Data & Analytics Agent Skills

表格全场景处理：本地 Excel/CSV 与在线表格（飞书、doubao.com 的 /sheets/ 链接）的创建、读写、分析、计算、建模、语义处理、可视化与美化。**只要用户输入包含表格类附件——上传 .xlsx/.xls/.csv 文件，或给出 feishu/doubao.com 的 /sheets/ 链接或 token——必须加载本技能。** 此外，用户口述数据要整理成表，或要求计算/统计/建模/预测/透视/可视化/美化/公式等任何表格操作时，也加载本技能。一切与 Excel/表格相关的任务都必须加载。本技能不负责获取外部信息，如需补充数据须先通过其他途径获得。

Kysely type-safe SQL query builder - End-to-end type safety from schema to queries, migrations, transactions, plugins

Reporting pipelines for CSV/JSON/Markdown exports with timestamped outputs, summaries, and post-processing.

Automated guided vehicle route planner. Use when json agv tasks, csv agv tasks, checking agv status.

Dask parallel computing reference for Python. Covers Dask DataFrame (parallel Pandas), Dask Array (parallel NumPy), Dask Delayed for custom parallelism, Dask Bag, distributed clusters, dashboard monitoring, and scaling best practices.

Spreadsheet creation, format conversion (ODS/XLSX/CSV), formulas, data automation with LibreOffice Calc.

Biblioteca DataFrame rápida (Apache Arrow). Selecione, filtre, group_by, joins, avaliação preguiçosa, I/O CSV/Parquet, expression API, para fluxos de trabalho de análise de dados de alto desempenho.

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.

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: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

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.

Your won/lost/churn pattern analyst. Connect a CRM plus a product-analytics tool, then turn retrospective data into forward-looking action. Three modes. (1) WON, win pattern recognition that feeds your ICP and lookalike search. (2) LOST, loss pattern plus competitive intel that feeds a messaging refresh. (3) CHURN, churn theme extraction plus predictive scoring (which active accounts look like recent churners?). Trigger on "why are we winning?", "why are we losing?", "closed-lost autopsy", "churn patterns", "competitive intel rollup", "who do we lose to most?", "show me lookalike candidates to {winning customer}", "predictive churn", "which active accounts look like churners?", or any portfolio-level pattern recognition.

RICE feature prioritization with scoring and capacity planning. Usage: /rice prioritize <features.csv> [options]

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: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing ...

Supabase-first database schema design for Nick's app stack. Generate production-ready Postgres schemas, Supabase SQL migrations, RLS policies, TypeScript types, seed data, role-aware access patterns, and schema checklists from plain English requirements. Use when starting any new project database, designing or reviewing schemas, adding tables, planning migrations, or turning product requirements into Supabase-ready data models. Bias toward product-led schema design, MVP discipline, core-workflow-first modeling, and avoiding premature table/role sprawl.

Process csv processor operations. Auto-activating skill for Business Automation. Triggers on: csv processor, csv processor Part of the Business Automation skill category. Use when working with csv processor functionality. Trigger with phrases like "csv processor", "csv processor", "csv".

Detect anomaly detector operations. Auto-activating skill for Data Analytics. Triggers on: anomaly detector, anomaly detector Part of the Data Analytics skill category. Use when working with anomaly detector functionality. Trigger with phrases like "anomaly detector", "anomaly detector", "anomaly".

Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making. Use when analyzing financial statements, building valuation models, assessing budget variances, or constructing financial projections and forecasts. Also applicable when users mention financial modeling, cash flow analysis, company valuation, financial projections, or spreadsheet analysis.

Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pa

Use for writing, reviewing, debugging, testing, or optimizing Python NumPy ndarray code. Trigger on array construction, shape/axis reasoning, dtypes and casting, broadcasting, indexing, copies/views, ufuncs, reductions, vectorization, random Generator, linear algebra, FFT, masked/structured arrays, memory layout, or NumPy interoperability. Do not use for pandas/Polars table semantics, JAX/CuPy-only arrays, symbolic SymPy, or pure Python sequences without a NumPy boundary.

Build, review, debug, or test declarative statistical visualizations in Python with Altair and Vega-Lite, including chart marks, typed encodings, transforms, parameters, layers, facets, and specification export.

Build, verify, and debug interactive Python visualizations with Plotly, including Plotly Express, graph_objects, subplots, facets, hover/customdata, axes, legends, FigureWidget events, and HTML/image export.

Provides guidance for using python-xxhash to implement fast non-cryptographic hashing, including algorithm selection, byte encoding, streaming updates, digest representation, and collision-aware identity for checksums, bucketing, and sampling.

Creates executable Polars dataframe contracts using Pandera's Polars backend for runtime validation of schemas, columns, and checks.

Choose between scikit-learn for predictive workflows and statsmodels for inferential modeling, with guidance on preprocessing, leakage prevention, validation, and result handoff.

Access and manage NocoDB databases, tables, and records via REST API. Use when the user wants to view bases, list tables, inspect column schemas, query or filter row data, or insert new records into a self-hosted NocoDB instance. Also use for spreadsheet-style database lookups and data entry.

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.

Master SQL query optimization, indexing strategies, and EXPLAIN analysis to dramatically improve database performance and eliminate slow queries. Use when debugging slow queries, designing database schemas, or optimizing application performance.

Generate PPTX presentations, DOCX documents, XLSX spreadsheets, and PDF reports from structured JSON input using PaperJSX.

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: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

Always start with EXPLAIN ANALYZE to understand execution:

This skill should be used when the user asks about Hyperliquid whale positions, Hyperliquid liquidations, Hyperliquid open interest, Hyperliquid trader analytics, Hyperliquid taker data, smart money on Hyperliquid, or any Hyperliquid-specific query. Use when user says: 'Hyperliquid whales', 'HL whale positions', 'HL liquidations', 'HL open interest', 'HL trader', 'smart money', 'Hyperliquid大户', 'HL鲸鱼', 'HL持仓', 'HL清算', 'HL持仓量', 'HL交易员'. For general crypto prices/news, use aicoin-market. For exchange trading, use aicoin-trading. For Freqtrade, use aicoin-freqtrade.

Query blockchain data via Allium APIs. Supports API key, x402 micropayments, and Tempo auth. Covers prices, wallets, tokens, and SQL analytics.

Import raw data (CSV, XLSX, TXT, or MATLAB tables) into formats used by Sensor Fusion and Tracking Toolbox. Handles both ground truth trajectories and sensor detection data. For truth: builds trackingScenarioRecording, tuning timetable, truthlog, or converted table. For sensor data: builds task-oriented dataFormat structs (preferred) or objectDetection arrays (legacy). Use when importing flight logs, GPS logs, radar detections, IR measurements, lidar/camera bounding boxes, ADS-B data, AIS ship tracks, or any recorded data for use with trackers, filter tuning, or tracker evaluation.

Customer retention analysis, churn prediction, cohort analysis, win-back campaigns, and loyalty program design. Use when the user asks about churn, retention, customer lifetime value, cohort analysis, or win-back strategies.

Run a read-only SQL query against a SQLite database file

Bulk-import credentials into the Paladin vault from .env files, CSV/JSON exports, Bitwarden, or 1Password — or discover where credentials live. Returns names/kinds/counts only; secret values never enter the agent's context.