Rigorous A/B test statistical analysis. Use when analyzing experiment results, calculating statistical significance, checking for sample ratio mismatch, or validating test design before launch.
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Build structured semantic layer documentation for metrics, dimensions, and entities. Activate when you need to define a business metric, document a data model, or create YAML definitions compatible with dbt Semantic Layer or similar frameworks.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
references/metric_definition_framework.md for metrics and references/dimension_hierarchy_patterns.md for dimensions.scripts/metric_template_generator.py to scaffold the initial YAML structure for the object type. Fill in the generated template.scripts/model_yaml_validator.py to check required fields, type constraints, and reference integrity (referenced dimensions exist in the same file).references/dbt_semantic_layer_guide.md for the exact field names and constraints for your dbt version.assets/metric_definition.yaml, dimensions to assets/dimension_definition.yaml, entities to assets/entity_definition.yaml.assets/metric_definition.yaml — filled metric YAML definition(s)assets/dimension_definition.yaml — filled dimension YAML definition(s)assets/entity_definition.yaml — filled entity YAML definition(s)scripts/model_yaml_validator.py (inline output)还没有评价。欢迎分享你的使用感受。
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Rigorous A/B test statistical analysis. Use when analyzing experiment results, calculating statistical significance, checking for sample ratio mismatch, or validating test design before launch.
中文简介正在准备中。当前显示原文说明。
Track and document analytical assumptions and decisions. Use when making analytical choices, documenting trade-offs, ensuring transparency, or creating audit trails for analytical work.
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Structured, reproducible analysis documentation. Use when documenting analysis findings, creating analysis notebooks, ensuring reproducibility, or building analysis archives for future reference.
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Structure analysis approach before starting work. Use when receiving new analysis requests, breaking down complex questions into steps, or planning iterative analysis workflows.
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Pre-delivery quality assurance for analysis work. Use when reviewing analysis before sharing with stakeholders, checking for completeness, validating assumptions, or ensuring clarity of recommendations.
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Post-analysis learning and process improvement. Use when completing major analysis projects, documenting lessons learned, or improving team analytical practices.
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