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调查预测市场价格所隐含的概率，记录来源与时间，并结合交易深度、结果判定规则和其他信息，评估其能否用于产品或业务决策。

• 评估产品采用预测市场数据的可行性
• 比较市场概率与内部指标
• 考虑用于智能体记忆或通知

制作Excel工作簿，比较类似企业的收入、利润率及估值倍数。汇总公式、中位数、四分位数和数据来源，为投资或收购评估准备可更新的比较资料。

• 与同行比较企业价值
• 制作收购评估比较资料
• 比较行业增长与盈利能力

从Caveman观测报告出发，针对用户选择的对象提出最小代码改动。经批准后，以相同输入比较改动前后的质量和资源用量，为是否采用提供依据。

• 查看Caveman观测报告
• 比较改动前后的词元数或字节数
• 确认优化后是否保持质量

Create and run automated tests that score LLM answers against expected outputs or judging criteria. Monitor progress, inspect individual results, compare models on the same tests, and use score thresholds in CI, an automated checking workflow.

中文简介正在准备中。当前显示英语简介。

• Creating tests with expected answers
• Monitoring evaluation progress
• Inspecting failed samples and scores

Evaluate an Agent Skill bundle before release by checking structure, activation conditions, output quality, scripts, safety, file integrity, and host compatibility. Receive a detailed JSON report that distinguishes local evidence checks from production readiness backed by external attestation.

中文简介正在准备中。当前显示英语简介。

• Reviewing a skill bundle before distribution
• Evaluating activation accuracy and output improvements
• Checking scripts, safety, and distributed files

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.

中文简介正在准备中。当前显示英语简介。

• Compare a generated point cloud with reference data
• Measure errors in estimated movement trajectories
• Check ground segmentation against quality thresholds

Design evaluation tasks that check whether agents actually complete work. Define requests, simulated user behavior, starting conditions, and verifiable success criteria, then plan difficulty levels, training-data leakage checks, and ongoing maintenance.

中文简介正在准备中。当前显示英语简介。

• Creating evaluation tasks for real business workflows
• Reviewing success criteria and verification logic
• Designing checks for evaluation data leakage into training

Forecast sales and other time series with Google’s TimesFM without task-specific training. Prepare regularly spaced CSV data, export predicted values and uncertainty ranges, add explanatory variables, and evaluate forecasts against held-out observations.

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• Forecast monthly sales or revenue from CSV data
• Export predicted values with uncertainty ranges
• Forecast with additional explanatory variables

Use PyTDC to discover therapeutic machine-learning datasets, create task-appropriate training and test splits, and evaluate predictions against curated benchmarks. Also score selected molecules with supported scoring functions, with explicit download plans and bounded outputs.

中文简介正在准备中。当前显示英语简介。

• Find datasets for therapeutic machine-learning tasks
• Plan and create task-appropriate data splits
• Evaluate predictions with benchmark-specific metrics

Plan and review PufferLib reinforcement learning workflows, where agents learn through rewards. Covers version-specific environment checks, parallel execution, training and evaluation plans, plus checkpoint inspection before loading saved models.

中文简介正在准备中。当前显示英语简介。

• Choose APIs for a specific PufferLib version
• Check environment behavior before training
• Create training and evaluation plans with separate seeds

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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• Prototype mortality and readmission prediction models
• Check patient splits and data leakage
• Train and evaluate sleep staging models

Prepare and check research documents for clinical decision-support evaluation, including evidence profiles, cohort tables, survival plans, and model assessments. Work with aggregate or synthetic inputs, record sources and review status, and keep outputs outside patient care.

中文简介正在准备中。当前显示英语简介。

• Preparing research model evaluation documents
• Checking evidence profiles and source links
• Reviewing survival analysis plans

Organize repeated experiments to improve models, prompts, or agent systems against measurable goals. The skill records hypotheses and lessons in a persistent tree, isolates trials, and compares promising candidates using evaluation data kept separate from development.

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• Compare repeated experiments to improve model training
• Improve an agent system's measured success rate
• Test prompt changes against a fixed evaluator

Implement .NET workflows for Azure AI Foundry projects, including agent creation, resource connections, dataset uploads, model deployment inspection, search index management, and evaluations. Examples also cover authentication, asynchronous operations, and resource cleanup.

中文简介正在准备中。当前显示英语简介。

• Creating and running agents from a .NET application
• Inspecting resource connections and model deployments
• Uploading datasets and managing search indexes

Implement Java workflows for Azure AI Foundry projects using SDK examples for authentication, connected resources, datasets, search indexes, and model evaluations. The skill also covers client setup, paginated resource lists, and error handling.

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• List connected Azure resources
• Create or update search indexes
• Implement access to model evaluations

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.

中文简介正在准备中。当前显示英语简介。

• Plan an agent deployment on AgentCore
• Expose a REST API as an agent tool through MCP
• Configure credentials and access controls

Compare prompt revisions on a fixed set of test cases to identify regressions, meaning behavior that gets worse after a change. Record model settings, case-level evidence, missing results, costs, and evaluation limits for a reviewable decision.

中文简介正在准备中。当前显示英语简介。

• Comparing original and revised prompts
• Checking required JSON fields after prompt changes
• Reviewing tool selection and task completion

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.

中文简介正在准备中。当前显示英语简介。

• Compare agent behavior before and after prompt changes
• Reproduce an observed failure with preserved evidence
• Run regression tests after a bounded fix

Design criteria for using an LLM to judge AI responses through scoring or comparisons between two answers. Includes checks for order and length bias, plus validation against human judgments to assess evaluation reliability.

中文简介正在准备中。当前显示英语简介。

• Build an automated evaluation pipeline for AI responses
• Compare responses after changing a prompt or model
• Define shared scoring criteria for reviewers

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.

中文简介正在准备中。当前显示英语简介。

• Compare agent quality before and after prompt changes
• Test tool selection and multi-step task execution
• Evaluate resistance to prompt injection

Design backtests that evaluate trading strategies against historical data. Account for trading costs, avoid using future information or excluding delisted securities, and compare strategies using separate development and evaluation periods.

中文简介正在准备中。当前显示英语简介。

• Build a backtesting system for trading strategies
• Check for bias in historical performance estimates
• Compare strategies after accounting for trading costs

Explains how PluginEval combines structural checks and experimental model assessments into quality scores. Helps interpret low scores, identify improvement areas, set marketplace thresholds, and explain what evaluation badges actually represent.

中文简介正在准备中。当前显示英语简介。

• Investigating why a skill received a low score
• Improving descriptions that define when a skill should run
• Setting score thresholds for marketplace listings

Prepare an evaluation framework before fine-tuning a language model. Build labeled examples, checks for each failure type, and a base-model benchmark, while calibrating model-based judges against human labels to support comparisons after training.

中文简介正在准备中。当前显示英语简介。

• Build an evaluation set from agent traces
• Create separate checks for each failure type
• Calibrate an LLM judge against human labels

Turn graded execution traces and production logs into model training examples and preferred-versus-rejected response pairs. Select useful records, remove sensitive information, exclude evaluation examples, and preserve links to the original runs.

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• Convert high-scoring successful runs into training examples
• Use expert-corrected failures as training examples
• Pair passing and failing responses to the same task