'llm' 검색 결과

Assess a website’s readiness for AI search alongside traditional SEO. Review content citation readiness, crawler access, brand mentions, technical foundations, and structured data, then generate scores, prioritized recommendations, and client reports.

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• Audit a website’s readiness for AI search
• Assess a page’s suitability for AI citation
• Check AI crawler access and generate llms.txt

Coordinate AltLLM Portal CLI tasks across wallet login, API key management, credit balances, billing history, and crypto top-ups. This umbrella skill routes each part of a broader workflow to the relevant specialized skill.

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• Log in with a wallet and create an API key
• Review credit balance, transactions, and usage together
• Create a payment link and check its status

Use the AltLLM Portal CLI to create NOWPayments crypto payment links, check their status repeatedly until settlement, or pay supported links directly from a wallet. Includes handling discount codes and checking which payment currencies are allowed.

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• Create a NOWPayments crypto payment link
• Monitor a payment link until settlement
• Pay a supported link directly from a wallet

Routes AI development questions to focused references on agents, language model security, retrieval-augmented generation, which uses retrieved information in answers, and prompt design and evaluation. It also outlines principles for validation, safety boundaries, version control, and ongoing assessment.

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• Find guidance for agent development
• Locate references on prompt injection defenses
• Explore retrieval and reranking for RAG

Adds a new LLM provider implementing LLMProvider interface with call() and stream() methods. Integrates with provider factory in src/llm/index.ts, config detection in src/llm/config.ts, and error handling via tracking and recovery. Use when adding a new model backend, integrating a third-party LLM API, or extending LLM platform support. Do NOT use for fixing bugs in existing providers, modifying existing provider behavior, or changing the LLMProvider interface.

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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, monitor llm, debug llm, langfuse, observability, tracing, llm-monitoring, evaluation, prompt-management, debugging, analytics" mentioned.

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Erstellt oder überarbeitet einen Skill FÜR DIESES ÖKOSYSTEM: delegiert die generische Interview-/Entwurfs-/Eval-Schleife an das offizielle skill-creator-Plugin (Anthropic, Apache-2.0), setzt aber zusätzlich die ellmos-Anforderungen durch, die das offizielle Plugin nicht kennt — Haus-Frontmatter (9 Felder), Kategorie/Sichtbarkeits-Regeln, anbieter-/nutzer-/ellmos-neutrale aber ellmos-sensitive Erkennung via grounding-seed, mitgelieferte config.json (inkl. Lernvertrag) + connections.json, Anbieter-/Vendor-Bewusstsein (State-of-the-Art-Alternativen inkl. eigener ellmos-Komponenten vorschlagen), einen Selbstaktualisierungs-Abschnitt UND einen wiederverwendbaren Lern-/Abdeckungs-Dateisatz (user-usecases.json, usecase-gaps.json, known-providers-and-abilities.json, gap-closing-analysis.json — an skill-creators evals.json und A2A/AgentSkill angelehnt, kein Parallelstandard). IMMER nutzen, wenn ein NEUER Skill für dieses System entstehen oder ein bestehender auf ellmos-Konformität nachgerüstet werden soll — auch bei "erstelle einen Skill", "bau mir einen Skill für X", "skill-creator nutzen", "neuen Skill anlegen", "diesen Skill ellmos-konform machen", "Lernvertrag/Verhaltenscodex für einen Skill anlegen", "Usecase-Abdeckung eines Skills prüfen". Für reines Skill-FINDEN/ROUTEN stattdessen skill-finder, für Landschafts-Audit skill-explorer.

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LLM이 JSON, 정규 표현식, Pydantic 타입 정의에 맞춰 답변을 생성하도록 합니다. 텍스트에서 추출한 정보와 분류 결과를 후속 프로그램에서 다루기 쉬운 형식으로 정리합니다.

• 텍스트에서 기업과 제품 정보를 추출할 때
• 정해진 라벨로 리뷰를 분류할 때
• 프로필을 항목별로 정리할 때

NVIDIA GPU에서 대규모 언어 모델의 응답 생성을 가속하고 API로 제공하도록 설정을 돕습니다. 양자화, 일괄 처리, 여러 GPU로의 분산도 다룹니다.

• 언어 모델을 채팅 API로 제공
• 많은 프롬프트를 일괄 처리
• 양자화로 메모리 사용량 감소

NeMo Curator로 LLM 학습 데이터의 중복과 저품질 내용을 제거하고 개인정보를 삭제하거나 대체합니다. 처리 흐름 구성과 이미지, 영상, 음성 정리도 다룹니다.

• 웹 수집 문장을 학습용으로 정리합니다
• 완전 일치, 유사, 의미 기반 중복을 제거합니다
• 텍스트의 개인정보를 삭제하거나 대체합니다

Plan AI application development by selecting relevant phases and companion skills. The workflow covers language model integration, retrieval from documents, agents, machine learning pipelines, monitoring, and security, with prompts and quality checklists.

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• Plan AI features and language model integration
• Organize RAG retrieval design and implementation
• Map agent roles, tools, and memory

Build an end-to-end evaluation pipeline for Python AI applications using pixie-qa and real language-model calls. Define criteria, prepare realistic test data, score application runs, and analyze the evidence to produce a prioritized improvement plan.

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• Setting up quality checks for Python AI applications
• Creating realistic scenarios for known failure modes
• Scoring full application runs with real language-model calls

Build a screening pipeline for web pages, PDFs, and images before an AI agent reads them. Extract hidden text, check for malicious instructions with detection models, and record decisions to block or allow content.

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• Screening external content before an agent reads it
• Checking uploaded PDFs and images for hidden instructions
• Scanning documents before adding them to a knowledge base

Convert LLMs to GGUF and reduce model size through quantization, which stores weights at lower numerical precision. Prepare models for CPU or Apple Silicon inference, compare compression levels, and integrate them with local AI tools.

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• Preparing an LLM for CPU-only use
• Running a local model on Apple Silicon
• Comparing model size and quality across quantization levels

Use Phoenix to record execution traces, detailed records of an AI application's operations, and investigate problems. Evaluate answers on datasets, compare prompts and models, and set up monitoring through a self-hosted server.

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• Investigate AI application problems using execution traces
• Evaluate answer quality across a test dataset
• Compare prompts and models through experiments

Build and debug LangBot plugins that handle messages, commands, and AI tool calls. The skill covers SDK usage, common errors, test environment setup, WebSocket message testing, and preparation for publishing to LangBot Space.

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• Create a plugin that reacts to incoming messages
• Fix plugin SDK errors and missing responses
• Test group and private chats through WebSocket

Use Phoenix to record execution traces, detailed records of an AI application's operations, and investigate problems. Evaluate answers on datasets, compare prompts and models, and set up monitoring through a self-hosted server.

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• Investigate AI application problems using execution traces
• Evaluate answer quality across a test dataset
• Compare prompts and models through experiments

Get guidance on adapting LLMs through additional training with LLaMA-Factory. The skill covers a visual interface requiring no code, LoRA and QLoRA training methods, and documentation-based help with implementation, features, and debugging.

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• Learning additional training through a visual interface
• Exploring LoRA and QLoRA training methods
• Checking supported models and multimodal features

Guides LLM fine-tuning with Axolotl, from YAML configuration and dataset formats to distributed training across GPUs. Covers LoRA and QLoRA, methods that train on preferences, multimodal support, and troubleshooting using documentation references and code examples.

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• Prepare YAML settings for LLM fine-tuning
• Explore LoRA, QLoRA, and preference training options
• Check dataset formats and input handling

Evaluate Hugging Face Hub models using inspect-ai or lighteval, with local inference or a provider-backed option. Choose an inference engine, start with a small test, and switch engines when model compatibility requires it.

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• Run benchmark evaluations on a local GPU
• Choose an evaluation framework and inference engine
• Check a small sample before a full evaluation

Test polly's coordination of coding agents using a scripted mock language model and live command-line tools. Check startup, task dispatch, safety rules, parallel delegation, and whether real agents make appropriate orchestration decisions.

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• Reproducing polly orchestration bugs
• Checking safety rules with a mock language model
• Verifying delegation and cross-review with real agents

Build GenLayer contracts that use AI or external evidence to make on-chain decisions. The skill guides independent validator checks, persistent data storage, error handling, and interactions between contracts, with pinned runtime versions.

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• Build contracts that settle disputes using external evidence
• Design validator checks for AI-generated scores
• Review contract storage types and error handling

Test web APIs, authentication, and LLM features with six Python scanners. Check cross-site access rules, header injection, database inputs, and signed login tokens, then investigate hidden server behavior through external callbacks and chatbot responses.

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• Checking CORS and response-header behavior
• Testing NoSQL logins and JWT authentication
• Confirming hidden bugs through external callbacks

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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