'fuse' 검색 결과

Helps choose and tune Cloud Storage FUSE mounts, which expose bucket objects through file paths. It covers cache sizing, training performance, checkpoint write safety, and cost diagnosis on GKE, Compute Engine, and Cloud Run.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Access training data through file paths
• Choose between FUSE, native reads, and shared file systems
• Check checkpoint and concurrent write safety

Mounts Cloud Storage buckets as a POSIX file system with Cloud Storage FUSE (gcsfuse). Use when interacting with gcsfuse: decide whether FUSE, native gs:// reads, or Filestore/Managed Lustre fits a workload, deploy tuned mounts on GKE, Compute Engine, or Cloud Run, enable and size file, stat, and list caches, tune mount flags or config-file settings, apply workload profiles, keep ML checkpointing safe (rename atomicity, hierarchical namespace/HNS, close-time finalization, concurrent writers), or diagnose slow training, low throughput, or bill spikes with gcsfuse metrics. Covers mount semantics, gcsfuse CLI and config files, GKE gcsfuse CSI driver (Workload Identity principal:// bindings, profile StorageClasses, sidecar sizing), and Cloud Run volume mounts. Don't use for bucket administration or data management without a mount (google-cloud-storage-basics) or fully POSIX-compliant shared file systems (Filestore, Managed Lustre).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Add a diffusion model or Diffusers generation pipeline to SGLang. Examine reference code, choose a suitable pipeline structure, reuse existing components, and implement model settings and processing stages, with guidance for later multi-GPU support.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a new diffusion model to SGLang
• Porting a Diffusers generation pipeline
• Adding a variant by reusing existing model components

Run diffusers generation pipelines from a terminal and save their outputs. Inspect accepted inputs without downloading model weights, apply LoRA adapters during generation, run remotely through HF Jobs, and inspect software and GPU environment details.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running one-off generation or batch jobs from a terminal
• Checking a new model's basic operation
• Inspecting accepted inputs before running a pipeline

Review a diffusers contribution before opening a pull request. Check the full change against project rules, trace potentially unused code, and receive a severity-based report separating required fixes from points to discuss with reviewers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking a diffusers contribution before opening a pull request
• Investigating potentially unused code in a change
• Reviewing fixes again and preparing notes for reviewers

Integrate a model or pipeline into diffusers, from file structure and weight conversion to tests and documentation. Verify numerical agreement with the original implementation, with modular pipelines as the preferred starting point for new integrations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a new model and pipeline to diffusers
• Converting a pipeline to modular format
• Converting weights for a new version of a supported model

Set up Langfuse to record LLM application activity, costs, and response times. Manage prompt versions, organize evaluation datasets, and compare results to investigate failures and guide improvements using recorded evidence.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating failures in LLM applications
• Comparing prompt versions and evaluation scores
• Monitoring LLM costs and response times

Package an existing custom diffusers block into a directory ready for Hub upload. Select a class from a Python file and save its source, configuration, and associated files so others can load the block.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a custom block for Hub distribution
• Selecting one block class from a Python file
• Creating the directory layout needed to load a shared block

Build image generation workflows in Python with Stable Diffusion and HuggingFace Diffusers. The guide covers text prompts, transforming existing images, replacing selected regions, controlling composition, applying learned styles, and adjusting memory use during generation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generating images from text descriptions
• Changing the style of an existing image
• Replacing a selected region of an image

Diagnoses storage failures in GKE, Google's managed Kubernetes service, including stuck Pods, disk attachment errors, storage pressure, and missing volume expansion. It provides diagnostic commands and reviewable configuration patches for deployment through GitOps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Diagnose Pods stuck in ContainerCreating
• Investigate slow disks and node storage pressure
• Find why expanded storage is missing inside a container

Build image generation workflows in Python with Stable Diffusion and HuggingFace Diffusers. The guide covers text prompts, transforming existing images, replacing selected regions, controlling composition, applying learned styles, and adjusting memory use during generation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generating images from text descriptions
• Changing the style of an existing image
• Replacing a selected region of an image

Fuse all of a contributor's named skills into a single fusion node (suite capstone) in the registry. Use when the user says "fuse <contributor>'s skills", "create a suite for <contributor>", "build a capstone for <user>", "fuse all of <user>'s named skills", "create a suite fusion", "consolidate <contributor> into one skill", or explicitly types /gaia-fuse-full-suite. Also triggers when a contributor has accumulated 3+ named skills and the user asks what to do next with them, or when someone asks "can we make a suite from these?". This skill: collects component IDs from registry/named/<contributor>/, verifies nodes exist, researches evidence and appraises Trust Magnitude, runs `gaia dev fuse` to write the fusion node and suite manifest, validates, and opens a PR on a review/meta/ branch.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Interact with Langfuse and access its documentation. Use when needing to (1) query or modify Langfuse data programmatically via the CLI — traces, prompts, datasets, scores, sessions, and any other API resource, (2) look up Langfuse documentation, concepts, integration guides, or SDK usage, or (3) understand how any Langfuse feature works. This skill covers CLI-based API access (via npx) and multiple documentation retrieval methods.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Python으로 텍스트 기반 이미지 생성, 기존 이미지의 화풍 변경, 지정 영역 채우기를 구현합니다. 구도와 스타일 제어, 생성 설정 조정, 메모리 절약 방법도 안내합니다.

• 텍스트로 이미지를 생성할 때
• 기존 이미지를 수채화풍으로 바꿀 때
• 사진의 지정 영역을 채울 때

Helps configure persistent storage for GKE applications, including disk selection, storage requests, shared files through Filestore, and volume expansion. It also explains how disk compatibility and zone placement affect workload scheduling.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating persistent storage for a GKE application
• Sharing files across multiple Pods with Filestore
• Expanding an existing storage volume

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Find dependency confusion risks, where a public package can replace an internal package with the same name. Review dependency files, identify exposed names, and guide registry restrictions and recurring CI checks to prevent substitution.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Auditing projects that mix private and public package registries
• Checking exposure after internal package names have leaked
• Restricting package sources to approved private registries

Turn a Markdown code review into a single HTML file with code changes beside severity-labeled comments. Readers can jump to findings, inspect line numbers, and identify the named human reviewer.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning a lengthy Markdown code review into HTML
• Reading code changes beside review comments
• Jumping directly to findings by severity

Investigate TOCTOU flaws: race conditions where a checked property changes before an action uses it. The skill covers filesystem, kernel, web, and container testing, helping identify vulnerable sequences, reproduce timing issues, and document observed impact.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing file checks before privileged operations
• Testing access revocation during long-running requests
• Investigating duplicate payments from concurrent requests

Read and write Langfuse: browse traces and observations, list prompts and datasets, score traces, add dataset items. Trigger phrases: langfuse, llm observability.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Investigate AI traces, observations, exceptions, latency, sessions, prompts, datasets, annotation queues, and scores through Langfuse MCP. Use when the request names Langfuse or asks to diagnose recorded AI behavior.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Combine two AI agent skills into one. Trigger phrases: /fuse, "fuse these skills", "combine skills", "merge these two skills", "create a fusion skill"

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Manages GKE storage, including PVCs, PersistentVolumes, Filestore, and GCS FUSE. Use when configuring GKE storage, creating PVCs, or setting up GCS FUSE on GKE. For diagnosing storage failures (volume attach/mount errors, disk performance/node storage pressure, or Cloud Storage FUSE OOM), use the gke-storage-troubleshooting skill instead. Don't use for database administration or replication strategies outside volume provisioning context.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Especialista em Langfuse - plataforma de observabilidade LLM de código aberto. Cobre rastreamento, gerenciamento de prompts, avaliação, datasets e integração com LangChain, LlamaIndex e OpenAI. Essencial para depuração, monitoramento e melhoria de aplicações LLM em produção. Use quando: langfuse, observabilidade llm, rastreamento llm, gerenciamento de prompts, avaliação llm.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.