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Plan systems conference papers with section page budgets, paragraph outlines, and writing patterns. Organize design choices, experimental evidence, and related work into a clear argument, then review the draft with submission checklists.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning sections and page budgets for a systems paper
• Explaining design choices and alternatives
• Organizing evaluation experiments and results

Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

Helps adapt machine learning training to multiple GPUs and machines with Ray Train. Covers resource allocation, recovery from saved training states, and Ray Tune searches that compare settings such as learning rate and batch size.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adapt existing PyTorch training to multiple GPUs
• Run HuggingFace training across multiple machines
• Compare learning rates and batch sizes with Ray Tune

Configure machine learning training and batch jobs across multiple clouds with SkyPilot. The skill guides GPU selection, lower-cost cloud and region choices, and automatic recovery when discounted spot instances are interrupted.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a lower-cost cloud for GPU training
• Run long training jobs with recovery after interruptions
• Configure distributed training across multiple machines

Guides setup and operation of Lambda Labs GPU instances for machine learning training and inference. Covers SSH access, persistent storage, API automation, and multi-GPU or multi-node training, including how to retain checkpoints and terminate idle instances.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up a GPU instance for LLM fine-tuning
• Keeping datasets and checkpoints between sessions
• Automating instance launch and termination through the API

Build infrastructure as code with Terraform across AWS, Azure, and GCP. Create reusable modules, manage state records that track resources, configure environments and providers, and validate proposed changes before applying an explicitly approved plan.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating reusable Terraform modules
• Migrating state storage or importing existing resources
• Managing settings across development and production

Configure Django file storage on AWS S3 using django-storages and boto3. Separate public and private files, create temporary download or upload links, configure CloudFront delivery, and test storage behavior without accessing real S3 buckets.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Moving Django static files and uploads to S3
• Separating public files from private documents
• Creating temporary download and direct upload links

Design cloud architectures and migration plans for AWS, Azure, and GCP. Produce service diagrams, selection rationale, security designs, cost estimates, and recovery strategies, with deployment and rollback plans that account for availability and operational monitoring.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design a cloud architecture and explain service choices
• Plan a cloud migration and cutover checks
• Review cloud spending and resource sizes

Create Dockerfiles, automated build and deployment pipelines, Kubernetes configurations, and infrastructure templates. The skill also prepares deployment checks, rollback procedures, and incident response runbooks to support application releases and production operations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up automated build and deployment pipelines
• Packaging applications with Docker
• Managing Kubernetes and cloud infrastructure as code

Prepare an isolated Python environment for SageMaker deployments and AWS automation. The skill selects a supported Python version, installs current AWS packages, and checks versions so installation and execution use the same environment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare a Python environment for SageMaker deployment
• Separate AWS automation dependencies from system Python
• Check installed AWS package versions

Prepare SageMaker deployments and training by finding existing execution roles, checking trust settings, and reviewing required permissions. Guides role creation only when necessary and identifies permission issues that require an AWS administrator.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check execution roles before deploying a SageMaker endpoint
• Find an existing role when no ARN is provided
• Investigate IAM AccessDenied errors

Check the AWS profile, region, account ID, and caller identity before starting AWS work. Read local configuration and validate credentials, then flag missing settings, expired access, or possible IAM restrictions associated with SSO authentication.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check the AWS account and region before deployment
• Identify the active profile when none was specified
• Detect expired credentials or missing configuration

Plan how to serve a model on Amazon SageMaker AI. The skill uses model type, traffic, response time, and cost constraints to recommend a deployment route, check instance quotas, and coordinate specialized setup and deployment skills.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan AWS hosting for a language model
• Choose a serving route for an embedding model
• Plan asynchronous inference for image generation

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

Deploy SageMaker endpoints, the services that answer model requests, with automatic capacity scaling, CloudWatch alarms, and resource tags. Supports real-time and asynchronous inference, scaling down to zero instances, deployment checks, and resource cleanup.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploying a real-time model endpoint
• Scaling a sparse workload down to zero instances
• Running long inference jobs asynchronously

Set up a litigation practice profile through an interview tailored to your role and the side you represent. Record risk preferences, writing conventions, and working context in CLAUDE.md for later litigation tasks, and check available integrations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set up a litigation plugin for the first time
• Tailor practice settings to your role and litigation side
• Update risk preferences and document style

Review AI vendor contracts against your organization's policies. Identify gaps in data training rights, liability, model changes, and other terms, then prepare targeted contract edits and escalation options for issues outside agreed limits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing an AI vendor's agreement before adoption
• Checking whether inputs may be used for model training
• Comparing contract terms with internal AI policies

Run OpenClaw tests and reproduce bugs on remote machines through Crabbox. Choose a suitable execution provider, verify realistic user workflows, and report the actual provider, environment ID, logs, and timing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run broad OpenClaw tests in a remote CI environment
• Reproduce installation or Docker bugs
• Verify behavior on macOS, Windows, or WSL2

Review fiction for problems in structure, characters, prose, and consistency. Receive prioritized suggestions supported by passages from the text, using multiple reviewers when available or a single reviewer when the required setup is unavailable.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing a chapter before publication
• Checking character motivations and setting consistency
• Prioritizing revisions across several chapters

Assess web applications and APIs for SQL injection, where input changes database queries. The skill covers finding vulnerable inputs, testing different database systems, automating checks, and documenting findings with remediation suggestions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing web application inputs for SQL injection
• Reviewing GraphQL and WebSocket injection risks
• Combining SQLmap automation with manual verification

Organize red team exercises that simulate adversaries to assess an organization's detection and response. Covers engagement rules, infrastructure, operational controls, evidence handling, and reports that help security teams review gaps and prioritize improvements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Defining red team objectives and engagement rules
• Designing an exercise that assumes an existing breach
• Organizing evidence and activity logs

Assess AWS, Azure, and GCP environments by identifying identities, permissions, and paths to broader access or data exposure. Covers cloud storage, secrets, cross-account trust, and CI/CD, the automated systems used to build and deploy software.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning an AWS, Azure, or GCP security assessment
• Tracing privilege escalation from current permissions
• Reviewing CI/CD credentials and OIDC trust settings

Assess Kubernetes security from an existing pod or API access point. Covers permission checks, exposed node services, secrets, host access, and cloud metadata to explain how configuration weaknesses can expand access across a cluster.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review service account permissions and RBAC settings
• Assess host access and secrets exposure from pods
• Trace access paths from a cluster to cloud infrastructure

Guides security testing of Android and iOS apps through file inspection and observation while the app runs. Covers network traffic, stored secrets, external app interfaces, authentication, and backend APIs, with tool examples and a testing checklist.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspecting APK or IPA files for endpoints and embedded secrets
• Observing app behavior and traffic with Frida
• Reviewing deep links and externally accessible components