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From badi
Provides skills for data analysis, BI, machine learning, and data engineering including SQL queries, dashboards, statistical analysis, predictive models, and ETL pipelines.
How this skill is triggered — by the user, by Claude, or both
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/badi:data-analyticsThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
This file contains all the skills across data collection, analysis, visualization, machine learning, business intelligence, and data engineering.
This file contains all the skills across data collection, analysis, visualization, machine learning, business intelligence, and data engineering.
Enables data-driven decision making by building a comprehensive data strategy for the organization.
Designs data collection mechanisms to ensure an accurate, complete data flow.
Designs the event-tracking infrastructure for applications and websites.
Builds the analytics foundation by designing the data warehouse architecture.
Automates data transformation and loading by designing ETL/ELT pipelines.
Enables storage of structured and unstructured data by designing a data lake architecture.
Writes and optimizes SQL analysis queries that answer business questions.
Designs effective, actionable dashboards for business users.
Applies chart-type selection and design principles to present data clearly and effectively.
Surfaces trends through time-based behavior analyses over user cohorts.
Identifies drop-off points and optimization opportunities by analyzing the user conversion funnel.
Runs segmentation analyses that split users or customers into meaningful groups.
Drives reliable decisions by analyzing A/B test results statistically.
Anticipates future trends by building forecasting models on historical data.
Builds models and analyses that compute customer lifetime value (LTV/CLV).
Determines churn causes and prevention strategies by analyzing customer/user loss.
Shapes marketing strategy by segmenting customers with RFM (Recency, Frequency, Monetary) analysis.
Builds attribution models measuring marketing channels' impact on conversion.
Discovers purchase patterns through association-rule analysis.
Builds systems that automatically detect abnormal patterns and anomalies in data.
Measures customer opinion and market perception through sentiment analysis on text data.
Builds systematic processes that measure, monitor, and improve data quality.
Manages data assets by building an organizational data governance framework.
Makes the organization's data assets discoverable by building a data catalog.
Meets privacy and compliance requirements in analytics processes.
Enables self-service analytics through BI platform selection and configuration.
Builds a reporting system that generates and distributes periodic reports automatically.
Builds a performance-tracking framework by defining KPIs aligned with business goals.
Extracts meaningful insights from datasets with statistical methods.
Builds trend, seasonality, and forecasting models by analyzing time-series data.
Builds scalable data-processing capacity by designing the data engineering infrastructure.
Builds streaming analytics infrastructure enabling real-time data flow and analysis.
Builds the workflow and methodology that manages an ML project end to end.
Designs the process of building effective features for machine-learning models.
Applies metrics and methods that evaluate ML model performance comprehensively.
Delivers a continuous prediction service by deploying the ML model to production.
Detects degradation by continuously monitoring ML model performance in production.
Ensures correct causal inference through data-driven experiment design.
Drives action by presenting data analyses as persuasive stories.
Builds self-service infrastructure so non-technical users can run their own analyses.
Delivers query performance and ease of use by designing analytics-oriented data models.
Provides analytics-ready data by building the transformation layer in the warehouse with dbt.
Builds code and workflows for producing insights through data analysis in Python.
Grows the experimentation culture by building the organizational A/B testing platform and process.
Builds the strategy for forming the data analytics team, defining roles, and designing the working model.
Builds a metric tree mapping the relationships between business metrics.
Plans safe, complete data migration from source systems to target systems.
Builds the training and culture-change program that grows data literacy across the organization.
Surfaces churn trends by analyzing user retention rates per cohort.
Builds systems that continuously monitor pipeline health and data quality.
npx claudepluginhub fatihkan/badi --plugin badiCreates bite-sized, testable implementation plans from specs or requirements, with file structure and task decomposition. Activates before coding multi-step tasks.