'checkpoints' 검색 결과

승인을 기다리며 중단했다가 재개하는 처리나 여러 에이전트에 병렬로 작업을 배분하는 흐름을 저장형 템플릿 또는 스크립트로 작성합니다.

• 승인 대기가 포함된 처리 작성
• 여러 에이전트에 검토 작업 분담
• 정해진 형식으로 조사 결과 취합

며칠에서 몇 주에 걸친 목표를 단계별로 나누고 대화가 바뀌어도 진행 상황과 발견을 기록합니다. 매번 할 일을 계획하고 일정 지연과 초기 범위의 변화를 확인합니다.

• 여러 주에 걸친 기능 개발 진행 관리
• 조사 결과를 다음 대화로 이어가기
• 이전 계획의 지연과 범위 변경 확인

Learn to create, discover, invoke, secure, evaluate, package, and port Agent Skills through one interactive lesson at a time. Practical checkpoints and quizzes track demonstrated progress in a local file, so later sessions resume where you stopped.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Learn to build and invoke a minimal Agent Skill
• Understand discovery and automatic routing
• Check permission, sandbox, and trust boundaries

Breaks long-running goals into small, verifiable steps, tracks progress, and records evidence from actual operation. Checkpoints preserve what has been proven so work can resume after interruptions and continue toward explicit completion criteria.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Managing a long-running development goal
• Resuming work after an interruption
• Recording evidence for completion criteria

Organize treatment decisions already supplied and verified by authorized licensed professionals into local JSON documents. Check document structure, source references, declared review status, and supplied dates to support documentation handoffs without making clinical decisions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Organize verified clinician-authored treatment records
• Check source references and missing documentation fields
• List checkpoint dates already supplied by clinicians

Build and evaluate single-agent reinforcement learning experiments, where an agent learns actions from rewards. Use Stable Baselines3 to choose algorithms, create environments, monitor training, and save models with the normalization data needed for consistent evaluation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set up a reproducible reinforcement learning experiment
• Create and validate a custom Gymnasium environment
• Monitor training and save checkpoints

Work with Waypoint models to analyze microbiome samples, the communities of microbes in a sample. Convert taxonomic abundance tables, generate numerical sample representations, configure further training for prediction tasks, and evaluate models on the Compass benchmark.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Convert microbiome abundance tables into Waypoint inputs
• Generate sample vectors for clustering and visualization
• Configure further training with sample labels

Audit an AI agent's completion claims using existing logs, artifacts, approvals, and deployment records. Reconstruct the run and assess each claim separately, distinguishing proven results, partial support, contradictory evidence, and missing proof without rerunning the work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking whether an agent's completion report has supporting evidence
• Reviewing tool calls, retries, and checkpoint recovery
• Matching approvals and deployments to the same artifact

Set up Antigravity 2.0 Agent Manager alongside Antigravity IDE to coordinate parallel development agents. Assign roles and folder boundaries, review their changes in the editor, and use Git checkpoints to manage shared work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up Agent Manager alongside the IDE
• Assigning frontend, backend, and QA roles
• Separating editing scopes to avoid conflicts

Evaluate whether a fine-tuned model is ready for release by checking training data, general capability loss, comparisons with the base model, and staged production rollout. Produces an evidence-based promotion or rejection report with one prioritized remedy when rejected.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deciding whether to release a fine-tuned model
• Checking whether training reduced general capabilities
• Reassessing a model against updated reference evaluations

Design vocational training around a real work task, from preparation and equipment checks to operation, verification, and handover. Build interactive practice with tools, ordered steps, consequences for mistakes, and checkpoints where learners decide whether to proceed, recheck, or stop.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing hands-on vocational training
• Practicing equipment checks and operating steps
• Adding proceed-or-stop decisions for unsafe conditions

Design a slide-based lecture with sustained teacher narration, connected arguments, and recurring real cases. It organizes ideas into short sequences and places occasional application quizzes at transitions, helping learners understand a subject well enough to explain it.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning a systematic lecture on a subject
• Connecting a mechanism to recurring real cases
• Writing sustained narration that develops an argument

Provides prompts for assessing and planning .NET upgrades across multiple projects. Helps identify compatibility issues, order migration work, propose code and build pipeline changes, and prepare testing checklists, review guidance, and release documentation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing project structure and dependency compatibility
• Planning an incremental upgrade with rollback checkpoints
• Reviewing deprecated APIs and NuGet package replacements

Convert feature requirements and implementation plans into a test strategy, task checklist, and quality assurance plan for GitHub projects. Define test coverage, priorities, dependencies, estimates, and quality checkpoints using established testing and software quality frameworks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan testing from feature requirements and implementation documents
• Break testing work into tasks for GitHub issue management
• Prioritize performance, security, and accessibility checks

Guide a development brief through specification, dependent tickets, implementation, review, and verification. Coordinate parallel work while keeping human approval at key decisions and recording agreed vocabulary, business rules, and the reasons behind technical choices.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning a development brief into a specification and tickets
• Coordinating parallel implementation across dependent tickets
• Approving acceptance criteria and testing boundaries

Ask a more capable agent for a concrete execution plan, including step order, dependencies, and checks. The requesting agent then adapts and carries out the plan, with follow-up questions available to clarify individual steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Breaking a complex task into actionable steps
• Planning changes across multiple modules or systems
• Defining task order, dependencies, and checkpoints

Check training data and machine learning models for malicious changes, including backdoors that cause unexpected behavior on specific inputs. Combine data quality checks, model analysis, and file integrity verification, then document findings and retest after remediation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Screening third-party data before training
• Checking downloaded model weights before deployment
• Investigating unexpected behavior on specific inputs

Coordinates specialist agents in Claude Code to work from product requirements through design, implementation, testing, deployment, and operations. Includes task queues, saved progress, parallel reviews, and recovery workflows for managing extended development work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning product requirements into development tasks
• Coordinating implementation, testing, and reviews
• Resuming extended work from saved progress

Assess language models with over 60 academic benchmark tests using consistent prompts and scoring. Compare models, track performance during training, and organize research results with support for HuggingFace models, vLLM, and APIs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare models on standard knowledge and reasoning tests
• Track model performance across training checkpoints
• Organize evaluation results for research reports

Organize PyTorch training code with LightningModule and Trainer. Follow examples for validation, logging, model saving, and distributed training across multiple GPUs, with callbacks that add monitoring or stop training when progress stalls.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Converting an existing PyTorch training loop
• Adding validation, logs, and model checkpoints
• Configuring training across multiple GPUs

Process large email, document, and transcript archives by routing items to AI models according to their importance. Rule-based privacy checks run before model calls, while extracted information becomes linked knowledge pages with progress checkpoints.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extract useful information from large email archives
• Organize decisions and advice from meeting transcripts
• Route reports to suitable models by importance

Plan database, service, and infrastructure migrations with phased steps, compatibility checks, and rollback procedures for returning to the previous system. Define risks and validation checkpoints to guide transitions with minimal disruption to business operations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning a database schema migration
• Checking API changes for compatibility
• Preparing a phased cloud migration

Prepare a long play session with timed observation checkpoints and logs for memory growth, stability, and player fatigue. It creates the testing protocol and analysis template; a human plays the game and records the findings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare an endurance test before release
• Check for recurring memory issues after a fix
• Track crashes and performance over a long session

Review a development milestone using sprint reports, feature status, bugs, and risks. Summarize completion, quality, and delivery pace, then recommend whether to proceed, proceed with conditions, or stop, with scope choices and action items.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing progress before a milestone deadline
• Identifying blockers and quality risks
• Choosing features to protect or defer