Profile a dataset, run statistical tests, generate visualizations, build dashboards, write SQL across warehouse dialects, and synthesize findings into stakeholder-ready reports. Skills wrap pandas, plotly, matplotlib, and the major cloud SQL flavors.
Related searches: claude data analysis skill, AI SQL writer, claude code analytics workflow, claude data visualization.
Analyze datasets to extract insights, identify patterns, and generate reports. Use when exploring data, creating visualizations, or performing statistical analysis. Handles CSV, JSON, SQL queries, and
science
Your data has answers. CellCog asks the right questions. #1 on DeepResearch Bench (Feb 2026) + frontier coding agent — upload messy CSVs with minimal prompting and get structured insights back: charts
science
Fast in-process analytical database for SQL queries on DataFrames, CSV, Parquet, JSON files, and more. Use when user wants to perform SQL analytics on data files or Python DataFrames (pandas, Polars),
engineering
GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, and RAFT. Use whenever the user mentions GPU/CUDA/NVIDIA acceleration, or wants
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Expert guidance for Ibis, the Python dataframe library that provides a pandas-like API but generates SQL for execution on any backend — DuckDB, PostgreSQL, BigQuery, Snowflake, Spark, and more. Helps
engineering
Renders canned Vectra AI dashboard reports via the Python channel. The user must explicitly name a report from the catalog — active connections, C2 beacon report, DNS error rate, flow records, HTTP st
security
Dashboard auto-orchestration for ChatBI and analytics workflows. Use when Codex needs to generate a dashboard layout and frontend-ready dashboard JSON from multiple chart recommendations, Query Intent
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Connect to databases, monitoring systems, analytics platforms, and APIs to fetch, analyze, and report on data. Use when the user says 'query the database', 'check the metrics', 'fetch data from', 'ana
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Design and spec an analytical dashboard — define the question each chart answers, write the SQL queries, spec the layout and refresh cadence. Produces a complete dashboard spec ready to implement. Use
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Comprehensive expertise in blockchain data analysis using Dune Analytics, custom indexers, and on-chain data querying. Covers SQL for blockchain, dashboard creation, protocol metrics, and alpha discov
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Professional trading analysis dashboard template (single-file HTML) with light/dark theme switch, dense market panels, chart interactions, demo/live playback, and command palette behavior. Use when us
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Use when you need to choose the right visualization for your data and question, then create a narrated report that highlights insights and recommends actions. Invoke when analyzing data for patterns (
sales
Apache Superset is a widely adopted open-source BI platform for SQL exploration, chart building, and dashboard delivery. This skill is useful when an agent needs to query warehouse data, assemble dash
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Build effective charts, dashboards, and reports across analytics, infrastructure monitoring, and ML domains. Use for library selection, visualization UX, accessibility, and domain-specific dashboard d
product
Data exploration and analysis partner for Product Managers. Use when the user needs to query databases, analyze metrics, create dashboards, or extract insights from data. Triggers include "query", "an
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Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotli
engineering
Select the right Python charting library (seaborn, matplotlib, graphviz) and produce publication-quality static visualizations. Use when creating charts, plots, graphs, diagrams, heatmaps, visualizati
engineering
csvkit is a suite of Python command-line utilities for converting to, working with, and analyzing CSV files. It includes tools for format conversion, querying CSV with SQL, data cleaning, filtering, s
engineering
A skill for analyzing data using Python (pandas) and generating professional visualizations (matplotlib/seaborn).
engineering
Bulk reads, multi-page iteration, and analytics over Dataverse data via the Python SDK and Web API. Use when the user wants to read, list, filter, aggregate, group, join, or analyze records — includin
engineering
Automatically generate Excel reports from data sources including CSV, databases, or Python data structures. Supports data analysis reports, business reports, data export, and template-based report gen
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Generate publication-quality figures from code analysis data. Multi-backend: Graphviz, Mermaid, NetworkX/D3, matplotlib, plotly, seaborn. Supports aerospace, nuclear, military, and mathematics visuali
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Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, ge — from engineering/engineerin
engineering
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, ge — from bg-szy/TOP-SKILLS
engineering
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, ge — from engineering/engineerin
engineering
Build network traffic baselines from NetFlow/IPFIX data using Python pandas for statistical analysis, z-score
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Expert guidance for creating and working with marimo notebooks - reactive Python notebooks that can be executed as scripts and deployed as apps. Use when the user asks to create marimo notebooks, conv
engineering
Best practices for Pandas data manipulation, analysis, and DataFrame operations in Python
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Schema-driven test data factory generator. Reads your database schema or model definitions — Prisma schema, SQLAlchemy models, Django models, TypeORM entities, Zod schemas, Pydantic models, or raw SQL
engineering
Expert in quantitative finance, algorithmic trading, and financial data analysis using Python (Pandas/NumPy), statistical modeling, and machine learning.
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