Analyzing Pigment Data
by gopigment6fec49f4ce9dNo license22 starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated 2 days ago
Always use this skill when querying, exploring, or analyzing existing data in a Pigment workspace. Covers the analysis workflow, query formulation, data concepts, analysis patterns, ambiguity handling, and result interpretation.
- 6fec49f4ce9dCurrentcommit 6fec49fPublished Oct 8, 2026
Source and attribution
Source:gopigment/ai-pluginsinskills/analyzing-pigment-dataat commit6fec49f
License: No license
Content belongs to its original authors. SourceWeft indexes it from a public repository.
More from gopigment/ai-plugins
Writing Pigment Formulas
gopigment
Guides writing, editing and validating Pigment metric formulas, covering quoting, data types, modifiers and BLANK.
Writing Performant Formulas
gopigment
Checklist and rules for writing sparse, performant Pigment formulas before delivery.
Using Search Tools
gopigment
Execution skill. Choosing between the discovery tools and calling them cheaply: why breadth is free but call count is not, and lineage versus full impact analysis. Load before exploring an application, resolving block names, or checking what a change breaks.
Using Mapped Dimensions
gopigment
Explains how to model time-dependent hierarchies in Pigment using mapped dimensions and joined pivots in views.
Using List Subsets
gopigment
Guides when and how to use List Subsets in Pigment, covering decision gates, risks, and safe implementation patterns.
Using Formula Modifiers
gopigment
Explains how to apply Pigment formula modifiers such as BY, ADD, REMOVE, KEEP, SELECT, FILTER and EXCLUDE to align metric dimensions.
More in Data & Analytics
Spanner Basics
Guides Google Cloud Spanner administration, schema design, querying and performance diagnosis.
Gke Cost Analysis
Answers natural-language questions about GKE cluster and workload costs using BigQuery billing exports and live cluster metrics.
Datalineage Bigquery Asset Impact Analysis
Guides an agent through downstream impact (blast radius) analysis for a BigQuery table or view using Data Lineage.
Bigquery Troubleshooting
Diagnoses failing, slow, or unexpectedly expensive BigQuery jobs through structured root-cause workflows.
Bigquery Optimization
Guides BigQuery cost and performance optimization across capacity editions, storage layout, and SQL queries.
Bigquery Bigframes
Guides writing Python code with BigQuery DataFrames (BigFrames) for data processing, analysis, and machine learning.