'labeling' 검색 결과

코드에서 LLM을 사용하는 흐름을 업무 단위로 찾고 승인 후 용도별 라벨을 붙입니다. Caveman Cloud에서 처리 흐름별 비용을 나누어 확인할 수 있도록 합니다.

• LLM 비용을 업무별로 파악할 때
• API와 정기 작업의 LLM 사용을 조사할 때
• 평가 처리 비용을 따로 확인할 때

Review buttons for missing or unclear names, including controls that contain only icons. Add text or labels that explain each action to screen reader users, and replace buttons used for page navigation with links.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding buttons without descriptive names
• Labeling icon-only buttons for screen readers
• Checking whether navigation controls should be links

Check checkboxes, radio buttons, and switches for descriptive names that screen readers can announce. Identify missing or unclear labels, suggest appropriate HTML or ARIA labeling, and explain how to verify the changes with accessibility tools.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking labels on checkboxes and radio buttons
• Reviewing whether switch names explain their purpose
• Fixing missing or unclear accessible names

Transcribe audio with Azure AI Speech and obtain word timestamps, speaker labels, and language identification. Use the resulting transcript in OpenMontage subtitle workflows, with a compatible local transcription fallback when Azure is unavailable.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Transcribing recorded audio
• Preparing timestamps for video subtitles
• Labeling speakers in interviews

Record and inspect source history, trust scores, and handling labels on AI-generated or modified files. Use AKF metadata, information attached to files, to support review, handoff, and advisory audit checks without treating labels as proof of compliance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording how an AI-generated file was created
• Inspecting existing metadata before editing a file
• Labeling files as public, internal, or confidential

Add boxes, arrows, labels, and color-coded highlights to screenshots and diagrams using Pillow. Includes automatic label placement, detection of changed image regions, and guidance for timing annotations in animated GIF demonstrations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Highlighting screenshot changes for pull request reviews
• Labeling changes in before-and-after images
• Adding explanations to diagrams and architecture images

Create shareable links that open specific Arize resources, including traces, datasets, and evaluators. Use IDs from logs or exported data, with suitable time ranges, to direct teammates to the relevant view.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Share a trace with teammates
• Open an Arize view from IDs in logs
• Link directly to a dataset or evaluator

Organize human review in Arize by defining label choices, numeric ranges, or free-text feedback fields. Manage review queues and reviewers, submit annotations for individual records, and apply existing labels to project spans, or individual processing records, in bulk.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Define shared labels and scoring ranges for reviewers
• Create review queues and assign reviewers
• Record feedback on individual queue items

Classify skills CLI pull requests, or proposed code changes, as documentation, bug fixes, or enhancements using their descriptions and patches. Add one label when the evidence is clear, while preserving existing classification labels.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Label changes that only update documentation or examples
• Classify fixes to existing CLI behavior
• Identify new commands or expanded agent support

Explore code structure and history using an index of code relationships and meaning-based search. Trace calls, assess change impact, find likely bug locations, generate architecture reports, and review pull requests for risk and conflicts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Tracing function calls and change impact
• Investigating code locations related to a GitHub issue
• Generating architecture reports and class diagrams

Prepare for FDA medical device quality audits through six evidence-focused questions. Review complaints, adverse event reports, process validation, manufacturing records, corrective actions, labeling, and inspection history, then organize readiness gaps and three next actions with owners.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing for an annual internal quality audit
• Reviewing readiness before an FDA inspection
• Organizing evidence for a Form 483 response

Investigate failed tests, type checks, builds, and CI jobs by recording exact evidence and repeating checks in a clean comparison environment. Use the results to distinguish existing failures, newly introduced problems, environment issues, and unresolved cases.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking whether a failing test was already broken
• Investigating a failure that appears only in CI
• Distinguishing timeouts from environment problems

Audit and strengthen security for applications running on GKE. Configure pod traffic restrictions, sandbox isolation, namespace security standards, and Secret Manager volume mounts to protect workloads at the application and namespace level.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Auditing GKE security settings
• Restricting communication within a namespace
• Isolating risky pods and applying security standards

Sort a Superhuman inbox into messages needing a reply, waiting for others, useful information, and noise. Receive a clear attention list, then apply requested actions such as archiving, labeling, reminders, or unsubscribing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding messages that need a reply
• Reviewing requests awaiting a response
• Sorting newsletters and notifications

Analyze incoming Slack messages, emails, or escalations before replying. Separate the explicit request from the outcome behind it, clarify requirements and success criteria, and develop a reply or counterproposal while clearly labeling assumptions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Analyzing an executive's urgent request before replying
• Clarifying the outcome behind a vague feature request
• Preparing a response to an escalation email

Develop public equity trade pitches for portfolio managers, covering long, short, or paired positions. Explain the difference from market expectations, potential catalysts, gains and losses, risk considerations, and exit rules, while labeling assumptions and missing evidence.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Develop a long or short equity trade pitch
• Compare a trade thesis with market expectations
• Test catalysts, scenarios, and risk controls

Design a dark interface with matte-black surfaces, emerald accents, framed cards, and restrained glow. Apply consistent styling across navigation, typography, charts, and motion to create a calm, technical dashboard.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a dark analytics dashboard
• Unifying cards and navigation styles
• Highlighting status and chart data in green

Create a dark interface built around fine gradient borders, asymmetrical panels, and precise labels. Apply a consistent visual system to page frames, typography, controls, and backgrounds, using restrained monochrome colors and subtle motion.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Defining a consistent dark interface style
• Structuring panels with fine gradient borders
• Designing navigation and active list states

Classify existing tests with standard tags for success cases, errors, boundaries, and other behaviors. Get a per-test mapping and distribution summary, or apply tags using supported framework syntax and verify compilation and test discovery.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing success-case and error-case test counts
• Adding standard tags to existing tests
• Reviewing the mix of boundary and integration tests

Extract unidentified external addresses from TRES Finance and export a deduplicated Excel workbook. An import-ready contacts sheet lets users add names and tags, while a reference sheet shows inferred networks, transaction counts, and USD volumes to help prioritize labeling.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building an address book from transaction history
• Preparing a TRES contacts import sheet
• Prioritizing unidentified addresses by transaction volume

Use this skill whenever the user asks for a chart, graph, plot, or visualization of data — 'chart this by month', 'show me a breakdown by category', 'visualize the trend'. Covers picking the right chart type for the question being asked, labeling/readability rules, and which plotting library to reach for. Pair with data-analysis for the underlying data prep.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Review chart choice, labeling, accessibility, and storytelling quality for existing visualizations. Use as an explicit/manual helper for critique and cleanup, not as the main owner for scientific figure production or general chart generation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Run the PAIDF Orchestration Event Video Generation DAG on Kubernetes - image-to-video anomaly generation, auto-labeling, and anomaly dataset generation. Select for requests about event video generation, anomaly video generation, image-to-video synthesis, Cosmos3 image2video, anomaly dataset creation, safety/surveillance SDG, or generating person-falling, person-climbing, person-running, fighting, smoking/vaping, fire/smoke, or shoplifting video clips from a seed image. Runs environment setup first when controller readiness is unknown. Not for person-crop clothing/attribute augmentation (that is image-attribute-augmentation-workflow) and not for video style transfer.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user wants to orchestrate defect image generation with NVIDIA Cosmos AnomalyGen (Cosmos-Predict2-derived) on OSMO for PCBA, metal surface, and glass inspection. The Day 0 path handles cold-start with USD-to-ROI, image-edit augmentation, and AnomalyGen to create initial PCBA datasets. The Day 1 path performs inference and labeling on real images. This skill helps with first-time asset setup, creation of finetuning checkpoints, and configuring deployment. Trigger keywords: defect image generation, dig workflow, dig pipeline, defect image detection workflow, aoi pipeline, aoi anomalygen, usd2roi anomalygen, day 0 pcba, day 1 pcba, day 1 real-photo alignment, day 1 manual roi, metal surface anomaly, glass defect, anomalygen finetune, setup_pcb, setup_metal, setup_glass, setup_pretrained, dig setup, dig datasets, dig pretrained checkpoint, dig image-edit endpoint, cosmos defect generation, cosmos-predict2 defect, cosmos-anomalygen, cosmos predict2 finetune.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.