'evaluate' 검색 결과

Guides fine-tuning and evaluation of OpenVLA-OFT robot models that turn images and language into actions. Covers LIBERO simulation, ALOHA deployment, LoRA adaptation that updates a small set of parameters, and troubleshooting action scaling or configuration mismatches.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Evaluate pretrained models in LIBERO
• Fine-tune robot action models with LoRA
• Deploy server-client inference for ALOHA

Use Phoenix to record execution traces, detailed records of an AI application's operations, and investigate problems. Evaluate answers on datasets, compare prompts and models, and set up monitoring through a self-hosted server.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate AI application problems using execution traces
• Evaluate answer quality across a test dataset
• Compare prompts and models through experiments

Evaluate NVIDIA Cosmos Policy for robot manipulation in LIBERO and RoboCasa simulations. Follow GPU environment setup, configure rendering without a display, run small checks before larger benchmarks, inspect success rates, and profile inference latency.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running a small LIBERO evaluation
• Evaluating kitchen tasks in RoboCasa
• Setting up simulation on a GPU cluster without a display

Evaluate language models on knowledge, math, coding, safety, and image understanding benchmarks. The skill guides reproducible runs in containers using local Docker, Slurm clusters, or cloud execution, with shared configurations for model comparison and result export.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing models on the same benchmark tasks
• Running a small evaluation sample before a full benchmark
• Running large evaluations on a Slurm cluster

Assess a fixed patch or pull request diff before merging. Trace affected behavior, evaluate regression risks, tests, and recovery options, then provide an evidence-based recommendation and advisory automatic-merge eligibility without modifying or merging the patch.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assess a generated patch before merging
• Trace how a pull request affects production behavior
• Evaluate tests and rollback options

Evaluate supplied security findings against repository code and security policy without running the software. Classify each claim as confirmed, not actionable, or needing review, then rank relevant findings by exploitability and explain evidence gaps and next steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assess findings from a vulnerability scanner
• Review security tickets from Jira or Linear
• Evaluate GitHub alerts and security advisories

Design and evaluate paid-ad experiments with clear hypotheses, comparison groups, sample requirements, and stopping rules. Check data quality before estimating effects, then deliver structured results and a plain-language decision that reflects uncertainty and the population tested.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning an A/B test for paid advertising
• Estimating sample requirements and test duration
• Setting success thresholds and stopping rules in advance

Evaluate vulnerability reports using seven practical criteria, including attacker capabilities, real usage, documented behavior, and remediation costs. Produce a reasoned decision to accept, dismiss, or request more evidence before committing to deeper investigation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Screening AI-generated security findings before human review
• Assessing whether a reported CVE warrants investigation
• Reviewing bug bounty submissions

Evaluate an AI product or feature idea by connecting customer problems, business outcomes, testable assumptions, risks, and differentiation. Produce a structured recommendation with lightweight experiments, success measures, and next steps for an investment decision.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deciding whether to invest in an AI feature
• Comparing AI solution options by outcomes and risk
• Preparing a proposal for executive or budget approval

Evaluate SaaS customer economics and cash efficiency using acquisition costs, customer lifetime value, margins, and payback periods. Compare segments and channels, estimate how long cash will last, and assess whether to expand spending or improve efficiency.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing customer economics before increasing paid acquisition
• Comparing acquisition costs and payback across channels
• Estimating cash runway and funding needs

Evaluate a proposed pricing change by modeling revenue per customer, conversion, cancellations, and customer acquisition cost recovery. Compare scenarios and receive a recommendation to implement, test, modify, or hold the change, with supporting calculations and risks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Evaluating a price increase for new customers
• Estimating revenue from a new premium tier
• Assessing an annual prepayment discount

Evaluate whether a product feature deserves investment by comparing revenue or retention benefits with development and ongoing costs. Receive calculations, payback estimates, and recommendations that account for strategic value and uncertain assumptions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Decide whether to build a feature this quarter
• Compare feature ideas by financial return and strategic value
• Explain roadmap priorities to stakeholders

Improve an evaluated Codex skill by turning plugin-eval findings into prioritized changes. Refine activation descriptions, shorten SKILL.md, move lengthy details into supporting files, fix structural issues, and compare evaluation results before and after the rewrite.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prioritizing fixes after a skill evaluation
• Shortening SKILL.md and reducing token use
• Clarifying when a skill should activate

Turn an idea, existing code, or a previous Codex conversation into a runnable OpenAI Agents SDK app. Supports local verification, focused evaluations of agent behavior, and local deployment through the Deployment Manager.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a runnable agent prototype from an idea
• Adapt existing code into an agent app
• Turn a previous Codex conversation into an app

Evaluate supplied security findings against repository code and security policy without running the software. Classify each claim as confirmed, not actionable, or needing review, then rank relevant findings by exploitability and explain evidence gaps and next steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assess findings from a vulnerability scanner
• Review security tickets from Jira or Linear
• Evaluate GitHub alerts and security advisories

Verify finished work against acceptance criteria using executable checks and a review of whether each requirement is met. Multiple models can review uncertain cases, and rejected work can enter an automated repair loop when the required Seed is available.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking finished work against its specification
• Verifying observed behavior against acceptance criteria
• Requesting multiple model reviews for uncertain results

Analyze TypeScript and JavaScript code for reusable design structures known as GoF patterns. Get detection reports, quality scores, and prioritized refactoring suggestions that account for the frameworks and libraries already used in the project.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find existing design patterns in a codebase
• Plan refactoring for complex conditionals
• Evaluate the quality of a pattern implementation

Review local skills for description quality, organization, writing style, and file structure. Get weighted scores, letter grades, and concrete improvement plans, or compare multiple skills to decide which issues to fix first.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing a skill before sharing it
• Prioritizing fixes for an existing skill
• Checking required fields and referenced files

Assess factual claims in AI-generated text by separating facts from opinions, choosing suitable independent evidence, and explaining what is supported or uncertain. Use it for a completed fact-check or an optional exercise in evaluating AI answers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fact-checking an AI-generated answer
• Choosing independent sources for a claim
• Identifying unverified numbers and assertions

Review local skills for description quality, organization, writing style, and file structure. Get weighted scores, letter grades, and concrete improvement plans, or compare multiple skills to decide which issues to fix first.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing a skill before sharing it
• Prioritizing fixes for an existing skill
• Checking required fields and referenced files

Assess a social science paper outline in a .qmd file for missing research questions, contributions, hypotheses, data, and analysis plans. Receive criterion ratings and concrete suggestions in a separate report, with causal assumptions assessed when relevant.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check whether a paper outline covers essential research elements
• Clarify contributions relative to previous studies
• Review causal assumptions in a research outline

Evaluate a paper’s novelty through related literature searches, match it to suitable fields and ABS journal rating levels, and recommend 20 submission targets. Produce a PDF report with journal details, selection reasons, and submission advice.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing a paper’s novelty with related literature
• Choosing suitable journal fields and ABS rating levels
• Comparing 20 submission targets and backup journals

Evaluate research papers and scientific claims by examining study design, statistics, bias, and evidence quality. Receive a structured critique of strengths, weaknesses, supported conclusions, and practical improvements, with uncertainty and limitations made explicit.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing a paper's methods and conclusions
• Assessing evidence quality across studies
• Checking scientific claims in media reports

Evaluate papers, research proposals, and literature reviews using structured criteria. Produces evidence-based feedback on research questions, methods, data, analysis, and writing, with optional scores and prioritized recommendations tailored to the work's stage and purpose.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing a paper before submission
• Assessing a research proposal's methods and feasibility
• Checking the coverage and synthesis of a literature review