“evaluate”的搜索结果

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.

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

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.

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

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.

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

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.

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• 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.

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• 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.

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• 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.

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• 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.

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

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.

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

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.

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• 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 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.

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• 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.

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

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.

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

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.

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

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.

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• 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.

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• 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.

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• 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.

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• 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.

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

This skill should be used when the user asks to "analyze skill quality", "evaluate this skill", "review skill quality", "check my skill", or "generate quality report". Evaluates local skills across description quality, content organization, writing style, and structural integrity.

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Assesses a research paper outline (.qmd file) against structured criteria from Kosuke Imai's empirical research guide. Evaluates the abstract and section outline for: research question clarity, stated contributions, hypotheses and testable implications, data description, planned results and inferential approach, and robustness checks. Detects causal papers from the abstract and conditionally assesses identification strategy only when causal framing is present. Produces a structured markdown report with PASS/PARTIAL/FAIL assessments and actionable suggestions. Use when asked to assess, evaluate, review, or check a paper outline, abstract-and-outline, or research plan in a .qmd file.

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Assess whether source code is ready to deploy to Azure — the check BEFORE infrastructure work. Evaluates build health, app completeness, dependencies and local services, stack compatibility, and deployment feasibility. Answers questions about what your app needs before it can be deployed — frameworks, dependencies, and configuration. Checks whether dependencies are compatible and identifies deployment blockers and unsupported frameworks. WHEN: "evaluate my repo", "is my app ready to deploy", "what does my app need to deploy", "what do I need before deploying", "does my app need", "can I ship this to Azure", "scan my repo for issues", "is this app deployable", "check if my app is ready for Azure", "do I need a Dockerfile", "what's blocking my deployment", "are there any blockers", "are my dependencies compatible", "does Azure support my framework", "what needs to change before deploying", "check my app configuration".

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Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, managed harness agent create, GitHub Copilot harness agent, create agent, update agent, add tool to agent, invoke agent, agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

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Evaluates where to spend limited capital — investment appraisal, hurdle rates, payback, and comparing proposals that are not alike. Use this to evaluate an investment or major purchase, compare competing funding requests, set a hurdle rate, decide between building and buying, or review whether past investments delivered what was claimed.

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