'deploy' 검색 결과

Set up a GitHub Actions CI/CD pipeline with linting, testing, type-checking, and deployment steps.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Focused Signals scout for PostHog projects collecting Content Security Policy (CSP) violation reports. Watches `$csp_violation` events for fresh blocked-URL clusters, per-directive bursts, page-scoped regressions after deploys, and suspicious third-party domains that may indicate a compromised script. Emits aggregated findings only when a cluster clears the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Launch multiple explore subagents in parallel to investigate architecture, data models, auth, APIs, and deployment. Synthesize into an onboarding document.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Stellar smart contract development (Rust, soroban-sdk). Entry point with project setup, contract anatomy, and build/deploy workflow, routing to three companion files in this directory — development.md (storage/TTL, authorization, cross-contract calls, tokens, events, errors, upgrades, fees, troubleshooting), testing.md (unit, fuzz, property, fork, mutation, integration), and security.md (vulnerability classes, checklists, tooling, audits). Use when writing, testing, reviewing, securing, debugging, or shipping Stellar smart contracts, including anything the user calls "Soroban" — Soroban contracts, soroban-sdk, Soroban auth/storage/TTL errors, SEP-41 tokens, or SAC integration from contract code.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create AI governance policies for responsible development and deployment. TRIGGERS - Use when user needs help with ai-governance-policy related tasks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

OpenAI API (developers.openai.com) の運用・管理リファレンス。 Administration API, admin API keys, RBAC, roles, groups, invites, projects, spend limits, audit logs, usage / costs API, Terraform provider (openai/openai), workload identity federation (WIF, OIDC / SPIFFE / X.509 / Idira), moderation API, misalignment monitoring, safety alerts / cases, safety best practices, under-18 guidance, ZDR Private Safety Processing (PSP), production best practices, deployment checklist, cost / latency optimization, fast / ultrafast mode, Realtime API costs。 rate limits・error handling・prompt caching・webhooks は openai-api-core、Codex WIF・管理は openai-codex。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Claude Code (code.claude.com) デプロイ・CI/CD リファレンス。 Amazon Bedrock / Google Cloud Agent Platform (Vertex AI) / Microsoft Foundry / Claude Platform on AWS、 Claude apps gateway、LLM gateway (protocol / rollout)、 GitHub Actions (claude-code-action) / GitLab CI/CD / GitHub Enterprise Server (GHES)、 Code Review (REVIEW.md・review level・@claude review always・コスト・トラブルシュート)、 ローカル /code-review、ultrareview (claude ultrareview)、 Slack (Claude Tag、旧 Claude Code in Slack、Pro/Max)、 headless mode、cloud environments、devcontainer、 self-hosted environments（runner 構成・identity 検証・本番デプロイ）。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

最適化モデルの運用設計。自動化・監視・フォールバックを定義する

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when building MCP (Model Context Protocol) servers in Java/Spring Boot. Covers tool registration, resource exposure, prompt templates, and production deployment using the official MCP Java SDK. Use when user mentions MCP, AI agent integration, or tool calling.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Orchestrate end-to-end backend feature development from requirements to deployment. Use when coordinating multi-phase feature delivery across teams and services.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure MySQL Flexible Server SDK for .NET. Database management for MySQL Flexible Server deployments.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure PostgreSQL Flexible Server SDK for .NET. Database management for PostgreSQL Flexible Server deployments.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

High-level SDK for Azure AI Foundry projects with agents, connections, deployments, and evaluations.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, SAM/CDK deployment, and cold start optimization.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.