analyzing-innovation-portfolio
Analyze the CustomGPT.ai Labs Innovation workbook and cost tracking data to surface portfolio-level insights, trends, and recommendations for where to focus Innovation efforts.
Discover documentation data analysis skills, compare source repositories and read the instructions for each workflow.
Analyze the CustomGPT.ai Labs Innovation workbook and cost tracking data to surface portfolio-level insights, trends, and recommendations for where to focus Innovation efforts.
Guide for AI agent to use the tools offered by this library to perform analytic tasks.
ECharts图表配置技能,包括柱状图、折线图、组合图等可视化组件
Use when working with the scikit-hep hist library in Python to create, fill, slice, and plot histograms (1D/2D/multi-axis), including UHI indexing, categorical axes, and mplhep plotting conventions.
Analyze disk usage across directories, identify large/old directories, and generate storage reports. Use when you need to find directories consuming disk space, identify candidates for cleanup (directories olde...
Automated report generator for daily, weekly, and monthly performance summaries. Creates markdown reports with trading performance, defensive wins, agent accuracy, and constitutional compliance statistics.
Set-theoretic operations for finding unique elements, membership testing, and array intersections. Triggers: unique, isin, intersect1d, setdiff1d, union1d.
Sorting and searching algorithms including O(n) partitioning, binary search, and hierarchical multi-key sorting. Triggers: sort, argsort, partition, searchsorted, lexsort, nan sort order.
Standard and NaN-robust statistical functions for data analysis, histograms, and correlation matrices. Triggers: statistics, mean, nanmean, histogram, corrcoef, percentile, std.
Linear algebra operations in NumPy, including matrix multiplication, SVD, system solving, and least squares fitting. Triggers: linalg, matrix multiplication, SVD, eigenvalues, matrix decomposition, lstsq, multi...
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for...
Master SOTA data prep for Kaggle comps: automated EDA (Sweetviz), cleaning (Pyjanitor), and feature selection (Polars + XGBoost) for medium datasets (100MB–5GB) in Colab.
Extracts, transforms, and analyzes NBA statistics using the nba_api Python library. Use when working with NBA player stats, team data, game logs, shot charts, league statistics, or any NBA-related data engineer...
Data analysis workflows and patterns for exploring, transforming, and visualizing data. Use when working with data, creating reports, or when users mention "data analysis", "analyze data", "data exploration", o...
Analyze datasets using Python with pandas, numpy, and visualization libraries. Generates statistical summaries, identifies patterns, creates charts, and provides insights. Use when analyzing CSV/Excel files, ex...
Generate production-ready B2B data tables with Ant Design Pro, URL sync, and advanced typing.
Applies Fabric AI prompt patterns for summarization, analysis, extraction, code review, and content creation. Use when asked to summarize content, analyze documents, extract insights, review code, create docume...
Analyze completed experiments and craft executive-ready summaries with insights and recommendations.
Inventory available datasets, instrumentation gaps, and data quality considerations for the initiative.
Document key metrics, definitions, and segmentation required to track product success.
Align analysts and designers on the goals, requirements, and storytelling approach for a new or updated dashboard.
dlt (data load tool) patterns for SignalRoom ETL pipelines. Use when creating sources, debugging pipeline failures, understanding schema evolution, or implementing incremental loading.
Expert in creating database diagrams and visual representations. Use when generating ERDs, schema diagrams, or visualizing database relationships with Mermaid.js.
分析结构化数据并生成统计报告和可视化建议
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.