Data Engineering plugins

Create, read, edit, and generate Office documents — Excel spreadsheets, Word reports, PowerPoint presentations — plus PDF manipulation, form filling, and conversion between formats. Also includes art/image generation, frontend UI building, and document co-authoring workflows.

Perform end-to-end research workflows: market analysis, competitor benchmarking, trend detection, data validation, and idea vetting. A team of specialized agents retrieves and synthesizes information from web, files, and scientific literature to deliver actionable insights and strategic recommendations.

Build production-ready data pipelines with Apache Airflow and dbt, manage scalable data warehouses, and implement vector search and RAG systems using embedding models and vector databases.

Perform business analysis workflows — KPI frameworks, predictive models, real-time dashboards, TAM/SAM/SOM calculations, and multi-year financial modeling for startups — using Python, SQL, and cloud data warehouses like Snowflake and BigQuery.

Autonomous investment banking pitch agent: pulls market data, builds DCF and football-field valuation Excel models, and produces branded pitch deck files on disk — all from a single natural-language request.

Accelerate LLM application development with production-ready patterns for context window management, RAG pipelines, prompt caching, observability via Langfuse, and agent architectures.

Automate HR operations including recruiting, onboarding, performance reviews, compensation analysis, and policy guidance. Upload HR data for headcount and attrition reports or query company policies via Slack, Jira, Notion, or Benchling.

Delegate complex data engineering, ML, and AI workflows to specialized sub-agents that design scalable pipelines, build and optimize models, architect LLM systems, tune databases for performance, and deploy production infrastructure across clouds.

Build and manage end-to-end data analytics workflows: implement A/B testing with statistical rigor, design reliable analytics tracking, create interactive D3.js visualizations, architect scalable database schemas, and optimize SQL for cloud-native databases.

Generate sales call prep briefs, research prospects and companies, draft personalized outreach emails and LinkedIn messages, and build deal strategy assets like battlecards, landing pages, and forecasts using CRM data and web enrichment

Automate professional equity research workflows: generate earnings reports, initiating coverage, pre-earnings analysis, morning meeting notes, and sector landscapes. Screen stocks, track catalysts, update financial models, and maintain investment theses with cited sources.

Manipulate spreadsheet files (Excel, CSV, TSV) with full support for formulas, formatting, and charts. Enables reading, editing, creating, and converting workbooks for data cleaning and analysis.

Automate finance and accounting workflows including journal entries, account reconciliations, financial statement generation, variance analysis, and SOX 404 compliance testing, with integrations to BigQuery, Slack, and Benchling for querying financial and scientific data.

Ingest GP valuation packages, run portfolio-company marks through valuation templates, compute NAV and waterfall, and stage LP reporting for IR review in private equity workflows

Generate sector and thematic market research reports including industry overviews, competitive landscapes, comparable company analyses, and investment idea shortlists. Automates creation of structured .pptx and Excel outputs for financial analysis workflows.

Automate sell-side M&A workflows: build buyer universes, draft CIMs and teasers, manage deal pipelines with milestones and deadlines, model accretion/dilution, populate pitch decks and strip profiles, and create financial data packs — all from company data, SEC filings, or web sources.

Automates equity research workflows: reads earnings transcripts and filings, updates financial models with new data, generates professional post-earnings reports with variance analysis, and drafts morning meeting notes — all without live Excel, using Python/openpyxl for .xlsx output.

Automate general ledger to subledger reconciliation: detect breaks, trace root cause to originating journal entries, classify break causes, and route exception reports for sign-off. Also audits spreadsheets for financial model errors and integrity.

Automate private equity workflows: screen deals, run due diligence, analyze financials, build IRR models, assess unit economics, monitor portfolio performance, and generate investment memos and value creation plans.

Write and optimize SQL across data warehouses, profile datasets, and generate interactive dashboards or publication-quality charts from queries or files. Automates data analysis with natural language queries and connects to analytics platforms like Amplitude and Hex.

Build and audit financial models (DCF, LBO, comps, 3-statement) in Excel, generate competitive landscape and pitch decks in PowerPoint, and access financial data from FactSet, PitchBook, Morningstar, and other sources.

Build institutional-quality financial models (DCF, LBO, three-statement, and trading comparables) directly in Excel from a ticker and assumption set, with automated formula linkages, integrity audits, sensitivity tables exports as .xlsx files.

Streamlines fund administration and month-end close by automating reconciliation break analysis, accrual schedules, roll-forward tie-outs, and variance commentary — supporting controller — supporting review and audit documentation.

Audit LP capital-account statements against the fund NAV pack before distribution — ties out balances, allocations, and fees, flags discrepancies, and checks spreadsheet formulas for errors, hardcodes, and circular references.

Accelerate early-stage life sciences R&D by connecting to preclinical research tools and databases for literature search, genomics analysis, target prioritization, and data conversion.

Generate professional financial research reports including equity earnings previews, funding round briefings, and company tear sheets using S&P Capital IQ data, with remote access to S&P Global Kensho's financial intelligence platform for market data and analytics.

Perform product market research workflows: generate user personas, behavioral segments, and customer journey maps from surveys, CSVs, or feedback; conduct competitive landscape analysis with competitor profiles and differentiation maps; run sentiment analysis on reviews for insights and recommendations; estimate TAM/SAM/SOM with growth projections; output markdown reports.

Access LSEG (Refinitiv) financial data and analytics to price bonds, analyze yield curves, evaluate FX carry trades and yield curves, value options, and build macro dashboards.

Generate SQL queries from natural language descriptions using your database schema for PostgreSQL, MySQL, or BigQuery. Analyze CSV or Excel user data to produce cohort retention heatmaps, engagement trends, churn insights, and research recommendations. Evaluate A/B tests for statistical significance, confidence intervals, lift, and ship/extend/stop decisions with Python-powered reports.

Automate long-form webnovel creation: initialize projects interactively with genre/characters/worldbuilding/outlines, generate beat sheets/chapters (2000+ words), extract entities/relationships to SQLite indexes, visualize status/entity graphs in read-only dashboard, recover interrupted workflows, and validate chapters via agents for inconsistencies, pacing, OOC, reader pull, and quality reports.

Create and repair Redpanda Connect pipeline configurations and Bloblang transformation scripts using natural language. Automates component search, config generation, and script debugging with optional sample data validation.

Build, deploy, and run serverless web scrapers and automation scripts on Apify's platform, extract structured data from 15+ websites (Instagram, LinkedIn, Google Maps, etc.), integrate scraping into JavaScript/TypeScript/Python applications, and convert existing projects into scalable Apify Actors.

Work with Mooncake Python APIs to perform distributed storage operations, RDMA/TCP data transfers, and PyTorch tensor processing.

Generate professional Excel financial models including DCF valuations with FCF projections, WACC, sensitivities; LBO analyses with debt schedules, IRR/MOIC; budget vs actual variance reports with waterfalls; and dynamic pivot tables via natural language prompts and auto-invoked skills.

Automate training and optimization of ML models for classification and regression on datasets: analyze data, select/configure algorithms, cross-validate, evaluate metrics, generate Python code using scikit-learn/PyTorch/TensorFlow/XGBoost, and save artifacts.

Build and manage PortalJS data portals: recommend an architecture, scaffold a portal, add datasets (CSV/TSV/JSON/GeoJSON), create charts (recharts) and interactive maps (Leaflet), connect a CKAN backend, harvest datasets from open-data platforms, infer data schemas, audit data quality, and deploy to PortalJS Arc hosting.

Build, debug, optimize, secure, and deploy FireCrawl web scraping pipelines for LLM/RAG data ingestion: scrape/crawl sites to markdown/JSON, extract structured data, handle rate limits/errors, add monitoring/observability, scale with backoff/caching, and integrate into Node/Python apps from dev to production.

Automate full Databricks lakehouse lifecycle: build Delta Lake ETL pipelines with medallion architecture and Auto Loader, engineer ML workflows via MLflow and Feature Store, deploy jobs/pipelines with Asset Bundles and GitHub Actions CI/CD, secure via Unity Catalog RBAC, optimize costs/performance, troubleshoot errors, and monitor with system tables.

Generate plots, charts, and graphs from data via natural language requests—AI analyzes datasets, selects optimal visualization types, produces validated Python code, delivers performance metrics and insights, saves artifacts, and creates documentation.

Automate machine learning feature engineering by generating and executing validated Python code to create interactions, scale data, encode categoricals, select features via importance analysis, compute metrics, save artifacts, and generate documentation.

Generate and execute automated Python pipelines for data cleaning, transformation, validation, and ETL in ML workflows. Analyze context to produce AI/ML code with built-in validation, error handling, performance metrics, saved artifacts, and documentation.

Synthesise multi-source user signals into weighted insight briefs with confidence ratings, design and evaluate A/B experiments with statistical rigor, structure AI/ML product decisions on a canvas, conduct ethical reviews of AI features with risk scoring, and transform feature briefs into ready-to-use design handoff docs for designers

Run structured product data analyses including metric deep-dives, funnel analysis, cohort studies, and churn investigations, then interpret results against targets to produce health reports with RAG status, trend analysis, root cause hypotheses, and prioritized actions.

Automate archiving historical PostgreSQL/MySQL records to archive tables or cloud storage (S3, Azure Blob, GCS) using age/status-based rules, retention policies, compression, and compliance tracking to shrink primary database size and manage cold data.

Design and implement partitioning strategies for PostgreSQL and MySQL tables using range, list, hash, and composite methods to handle massive datasets. Automate schema design, maintenance routines, query optimization, and data retention policies for improved performance.

Optimize DeFi yield farming strategies across Ethereum, BSC, and Polygon by aggregating DeFiLlama APY data, assessing risks via TVL and audits, and generating portfolio allocations tailored to your capital, risk tolerance, duration, and preferences.

Forecast future values from historical time series data using ARIMA and Prophet models, including trend, seasonality, and autocorrelation analysis with confidence intervals. Generate validated AI/ML code for forecasting tasks complete with error handling, performance metrics, insights, artifacts, and documentation.

Analyze DeFi liquidity pools on Uniswap V2/V3, Curve, Balancer, and other DEXes across multiple chains to calculate impermanent loss, APY, TVL, volume, fees, risks, LP profitability, and optimization opportunities using Python scripts.

Build production Clay SaaS integrations for lead enrichment: configure tables and webhooks, deploy receivers to Vercel or Docker, scale pipelines with Redis queues, secure with RBAC and PII guards, optimize costs and performance, troubleshoot failures, and monitor metrics using 30 dedicated Claude Code skills.

Query EVM blockchain data on Ethereum, Polygon, and Arbitrum from the command line using Etherscan APIs to fetch transactions, address balances, token histories, blocks, and smart contract details, then generate structured markdown reports with holdings, histories, and insights.

Analyze and monitor on-chain blockchain metrics across chains and DeFi protocols, tracking whale movements, holder distributions, network health, TVL, fees, DEX volumes, yields, and trends. Generate analytics reports via DeFiLlama API using Python CLI tools.

Calculate cryptocurrency capital gains taxes from exchange CSV transaction data using FIFO, LIFO, or HIFO methods. Identify taxable events across trading, DeFi, NFTs, and mining. Generate compliant tax reports and forms like Form 8949 for US, UK, and EU jurisdictions.

Generate BUY/SELL trading signals for cryptocurrencies and stocks using technical indicators like RSI, MACD, and Bollinger Bands. Scan and rank watchlist opportunities with confidence scores, stop-loss/take-profit levels, multi-timeframe analysis, and markdown reports including risk guidance.

Scan cryptocurrencies, stocks, and forex markets for top gainers, losers, volume spikes, and unusual activity. Customize by market, timeframe, category, limits, filters, and sorting. For crypto, rank 1000+ assets by composite score of price change, volume ratio, and market cap to track pumps and trends.

Track institutional options flow and detect smart money movements or unusual activity in BTC/ETH markets on Deribit, OKX, and Bybit via API queries, analyzing positioning and sentiment for any symbol or market-wide with customizable params like timeframe and min-premium.

Analyze cryptocurrency market sentiment by pulling data from social media, news, on-chain metrics, derivatives, whale activity, and Fear & Greed Index to generate 0-100 mood scores, weighted insights, and predictions for overall market or specific coins like BTC.

Build secure Rust applications integrating Azure services: authenticate with Entra ID, manage Key Vault secrets/keys/certificates, perform CRUD on Cosmos DB documents and Blob Storage, and stream data via Event Hubs using official SDK patterns and code examples.

Perform comprehensive SEO and Generative Engine Optimization (GEO) analysis, content optimization, and monitoring directly from Claude, pulling data from major SEO platforms (Ahrefs, Semrush, etc.) and integrating with cloud services (Cloudflare, Vercel), collaboration tools (Slack, Notion), and CMSs (Webflow, HubSpot).

Wrangle, profile, clean, transform, and analyze tabular data (CSV, TSV, Excel, JSONL, Parquet) using 51 qsv skill-based commands — including SQL queries, joins, validation, ontology inference, charting, performance acceleration, and reproducible logging.

Run 80+ bioinformatics workflows locally — pharmacogenomics, GWAS, single-cell RNA-seq, ancestry, metagenomics, variant annotation, protein structure prediction, and clinical reporting — with deterministic Python execution, reproducibility bundles, and privacy-preserving local computation.

Detect anomalies and outliers in datasets using ML algorithms like Isolation Forest, One-Class SVM, LOF, and autoencoders to identify unusual patterns. Generate Python code for custom anomaly detection tasks, including validation, error handling, performance metrics, insights, saved artifacts, and documentation.

Design and optimize NoSQL data models for MongoDB, DynamoDB, Redis, and Cassandra by analyzing access patterns, embedding vs referencing, denormalization trade-offs, sharding keys, and indexes.

Split CSV datasets into stratified training, validation, and test sets using custom ratios for ML workflows, generating production-ready Python code with validation, error handling, performance metrics, artifact saving, and automatic documentation.

Build and validate linear and polynomial regression models on datasets to predict outcomes, uncover relationships, and report metrics like R-squared and RMSE. Generates validated code with error handling, delivers insights, saves artifacts, and creates documentation.

Build end-to-end AutoML pipelines in Python, automating data checks, feature engineering, model selection, hyperparameter tuning, evaluation, and deployment artifacts for repeatable ML workflows. Generate validated ML code with error handling, performance metrics, and documentation from context analysis.

Design optimal ClickHouse schemas with MergeTree engines, ingest data at scale via Node.js/Python clients, run analytical queries, optimize performance and costs, secure deployments with RBAC and quotas, integrate CI/CD testing and monitoring, troubleshoot errors/incidents, and manage migrations/upgrades for production analytics workloads.

Build, deploy, optimize, secure, and troubleshoot Python pipelines exporting Clari revenue forecasts, quotas, CRM data, and adjustments to Snowflake, BigQuery, or PostgreSQL. Includes CI/CD integration, API debugging, cost/performance tuning, local mocks, schema migrations, rate limit handling, and production checklists.

Build production Navan API integrations for travel bookings, expense management, and data syncing to ERPs/warehouses: automate OAuth auth setup, REST workflows, error debugging, CI/CD deployment, monitoring, security hardening, and webhook handling.

Use natural language to automate browser interactions—navigate, fill forms, click elements, handle multi-tab sessions—and extract structured data from websites into JSON/CSV, all via Claude through the ActionBook MCP server running locally.

Scrape, search, crawl, and map websites from the command line, extracting structured JSON or clean markdown from static and JavaScript-rendered pages, bulk-download entire sites, monitor pages for AI-filtered changes, and control live browser sessions for complex interactions.

Orchestrate Hex data projects via API in external pipelines: trigger parameterized runs, poll status, integrate GitHub Actions CI/CD for deploys/refreshes, optimize costs/performance/rate limits, debug errors, secure auth, deploy to Vercel/Fly.io/Cloud Run, and migrate SDKs.

Build and manage Snowflake data platforms: connect via Node.js/Python SDKs, ingest data from S3/GCS/Azure stages/Snowpipe, construct ELT pipelines with streams/tasks/dynamic tables, tune query performance/costs/clustering, enforce RBAC/security policies/governance, integrate CI/CD with GitHub Actions/Terraform, set up multi-env/observability, troubleshoot errors/incidents.

Accelerate Palantir Foundry integrations by generating Python SDK code for building ETL data pipelines, managing Ontology objects, handling API errors and rate limits, configuring RBAC and security, setting up CI/CD with GitHub Actions, deploying to GCP/AWS/Docker, and implementing monitoring, observability, and cost optimization.

Build, deploy, debug, and scale Apify Actors for web scraping: scaffold Crawlee crawlers with input schemas and routers, manage datasets/queues/APIs programmatically, set up local dev/CI-CD pipelines with GitHub Actions, diagnose errors/timeouts/proxies, tune performance/costs, secure tokens, and integrate runs into Node.js apps.

Build production web scraping pipelines with Bright Data: authenticate proxies/APIs, scrape JS/SPA sites via Scraping Browser/Playwright/Puppeteer/SERP, debug errors/rate limits, tune costs/performance/caching, deploy to Vercel/GCP/Fly.io, set up CI/CD/tests/webhooks, and monitor usage in Node.js/Python projects.

Guides Chinese academic paper writing from brainstorming through publication: structures research plans, generates literature reviews with BibTeX citations, produces publication-quality Python charts (matplotlib/seaborn), and outputs LaTeX-formatted manuscripts with journal templates.

Delegate AI agents to analyze business metrics and KPIs, build revenue models and dashboards, draft GDPR-compliant legal documents, integrate Stripe payments with webhooks, and develop quantitative financial models for trading strategies and portfolio optimization.

Delegate complex AI and data tasks to specialized agents that proactively build LLM applications with RAG and orchestration, design scalable ETL pipelines and warehouses, deploy MLOps workflows, optimize prompts, analyze datasets, manage context, and decompose goals into actionable hierarchies.

Automate processing of PDF, DOCX, PPTX, and XLSX files in Anthropic Claude workflows: extract text, tables, images, and metadata; edit content, structure, and tracked changes; generate documents and presentations from templates; clean, format spreadsheets with formulas, charts, and financial standards.

Develop and manage end-to-end Databricks workflows: data engineering with Spark, Delta, Iceberg, and streaming; build and deploy ML models and GenAI agents; create dashboards, apps, and CI/CD bundles; query Unity Catalog and manage infrastructure via SDK/CLI operations.

Agentically audit, optimize, and manage Power BI semantic models in Microsoft Fabric: trace dependencies across workspaces for impact analysis, review quality and performance against best practices, standardize TMDL naming conventions, author and validate Power Query M expressions, and orchestrate full/incremental refreshes via REST APIs and CLI.

Apply 28 prioritized best-practice rules for ClickHouse schema design, query optimization, and data ingestion, with companion skills for running ClickHouse SQL in Python, reviewing schemas and queries, writing Node.js client code, troubleshooting performance issues, and setting up local or cloud ClickHouse environments.

Discovers and runs weather/climate AI models from the Earth2Studio ecosystem: browse models and data sources filtered by GPU/VRAM, install with uv or pip and model extras, fetch variables and times from data sources, and build deterministic forecast inference scripts.

Load, query, and analyze data from files (CSV, Parquet, JSON, Excel, Avro, spatial), S3-compatible storage, or attached DuckDB databases using SQL in Claude Code sessions. Preview schemas/samples without full downloads, convert formats, perform spatial analysis (distances, joins), search docs/session logs, install extensions.

Manage Microsoft Fabric workspaces, notebooks, lakehouses, and OneLake files using the fab CLI, with support for migrating workspaces from trial to production capacity and retrieving Microsoft Learn documentation.

Build and test dbt models using SQL transformations, ref/source, and YAML unit tests; configure semantic layers for metrics, dimensions, and KPI queries; troubleshoot Cloud jobs with logs, API, and git; implement Mesh governance for contracts and cross-project refs; access docs; format CLI commands; generate MCP configs for VS Code integration.

Build, manage, and deploy the full Salesforce platform stack — Apex, OmniStudio, Data Cloud, Agentforce agents, Lightning Web Components, UI bundles, flows, metadata, and mobile apps — with code generation, validation scoring, debugging, documentation retrieval, and CLI-driven deployment.

Generate vector embeddings from text data using OpenAI, Cohere, or local models, store them in a vector database with indexing, and perform semantic similarity searches to retrieve top-K matches with scores, metadata, re-ranking, and deduplication.

Detect PII in codebases and data stores, generate severity-ranked risk inventories with remediation recommendations, and anonymize datasets via pseudonymization techniques, outputting formatted reports of processed fields and applied methods.

Migrate dbt projects from Core to Fusion engine or across data platforms like Snowflake to Databricks by triaging errors as auto-fixable or guided, adapting SQL dialects, and validating fixes with dbt debug, compile, and unit tests.

Follow expert Dagster conventions and dg CLI guidance to create projects, define assets jobs schedules sensors, debug issues, and query pipeline components in Python data engineering workflows.

Scrape any webpage, search Google, and extract structured data from 40+ platforms (Amazon, LinkedIn, Instagram, TikTok, YouTube) via Bright Data's CLI, MCP server, or SDKs, with built-in bot detection bypass, proxy integration, and real-time competitive intelligence.

Automate creation, editing, analysis, and visual review of Office documents: build Excel spreadsheets with formulas/charts, edit Word docs and PowerPoint slides preserving layout, generate/extract from PDFs, using rendered previews for validation.

GPU-accelerated Mean-CVaR portfolio optimization using NVIDIA cuOpt for efficient frontier computation, scenario generation, backtesting, and rebalancing of stock portfolios.

Manage the full Airflow data engineering lifecycle: author, test, debug, and deploy DAGs; profile and query data warehouses; trace lineage; migrate between Airflow versions; and manage local and production deployments via the Astro CLI.

Build product metrics frameworks with North Star metrics, KPI trees, and counter-metrics; explain, optimize, and generate SQL queries for PostgreSQL, MySQL, SQLite; create structured dashboard specs including KPIs, charts, and layouts from business questions for BI tools like Looker or Grafana.