data-analysis

Produce OpenCost-compatible namespace, pod, and workload cost allocation tables from user-supplied cluster shape data and public cloud pricing. Input is cluster topology provided by the caller; no cluster credentials or live API access are required or accepted. Output maps to…

Emit pod CPU and memory request/limit recommendations from user-pasted p50, p95, and p99 utilization metrics over a 7-14 day window. Outputs recommended requests at p95 plus 20% headroom, limits at p99 plus 30%, estimated monthly savings, and Karpenter consolidation eligibility…

Searches the web, legal databases, case law, patents, and case.dev knowledge base via the casedev CLI. Use when the user mentions "search", "legal research", "find cases", "case law", "patent search", "web search", "fetch URL", "webfetch", "legal skills", or needs to research…

Manages case.dev encrypted document vaults for legal workflows. Creates vaults, uploads files and directories, lists and downloads objects, and runs semantic search across vault contents. Use when the user mentions "vault", "upload documents", "document storage", "download…

You are an expert in Airbyte, the open-source data integration platform with 300+ pre-built connectors. You help developers sync data from SaaS tools, databases, and APIs into data warehouses and lakes — handling incremental syncs, CDC (Change Data Capture), schema evolution,…

When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," or…

Process large-scale data with Apache Spark. Use when a user asks to process big data, run distributed computations, build ETL pipelines, perform data analysis at scale, or use PySpark for data engineering.

Assists with storing, searching, and managing vector embeddings using ChromaDB. Use when building RAG pipelines, semantic search engines, or recommendation systems. Trigger words: chromadb, chroma, vector database, embeddings, semantic search, similarity search, vector store,…

ClickHouse is a columnar OLAP database built for real-time analytical queries over massive datasets. It supports a familiar SQL interface with powerful aggregation functions, materialized views for pre-computed rollups, and high-throughput inserts that make it ideal for event…

Serverless vector database at the edge with Cloudflare Vectorize. Use when: building semantic search on Cloudflare Workers, RAG pipelines at the edge, low-latency vector similarity search, or storing and querying embeddings without managing a separate vector database.

Parse and generate CSV files with the csv package — stream large files, handle custom delimiters, transform records, validate data, and generate CSV output from objects. Use when tasks involve data import/export, ETL pipelines, processing uploaded CSV files, or generating…

Analyze spreadsheet data, generate insights, and create visualizations. Use when a user asks to analyze data, explore a dataset, find trends, generate statistics, create charts from CSV or Excel data, summarize data, or answer questions about tabular data.

Configure and manage Datadog for infrastructure monitoring, application performance monitoring (APM), log management, and alerting. Use when a user needs to set up Datadog agents, create dashboards, configure monitors and alerts, integrate services, or query metrics and logs…

dbt (data build tool) transforms data in your warehouse using SQL SELECT statements. Learn project setup, models, tests, documentation, incremental materializations, and integration with data warehouses like PostgreSQL, BigQuery, and Snowflake.

You are an expert in dlt, the open-source Python library for building data pipelines. You help developers load data from any API, file, or database into warehouses and lakes using simple Python decorators — with automatic schema inference, incremental loading, and built-in data…

DuckDB is an in-process analytical database that runs embedded inside your application with zero external dependencies. It can query CSV, Parquet, and JSON files directly without loading them into tables first, making it ideal for local data exploration, ETL pipelines, and…

Build local-first applications with ElectricSQL — sync Postgres data to client devices in real-time. Use when someone asks to "sync database to client", "local-first app", "ElectricSQL", "offline-first sync", "real-time Postgres sync", "sync Postgres to SQLite", or "build an app…

Configure Fluentd for unified log collection, routing, filtering, and forwarding across distributed systems. Use when a user needs to aggregate logs from multiple sources, transform log data with filters, route logs to different destinations, or set up Fluentd as a Kubernetes…

Analyze massive datasets with Google BigQuery. Run SQL queries on petabytes of data, load and stream data in real-time, create materialized views, and use BigQuery ML for machine learning models directly in SQL.

Provision and manage Cloud SQL instances on Google Cloud for MySQL, PostgreSQL, and SQL Server. Configure high availability, read replicas, automated backups, IAM database authentication, the Cloud SQL Auth Proxy, and Terraform deployments. Use for managed relational databases…

Investigate Google Cloud networking issues by analyzing VPC Flow Logs, firewall logs, Cloud NAT logs, threat logs, and networking metrics. Run Connectivity Tests for path diagnostics and use BigQuery linked datasets for high-volume traffic analysis. Use when troubleshooting…

Grafana is an open-source visualization and dashboarding platform that connects to dozens of data sources including Prometheus, PostgreSQL, ClickHouse, and Elasticsearch. It lets you build interactive dashboards with panels, set up alerting rules, and manage everything as code…

Great Expectations is a Python framework for data quality testing and validation. Learn to define expectations, create validation suites, build data docs, and integrate with data pipelines for automated quality checks.

Set up and manage InfluxDB for time-series data storage, querying, and analysis. Use when a user needs to configure InfluxDB buckets, write Flux queries, set up retention policies, create tasks for data downsampling, or build dashboards for time-series metrics.

Assists with interactive data analysis, visualization, and reproducible research using Jupyter notebooks. Use when building notebooks that combine code with rich output, managing kernels, converting to reports, or parameterizing notebooks for batch execution. Trigger words:…