Data & Analytics Agent Skills

Use when the user asks to write SQL queries, optimize database performance, generate migrations, explore database schemas, or work with ORMs like Prisma, Drizzle, TypeORM, or SQLAlchemy.

Generate CSV, Markdown, and XLSX spreadsheets from structured tabular data.

Generate a multi-sheet XLSX workbook with quarterly revenue, expenses, and summary formulas.

Generate an XLSX inventory report with stock levels, reorder points, unit costs, and total value formulas for purchasing teams.

seven API skill. Use when working with seven for analytics, balance, contacts. Covers 51 endpoints.

Query the Clever Data API to retrieve information about contacts, districts, schools, sections, students, and teachers across 44 read-only endpoints.

Use this skill to write and run SQL queries, pull data, build metrics, or answer analytical questions. Always use this skill when you need to query data.

Use this skill for any data or analytics task — querying databases, analyzing metrics, exploring data warehouses, processing datasets, or creating visualizations.

Notion archive: search, sync freshness, pages/databases, Markdown exports, SQL counts, and Notcrawl repo work.

Slack archive: search, sync freshness, threads/DMs, SQL counts, and Slacrawl repo work.

Granola archive: search, sync freshness, notes, transcripts, panels, SQL counts, and Graincrawl repo work.

Create, read, edit Excel .xlsx workbooks and CSVs.

接收加密.xls/.xlsx + 密码 → 解密 → 脱敏 → 加密输出。替换公司名/客户名/金额扰动。

Optimizes SQL queries with execution plan analysis, index suggestions, and query rewriting

Generates complex Excel formulas, pivot table configurations, and VBA macros from natural language requests

Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

Parse FCS (Flow Cytometry Standard) files v2.0-3.1. Extract events as NumPy arrays, read metadata/channels, convert to CSV/DataFrame, for flow cytometry data preprocessing.

Fast DataFrame library (Apache Arrow). Select, filter, group_by, joins, lazy evaluation, CSV/Parquet I/O, expression API, for high-performance data analysis workflows.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Database development and operations workflow covering SQL, NoSQL, database design, migrations, optimization, and data engineering.

Django 安全最佳实践、认证、授权、CSRF 防护、SQL 注入预防、XSS 预防和安全部署配置。

Expert guidance for Cube, the headless BI and semantic layer that sits between your data warehouse and analytics applications. Helps developers define data models, create metrics APIs, and build analytics features in applications with consistent, governed access to business metrics.

You are an expert in Knex.js, the flexible SQL query builder for Node.js that supports PostgreSQL, MySQL, SQLite, and MSSQL. You help developers write type-safe queries with a chainable API, manage database migrations and seeds, build complex joins and subqueries, and use transactions — providing direct SQL control without the overhead of a full ORM.

Integrate procurement systems with supplier portals, delivery tracking, inventory management, and supply chain analytics for real-time visibility across the supply chain

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when "langfuse, llm observability, llm tracing, prompt management, llm evaluation, monitor llm, debug llm, langfuse, observability, tracing, llm-monitoring, evaluation, prompt-management, debugging, analytics" mentioned.

One breach = game over. Threat modeling, OWASP Top 10, secure coding, security architecture, zero trust. The complete security skill for protecting your application from day one. Security isn't a feature you add later - it's a mindset that shapes every decision. This skill covers application security, not infrastructure security. Use when "security, owasp, xss, sql injection, csrf, authentication, authorization, secrets, api key, vulnerability, secure coding, security headers, rate limiting, input validation, sanitize, escape, security, owasp, authentication, authorization, vulnerabilities, secure-coding" mentioned.

When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or "hypothesis." For tracking implementation, see data-and-funnel-analytics.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Access the COSMIC catalogue of somatic mutations in cancer to query somatic mutations, the Cancer Gene Census, mutational signatures, and gene fusions (authentication required). Use when curating known cancer driver genes, looking up recurrent somatic mutations in a gene, or interpreting mutational signatures for cancer research and precision oncology. Not for germline pathogenicity calls (use alterlab-clinvar) or interactive cohort visualization like OncoPrints and survival from study data (use alterlab-cbioportal). Part of the AlterLab Academic Skills suite.

Scales pandas/NumPy workflows beyond memory with Dask distributed computing — parallel DataFrames, arrays, delayed task graphs, and cluster execution. Use when existing pandas/NumPy code must run on larger-than-RAM data or across clusters, for parallel file processing, distributed ML, or integration with existing pandas code. For out-of-core analytics on a single machine prefer vaex; for in-memory speed prefer polars. Part of the AlterLab Academic Skills suite.

Out-of-core tabular analytics with Vaex for billion-row datasets that exceed RAM — lazy evaluation, fast aggregations, big-data visualization, and ML on a single machine. Use when working with large CSV/HDF5/Arrow/Parquet files, computing fast statistics on massive datasets, visualizing big data, or building ML pipelines that do not fit in memory. For distributed clusters prefer dask; for in-memory speed prefer polars. Part of the AlterLab Academic Skills suite.

Zero-shot univariate time-series forecasting with Google's TimesFM foundation model, producing point forecasts and prediction intervals from CSV/DataFrame/array inputs, with a preflight system checker for RAM/GPU. Use to forecast any univariate series (sales, sensors, energy, vitals, weather) without training a custom model. Part of the AlterLab Academic Skills suite.

Creates, analyzes, and visualizes complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing topologies — applicable to social, biological, transportation, citation, and any pairwise-relationship networks. This is classical graph analytics, not deep learning — for training graph neural networks (GCN/message passing, node/edge/graph classification on Cora-style data) use alterlab-torch-geometric instead. Part of the AlterLab Academic Skills suite.

Builds statistical plots with the seaborn Python library and pandas DataFrame integration, on attractive matplotlib-based defaults. Use for quick exploration of distributions, relationships, and categorical comparisons — box plots, violin plots, swarm/strip plots, KDE/histograms, pair plots, joint plots, regression plots, correlation heatmaps, and faceted small multiples (relplot/displot/catplot/lmplot). For interactive/hover/zoom charts defer to alterlab-plotly; for exact journal/manuscript styling (column widths, point fonts, CMYK, vector export) defer to alterlab-scientific-viz; for low-level custom matplotlib figures defer to alterlab-matplotlib (seaborn integrates with it for fine-tuning). Part of the AlterLab Academic Skills suite.

Excel/Sheets Formula Troubleshooter

Apache Airflow, dbt, Prefect, Dagster, and modern data orchestration for production data pipelines