'ner' 검색 결과

Create and prepare marketing images for blogs, social posts, product showcases, and banners. Choose suitable generation or design tools, plan prompts and layouts, and optimize image dimensions, formats, and file sizes for web use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating blog headers and social sharing previews
• Preparing social graphics and profile banners
• Building product mockups from real screenshots

Identify runtime signs of attackers breaking out of Docker or Kubernetes containers. Review namespace changes, privilege abuse, sensitive host access, and unusual system calls to guide incident investigations and compare monitoring coverage across security tools.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating a suspected Docker or Kubernetes container escape
• Choosing runtime behaviors to monitor for escape attempts
• Comparing escape detection coverage across security sensors

Add Google Mobile Ads banners to Android, iOS, or Unity apps. Guides implementation from defining the ad view and choosing its size to handling load events, loading the banner, and checking the completed integration.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add a banner ad to an Android app
• Place an adaptive banner at the top or bottom of an iOS app
• Integrate a banner ad into a Unity app

Automate Docugenerate operations through Rube MCP, a connection for agent tools. The skill guides tool discovery, connection checks, and execution using current input requirements, with guidance for bulk operations and paginated results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find available tools for a Docugenerate task
• Check the connection before running operations
• Run bulk operations with the discovered tools

Automate Campaign Cleaner operations through Rube MCP and Composio. The skill discovers available tools, checks the service connection, and uses current input requirements to carry out the requested workflow.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding tools for a Campaign Cleaner task
• Checking the service connection before execution
• Automating a workflow with multiple operations

Generate synthetic body MRI volumes, or three-dimensional images, with NV-Generate-CTMR rflow-mr. The skill stages configuration settings, runs the existing inference workflow, and records output geometry and data checks alongside the generated images.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate synthetic body MRI volumes for experimentation
• Try supported MRI modalities such as T1 and T2
• Compare outputs with different settings and random seeds

Generate synthetic 3D brain MRI volumes with NV-Generate-CTMR MR-Brain v1, choosing scan types such as T1 or FLAIR. The wrapper records generation settings and output checks for experimental review, with restrictions on clinical and production use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generating synthetic T1 or T2 brain MRI volumes
• Trying FLAIR or SWI generation settings
• Specifying a random seed for generation

Best practices for Docker-based ROS2 development including multi-stage Dockerfiles, docker-compose for multi-container robotic systems, DDS discovery across containers, GPU passthrough for perception, and dev-vs-deploy container patterns. Use this skill when containerizing ROS2 workspaces, setting up docker-compose for robot software stacks, debugging DDS communication between containers, configuring NVIDIA Container Toolkit for GPU workloads, forwarding X11/Wayland for rviz2 and GUI tools, or managing USB device passthrough for cameras and serial devices. Trigger whenever the user mentions Docker with ROS2, docker-compose for robots, Dockerfile for colcon workspaces, container networking for DDS, devcontainer for ROS2, multi-stage builds for ROS2, or deploying ROS2 in containers. Also trigger for CI/CD with Docker-based ROS2 builds, CycloneDDS or FastDDS configuration in containers, or shared memory in Docker. Covers Humble, Jazzy, and Rolling distributions across Ubuntu 22.04 and 24.04 base images.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

copy_tuner（CopyTuner / copy_tuner_client）で集中管理している i18n データを、prefix（正規表現）単位で Rails 標準の config/locales（YAML）管理へ移すスキル。gem の local_first_key_regexp を使うので、 gem を残したまま特定 prefix だけをローカル管理にできる（部分ローカル化）。1 回の実行で 1 prefix。 全 prefix を移して gem ごと撤去したい場合は繰り返し、完了後 copy-tuner-to-locales-cleanup スキルへ進む。 対象 prefix はスキル引数で指定でき、未指定なら export を俯瞰して選定する。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Security-Scanner Agent fuer fabrikIQ und andere Projekte. Fuehrt umfassende Sicherheitspruefungen durch. Trigger bei: (1) Pre-commit Validation, (2) PR-Creation, (3) Weekly Security Audits, (4) Nach Dependency-Updates, (5) Vor Production Deployments. <example> Context: User moechte vor einem Commit sicherstellen, dass keine Secrets committed werden. user: "Ich will gleich committen. Pruefe mal auf Secrets." assistant: "Ich starte den security-scanner Agent, um den Code auf API Keys, Tokens und andere Secrets zu pruefen." <Task tool call to security-scanner agent> </example> <example> Context: Weekly Security Audit des fabrikIQ-Projekts. user: "Fuehre den woechentlichen Security-Scan durch." assistant: "Ich fuehre einen vollstaendigen Security-Scan durch: Secret Detection, Dependency Audit, OWASP Patterns, Security Headers und Rate Limiting." <Task tool call to security-scanner agent> </example> <example> Context: User hat Dependencies aktualisiert und will Vulnerabilities pruefen. user: "Ich habe npm update und pip install -U gemacht. Gibt es Sicherheitsprobleme?" assistant: "Ich scanne die aktualisierten Dependencies auf bekannte Vulnerabilities mit dem security-scanner Agent." <Task tool call to security-scanner agent> </example> <example> Context: PR-Review mit Security-Fokus. user: "Review den PR #42 auf Security-Probleme." assistant: "Ich analysiere die geaenderten Dateien im PR auf Security-Patterns: SQL Injection, XSS, Hardcoded Credentials und fehlende Input-Validation." <Task tool call to security-scanner agent> </example>

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Apple의 FoundationModels로 iOS 26 이상 앱에 기기 내 언어 모델을 통합합니다. 글 생성·요약, 데이터 추출, 앱 기능 호출과 생성 중 화면 갱신을 다룹니다.

• 오프라인으로 글을 생성하고 요약합니다
• 입력 문장에서 필요한 항목을 추출합니다
• AI가 앱의 자체 기능을 호출하게 합니다

Use Hugging Face Transformers to load pretrained models, generate text, and classify text, images, or audio. Prepare model inputs, control generation settings, and fine-tune models on custom datasets with the Trainer API.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prototype text, image, or audio classification
• Configure text generation and conversational responses
• Fine-tune a model on a custom dataset

Assess software dependencies, build pipelines, and container images for security risks. Create a software component inventory, check vulnerabilities and license conflicts, verify artifact signatures, and prioritize fixes based on whether vulnerable code can actually be reached.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating and auditing a software component inventory
• Prioritizing dependency vulnerabilities by actual exposure
• Auditing CI/CD pipelines and secret handling

Build a new classroom course through an agreed page plan, teacher and learner personas, ordered page generation, and final checks. It defines when pages and narration audio are complete and how to handle generation failures.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a new classroom course from scratch
• Setting teacher and learner personas before generating pages
• Checking saved pages and narration audio for completeness

Guide changes and reviews for SGLang’s Scheduler, TokenizerManager, and ModelRunner classes. Keep initialization in small, replaceable helpers and separate computation from coordination so large classes retain clear responsibilities and explicit dependencies.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing changes to SGLang’s three large classes
• Splitting initialization into replaceable helpers
• Moving computation out of ModelRunner

Create and tune Falco rules to detect possible container escapes, where a container gains access to its host. Define detection conditions, alert priorities, and output fields, then refine exceptions to reduce false positives.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Writing rules for suspicious host filesystem access
• Investigating noisy container escape alerts
• Reusing detection conditions through macros and lists

Investigate compromised Docker containers by preserving evidence and examining image layers, logs, file changes, and host settings. Summarize suspicious activity, security configuration issues, and vulnerability findings to support incident response.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating a compromised application container
• Examining a suspicious image from a registry
• Checking evidence of a container escape attempt

Compare direct and partner-led sales using full service costs and three views of return on investment. Produce channel verdicts and a recommended sales mix, testing how changes in acquisition costs or partner discounts affect the result.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing direct and partner sales profitability each quarter
• Assessing the cost of hiring a channel manager
• Explaining returns on partner marketing funds to the board

Generate text and images through a reverse-engineered Gemini Web API, an interface reconstructed from the web service. Use reference images for descriptions or variations, and continue conversations across requests with saved sessions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generating images from written prompts
• Describing the contents of reference images
• Creating variations from an existing image

Generate images from text or reference images using multiple image services. Choose aspect ratios, sizes, and quality settings, or generate a collection of images in batches from saved prompt files.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create illustrations from text descriptions
• Change scenes using reference images
• Generate images in specific aspect ratios

Plan and manage Cloud Spanner databases on Google Cloud, design tables and primary keys, write SQL and client code, and investigate slow queries using database statistics to identify resource-intensive operations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create Cloud Spanner instances and databases
• Design primary keys and related tables
• Write SQL queries and client library code

Import RoadRunner geometry and road data into a CARLA source build, then verify the map loads and supports driving. Handle tiled maps and generate and inspect pedestrian navigation areas for standard maps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Importing a custom RoadRunner map into CARLA
• Importing a large city split into tiles
• Rebuilding navigation areas for missing pedestrians

Create custom traffic scenarios for CARLA ScenarioRunner with a Python class and XML configuration. Generate a starting skeleton, define actor behavior and pass/fail criteria, and check the files before running them in a simulator.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a scenario where a leading car brakes hard
• Compare scenarios with different distance parameters
• Add a custom scenario to a Leaderboard route

Choose a serving container for models deployed on SageMaker. Match the model type to a compatible image, retrieve its address from the AWS catalog, and check regional availability, CPU or GPU variants, and required host settings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a container for an LLM deployment
• Select images for embeddings and rerankers
• Check regional image addresses and GPU compatibility