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Builds repeatable evaluation systems with golden datasets, scoring rubrics, pass/fail thresholds, and regression reports. Use for "LLM evaluation", "testing AI systems", "quality assurance", or "model benchmarking".
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使用统一标准审阅论文和研究计划，检查研究方法、证据、引用及文章结构，并说明评分依据、修改优先级和下一步需要修改的内容。
为尚未使用机器学习的现有应用梳理推荐、分类、预测等功能的引入步骤，从明确问题、检查数据到初始模型的训练、评估和集成。
将应用开发分配给规划、实现和评估代理，检查运行中的界面并按质量标准评分，把具体问题反馈给实现环节，反复修改直至达标或达到次数上限。
帮助设计让人工智能智能体持续工作的循环，结合质量检查、评估与失败后的停止和重试规则，处理进度停滞、反复失败及成本增长问题。
为主要由AI智能体实施的开发工作制定完成条件，拆分可独立验证的任务，并比较实施前后的评估结果；同时按难度选择模型，记录费用、耗时与重试情况。
使用统一任务评估大语言模型的知识、推理和代码生成能力，生成多模型对比表及训练阶段的成绩趋势，并保存评估结果和每道题的回答。
依据按编程语言分类的资料，协助开发和维护Claude应用。涵盖官方SDK调用、工具集成、模型迁移、费用检查、提示词审查和评估流程。
支持开发让 AI 使用外部服务的 MCP 服务器，从 API 调研、工具设计与实现，到功能检查和评估题编写，帮助检验多次工具调用下的实际使用效果。
训练并评估从WiFi信号估计人体姿态的模型。将结果与始终输出平均姿态的基准模型比较，检查训练数据是否混入评估数据，并按实际测量条件报告改进幅度。
Review existing DeerFlow skill packages for publication readiness, activation rules, safety boundaries, supporting files, and evaluation evidence. Receive concrete findings and suggested replacement text, with a clear distinction between inspection results and verified runtime behavior.
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Build MCP servers, which give AI agents tools for external services, using Python or TypeScript. The skill guides tool design, input validation, error handling, code review, and evaluations based on realistic tasks.
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Evaluate 3D point clouds and movement trajectories against reference data with CloudAnalyzer. Prepare inputs, measure differences, apply quality thresholds, inspect results visually, and track baseline history for mapping, localization, and perception workflows.
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Build and evaluate time-to-event models with scikit-survival, including records whose events remain unobserved when follow-up ends. Keep preprocessing separate from evaluation data, compare models, predict survival probabilities, and summarize censoring-aware metrics.
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Develop and prioritize early research ideas through independent brainstorming, structured discussion, evidence checks, and critical review. Keep assumptions, uncertainty, dissent, and scoring reasons visible, then record the human decision and appropriate next steps.
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Combine laboratory animal welfare measurements into reference-relative RELSA severity scores. Analyze individual trajectories, explore forecasts for specified observation times, and derive candidate score zones, with explicit checks on normalization, reference selection, prediction intervals, and evaluation limits.
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Build and check clinical machine learning workflows with PyHealth, from loading electronic health records, signals, or images to training and evaluation. Includes patient-level data splits, prediction task checks, model checkpoints, and medical code mapping.
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Build Python AI applications on Microsoft Foundry with azure-ai-projects. Provides examples for creating agents, connecting tools, managing conversations and project resources, and running evaluations through Foundry and OpenAI-compatible clients.
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Deploy and operate AI agents with AWS Bedrock AgentCore. Choose services for hosting agents, connecting external APIs, managing credentials and conversation state, monitoring activity, and assessing agent quality through automated evaluations.
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Plan and build AI agents through selected stages of design, implementation, tool integration, memory, and evaluation. The workflow points to specialized skills for single agents, coordinated agent teams, and stateful workflows using CrewAI or LangGraph.
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Build repeatable evaluations for AI agent responses, tool choices, and multi-step tasks. Use reference datasets and scoring criteria to compare changes, detect quality regressions, and set automated checks that must pass before deployment.
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Compare agent, prompt, or tool configuration changes using versioned test cases and explicit outcome checks. Record each attempt, distinguish agent failures from evaluation or infrastructure problems, and report regressions, permission violations, and uncertainty.
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Configure supervised fine-tuning for models that process images and text. Define which components remain unchanged, set LoRA adapters for updating selected model weights, choose image resolution limits, and investigate training runs whose evaluation results fail to improve.
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Investigate LLM application failures with Phoenix CLI by reviewing execution records, recording observations, and grouping mistakes into counted categories. Inspect datasets and experiments to identify which failure types deserve evaluation tests first.
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