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Create and run evaluations in Arize using an LLM as the judge. Define scoring criteria, connect them to actual data fields, evaluate recorded operations or experiment results, and set up continuous scoring for new data.
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Find and evaluate a small, compatible set of AI Agent Skills for a multi-step goal. Check existing capabilities, compare candidates for fit and safety, identify overlaps, and prepare recommendations with installation subject to user approval.
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Evaluate investment funds and ETFs using returns, risk, fees, investment style, and manager performance. Detect changes in style, compare how closely ETFs follow their indexes, and plan portfolios that invest in multiple funds.
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Assess a company’s management through public statements, delivered commitments, crisis decisions, and use of company funds. Produce evidence-based scores for integrity, strategy, capital allocation, and governance, alongside a verdict on long-term trust.
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Evaluate threat intelligence platforms, which collect and organize cyber threat information. Define requirements, compare integrations and operating costs, plan practical trials, and use weighted scoring to select a platform and outline its rollout.
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Plan authorized voice phishing assessments to evaluate how employees verify callers and report suspicious calls. Organize approved scenarios, record assessment outcomes, and summarize disclosure, verification, and reporting rates to review security awareness training.
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Test iOS apps while they run using Objection and Frida, tools for inspecting and changing runtime behavior. Examine stored credentials, files, memory, and authentication calls, and evaluate client-side protections during authorized security assessments.
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Plan and evaluate free calculators, generators, or assessment tools for marketing. Compare ideas by audience needs, search demand, and development effort, then outline essential features, email signup options, promotion, and success metrics.
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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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Evaluate code generation models with HumanEval, MBPP, and other benchmarks. Test generated programs, compare models across programming languages, and record pass@k scores, which estimate the chance that at least one of k solutions passes the tests.
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Choose research problems, refine new ideas, or rethink stalled projects through structured conversation. Assess feasibility, potential impact, and risks, then develop success criteria, decision points, and backup plans for a clearer research strategy.
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Review completed AI work by scoring task difficulty and execution quality separately. A fixed scoring matrix, arguments for higher and lower ratings, and saved score history help examine weaknesses and detect repetitive scoring patterns across sessions.
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Evaluate changes to OpenSEO skills by running fresh Codex sessions against a local backend. Compare saved reports using weighted criteria, then inspect tool logs to understand failures and check whether revised instructions improve output.
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Assess existing Google Cloud folders and projects through read-only discovery. Compare resource locations, service usage, and customer-managed encryption requirements with Assured Workloads frameworks, then report suitable options and policy readiness findings.
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Evaluate Google Cloud workloads and develop reliability recommendations covering user experience goals, redundancy, monitoring, and recovery. The skill applies the Google Cloud Well-Architected Framework to help identify gaps and plan improvements suited to business requirements.
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Evaluate Google Cloud workloads against built-in best practices or custom organizational rules using Workload Manager. Use public client libraries or the REST API to run checks, review violations and scanned resources, and export results to BigQuery.
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Reviews Google Cloud spending and resource usage to identify waste, recommend suitable resource sizes, compare discount options, and establish ongoing cost management using the cost optimization guidance in the Google Cloud Well-Architected Framework.
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Evaluate whether Google SecOps rules detect threats described in articles or supplied text. Generate simulated attack logs, identify coverage gaps, summarize matching rules, and create rules for uncovered threats, with user approval before adding them.
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Review BigQuery capacity, table layouts, storage retention, and SQL to improve cost efficiency. Compare billing options, plan partitioned tables, and evaluate search indexes, stored aggregation results, and BI Engine for suitable workloads.
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Evaluate AI agent outputs with code checks and model-based grading. Run repeated trials to measure reliability, compare prompts or models, and check whether changes reduce quality, with saved transcripts for reviewing the results.
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Build repeatable DeepEval tests for AI agents, chatbots, and applications that retrieve documents to answer questions. Reuse or generate evaluation datasets, inspect failures, and improve prompts, tools, or retrieval through repeated evaluation runs.
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Evaluate whether completed experiments support a research claim using a separate Codex reviewer. Identify unsupported conclusions and missing evidence, then determine whether to revise the claim, run additional experiments, or move toward paper writing.
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Evaluate whether completed experiments support a research claim by checking cited evidence and requesting a Codex review. Identify unsupported conclusions, missing comparisons, and needed revisions, then determine whether to add experiments, change direction, or prepare for writing.
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Set up a CARLA driving evaluation environment for Leaderboard 1.0, 2.0, or 2.1. Match the simulator and scenario_runner versions, configure Python paths, and verify that the required components load correctly.
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