , 2025 Abstract : Efficient deployment of 1-bit Large Language

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The BitLinear layers we use in this project quantize the weights using ternary precision (with value

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Agent Canvas Architecture - OpenHands Docs Documentation Index Fetch the complete documentation index at: /llms.

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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.

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Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.

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Get reliable JSON, enums, and typed objects from LLMs using response_format, tool_use, and schema-constrained decoding across OpenAI, Anthropic, and Google APIs.

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You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati

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Get reliable JSON, enums, and typed objects from LLMs using response_format, tool_use, and schema-constrained decoding across OpenAI, Anthropic, and Google APIs.

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Guide for when to use and when not to use return validators in Convex functions. Use this skill whenever the user is writing Convex queries, mutations, or actions and needs guidance on return value validation. Also trigger when the user asks about Convex type safety, runtime validation, AI-generated Convex code, Convex AI rules, Convex security best practices, or when they're debugging return type issues in Convex functions. Trigger this skill when users mention "validators", "returns", "return type", or "exact types" in the context of Convex development. Also trigger when writing or reviewing Convex AI rules or prompts that instruct LLMs how to write Convex code.

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Create custom external web service APIs for Moodle LMS. Use when implementing web services for course management, user tracking, quiz operations, or custom plugin functionality. Covers parameter validation, database operations, error handling, service registration, and Moodle coding standards.

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