Discover tools data analysis skills, compare source repositories and read the instructions for each workflow.
Specialized skill for analyzing personal health data, medical records, lab results, and supplement protocols. Use when working with health records, clinical data, lab values, or health optimization planning.
Analyze metrics, create reports, and track KPIs. Builds dashboards, revenue models, and growth projections. Use PROACTIVELY for business metrics or investor updates.
Data analysis expert for SQL queries, BigQuery operations, and data insights. Use proactively for data analysis tasks and queries.
Work with Resource datasets (mutable state tracking) using OPAL temporal joins. Use when you need to enrich Events/Intervals with contextual state information, track resource state changes over time, or navigat...
Aggregate and summarize event datasets (logs) using OPAL statsby. Use when you need to count, sum, or calculate statistics across log events. Covers make_col for derived columns, statsby for aggregation, group_...
Master OPAL window functions for row-relative calculations, rankings, and moving aggregates. Covers lag(), lead(), row_number(), rank(), dense_rank(), moving averages, first(), and last(). Use when comparing ro...
Detect anomalies in metrics and time-series data using OPAL statistical methods. Use when you need to identify unusual patterns, spikes, drops, or outliers in observability data. Covers statistical outlier dete...
Analyze percentile metrics (tdigest type) using OPAL for latency analysis and SLO tracking. Use when calculating p50, p95, p99 from pre-aggregated duration or latency metrics. Covers the critical double-combine...
Work with Interval datasets (time-bounded data) using OPAL. Use when analyzing data with start and end timestamps like distributed traces, batch jobs, or CI/CD pipeline runs. Covers duration calculations, tempo...
Work with Reference Tables (static CSV lookup data) using OPAL to enrich datasets with descriptive information. Use when you need to map IDs to human-readable names, add static metadata from CSV uploads, or per...
Aggregate pre-computed metrics (gauge, counter, delta types) using OPAL. Use when analyzing request counts, error rates, resource utilization, or any numeric metrics over time. Covers align + m() + aggregate pa...
Analyze event datasets (logs) and intervals over time using OPAL timechart. Use when you need to visualize trends, track metrics over time, or create time-series charts. Covers timechart for temporal binning, b...
Use OPAL subquery syntax (@labels) and union operations to combine multiple datasets or time periods. Essential for period-over-period comparisons, multi-dataset analysis, and complex data transformations. Cove...
Transform raw data into compelling narratives that drive decisions. Use when creating executive presentations, board decks, investor pitches, quarterly reviews, stakeholder reports, or any communication where d...
Guide Alfred to provide non-intrusive proactive suggestions based on risk detection, optimization patterns, and learning opportunities
Data analysis, visualization, statistical modeling, and reproducible research workflows.
R 4.4+ best practices with testthat 3.2, lintr 3.2, and data analysis patterns.
Perform comprehensive data analysis, statistical modeling, and data visualization by writing and executing self-contained Python scripts. Use when you need to analyze datasets, perform statistical tests, create...
Analyzes content marketing metrics to identify top performers, trends, and optimization opportunities. Use when reviewing blog posts, social media, or campaign performance. Accepts CSV data with engagement metr...
生成专业金融图表（K线图、技术指标图）。适用于数据可视化、技术分析展示、报告生成等场景。支持多种图表类型（basic、comprehensive）和自定义样式（深色、浅色主题）。
Automatically reviews data pipelines, optimizes SQL queries, and ensures data quality. Activates when you mention: data pipeline, ETL, SQL optimization, query performance, data quality, schema design, Airflow...
Frontend-backend time data transmission standard, using millisecond timestamps for time points (timezone-independent); Duration/Period use ISO 8601 string format.
专业数据可视化专家，精通现代图表库、仪表板设计和交互式数据展示。能够将复杂数据转化为直观、美观且富有洞察力的可视化作品。
Automatically score growth experiments using the ICE framework (Impact × Confidence × Ease). Use when the user creates a new experiment, mentions scoring or prioritization, or when analyzing experiment backlogs...
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.