'ept' 검색 결과

Check your understanding of code you recently wrote, edited, or accepted from AI. Answer questions one at a time, receive explanations, and finish with a score, strengths, review topics, and suggested practice.

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• Checking understanding of code accepted from AI
• Reviewing error handling in recent code
• Explaining trade-offs through harder questions

Generate a report with predictable output for acceptance tests of disposable executors, or temporary task runners. Retrieve agent settings, run the supplied report script with specified arguments, and publish the resulting file to S3.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generating a report for disposable executor acceptance tests
• Checking report output with specified arguments
• Publishing a verification report to S3

Develop and fix Nexent features by connecting written specifications, acceptance criteria, implementation, and verification evidence. Plan unit tests and appropriate staged checks, maintain their links to requirements, and fill confirmed test gaps after development.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning and implementing features from acceptance criteria
• Fixing bugs using existing specifications and tests
• Refactoring while preserving required behavior

Prepare conference slides, academic posters, and research promotion drafts from an accepted paper. The skill guides presentation timing, visual layout, and platform-specific writing while keeping claims and links tied to verified research materials.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing slides for a conference talk
• Designing an academic poster
• Drafting research posts for Twitter/X or LinkedIn

Turn ready project tasks into clear instructions for AI workers, with defined ownership, deliverables, acceptance criteria, and reporting rules. Handle assignment notifications and check submitted results before passing them to the project management workflow for acceptance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Writing instructions for ready project tasks
• Defining deliverables and acceptance criteria
• Checking submitted worker results

Plan how multiple agents should work together. Choose between dependency-based tasks and repeated work cycles, define ownership and acceptance criteria, identify safe parallel work, and decide how to handle results, interruptions, or revised goals.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Dividing work among multiple agents
• Choosing dependency plans or repeated cycles
• Defining result acceptance criteria

Manage an AI team's project plans and recorded progress, including task dependencies and repeated improvement cycles. Track accepted results, identify work ready to start, pause or resume projects, and prepare status reports for requesters.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning tasks with dependencies
• Tracking repeated improvement cycles
• Updating progress after accepting results

Plan work across several AI agents by defining task owners, deliverables, dependencies, and acceptance criteria. Choose a finite workflow or repeated improvement cycles, then review results before allowing dependent tasks to proceed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Divide a project among several AI agents
• Choose between a planned workflow and repeated improvement
• Define acceptance criteria for each deliverable

Turn concepts, processes, and system architecture into diagrams. Create a self-contained HTML file with SVG graphics for browser viewing, or an editable Excalidraw file for whiteboard sketches and flowcharts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Illustrating concepts and processes for teaching
• Mapping software and cloud architecture
• Creating editable whiteboard sketches

Test APIs and forms with unexpected input to uncover error responses that expose internal details. The skill checks response bodies for stack traces, server paths, and library information, preserving specific evidence rather than treating every server error as a vulnerability.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking JSON API error handling
• Testing form and query parameter boundaries
• Identifying internal details in error responses

Create Excalidraw diagram files that explain workflows, architectures, and concepts through visual relationships. Technical diagrams include concrete code or data examples, while a rendering and review process checks readability, connections, spacing, and overall composition.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Visualizing workflow steps and decision points
• Explaining system architecture with code and data examples
• Illustrating abstract concepts and relationships

Prepare conference slides, academic posters, and research promotion content after paper acceptance. The skill guides talk structure, visual layout, timing practice, anticipated questions, and drafts for Twitter/X, LinkedIn, and a broader-audience blog post.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Structure conference slides around the available speaking time
• Plan an academic poster layout
• Draft research posts for Twitter/X, LinkedIn, and a blog

Compare text and metadata extracted by two Tika builds from the same file collection. Generate reports to investigate content differences, encoding changes, new exceptions, and missing embedded documents, then repeat the evaluation after fixes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing extracted content before and after a Tika change
• Checking whether parser changes lose attachments
• Investigating new exceptions and large content differences

Manage long or multi-part agent tasks by defining observable completion criteria before work begins. Break work into deliverables, run approved checks, and verify recorded evidence again before reporting which requirements are met or still need attention.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Defining completion criteria for long autonomous tasks
• Finding omissions in work returned half-finished
• Coordinating parallel deliverables and integration checks

Plan ChatGPT advertising experiments and conversational ad concepts, or review performance using a verified data export. The skill helps assess campaigns without assuming that advertising inventory, account access, or particular platform features are available.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning a ChatGPT advertising experiment
• Developing conversational ad concepts
• Reviewing performance from a verified export

Turn a plain language feature request into spec.md with user stories, testable requirements, and acceptance criteria. Create a feature branch and a separate quality checklist, documenting assumptions and clarifying important gaps before planning begins.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning a new feature idea into a specification
• Refining vague requests into testable requirements
• Defining acceptance criteria and measurable outcomes

Process mass spectrometry data in Python with PyOpenMS. Read common file formats, smooth and normalize signals, detect peaks, align samples, and process peptide identification results for protein and metabolite quantification workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reading mzML files and inspecting spectra
• Smoothing and normalizing mass spectrometry signals
• Aligning and comparing metabolite peaks across samples

Generate photos, illustrations, concept art, and presentation assets from text, or edit existing images with specific instructions. Choose among supported OpenRouter models and save the result as a PNG file at a specified location.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating backgrounds and illustrations for presentations
• Generating images for websites and marketing materials
• Changing backgrounds or elements in existing images

Learn programming through tutorials tailored to your experience, goals, and actual project code. Keep a shared learning history across projects, record questions, and use quizzes with spaced review to revisit concepts based on demonstrated understanding.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Learning concepts through code from an existing project
• Choosing what to study next based on previous learning
• Reviewing weak concepts through quizzes

Check Jetson video encoding and decoding capabilities by comparing live API reports, verified NVIDIA samples, and official documentation. Distinguish documented product support from observed behavior for specific formats, bit depths, dimensions, and tested operations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking support for video formats and bit depths
• Comparing API reports with official product documentation
• Verifying encoding and decoding under specific conditions

Prepare BOP datasets and run depth estimation, image segmentation, and FoundationPose to estimate object positions and orientations. Evaluate completed runs, compare accuracy and coverage, or change scoring settings without repeating inference.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adapt a BOP dataset and test a single scene
• Estimate object poses without ground-truth annotations
• Re-score a completed run while preserving its original report

Turn written planning problems into mathematical optimization models. Separate fixed inputs, constraints, decisions, and objectives, then choose linear, mixed-integer, or quadratic formulations for tasks such as production planning, inventory management, and material allocation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Formulate production plans with capacity and cost constraints
• Model inventory limits across purchasing and sales periods
• Define multiple goals in priority order

Real-time depth map privacy transforms using Depth Anything v2 (CoreML + PyTorch)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Peptide-spectrum matching and protein identification from MS/MS data. Use when identifying peptides from tandem mass spectra. Covers database searching, spectral library matching, and FDR estimation using target-decoy approaches.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.