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Help recreate Our World in Data (OWID) charts (line, bar, bubble) as faithfully as possible in this repo’s Jupyter notebooks, using pandas + Matplotlib (and Plotly when requested), with correct data,

Process JSON, YAML, CSV, and XML data (jq, yq, awk).

Telegram DM sending from CSV, rate limiting, idempotency

You are a senior FP&A professional. You build financial models, run variance analysis, produce board-ready reports, and turn raw numbers into strategic decisions. You work with whatever data the user

- `daily` = `摸鱼日报`

Interpret CSV, JSON, and structured data, extract insights, identify patterns, and recommend appropriate visualizations.

在新数据集接入前检查字段、单位、缺失率、异常值与可用性。；use for data, dataset, audit workflows；do not use for 伪造统计结果, 替代正式数据治理平台.

分析退货原因并区分产品问题、预期错配、物流问题和描述问题。；use for ecommerce, returns, analysis workflows；do not use for 伪造订单数据, 替代客服系统.

Double-entry, full-cycle accounting suite built for AI agents. Converts bank CSVs, OFX, and QBO files into balanced, auditable books — balance sheet, income statement, general ledger, trial balance. All data stays in a single local SQLite file.

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Upload any data file (CSV, Excel, JSON, SQL export) and get a complete analysis report with insights, anomalies, and actionable recommendations — no code required.

3-in-1 China 12306 query — tickets, route stops, transfer plans. Zero login. Filter 高铁/动车/火车 by type, time, duration. Pure Python, text/json/csv output. 火车票/余票/经停站/中转换乘.

Create project timeline visualizations for grant proposals

Analyze bank transactions, categorize spending, track monthly budgets, detect overspending and anomalies. Outputs interactive HTML report.

与 Quicker 自动化工具集成，读取、搜索和执行 Quicker 动作列表。支持 CSV 和数据库双数据源，智能匹配用户需求并调用本地 QuickerStarter 执行。

AKShare财经数据接口库封装，提供股票、期货、期权、基金、外汇、债券、指数、加密货币等金融产品的基本面数据、实时和历史行情数据、衍生数据。

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Read analysis results. Use when user asks about maximum stress, extracting displacements, reaction forces, or exporting results. Post-processes ODB files.

When the user needs to set up multiple academic courses in a learning management system (Canvas/LMS) from structured data sources. This skill automates the entire workflow extracting course schedules from emails/attachments, matching instructors from CSV files, creating courses, enrolling teachers, publishing announcements with class details, uploading syllabi, enabling resource sharing for instructors teaching multiple courses, and publishing all courses. Triggers include course schedule setup, Canvas/LMS administration, academic term preparation, instructor assignment, syllabus distribution, and multi-course management.

Advanced analytics including machine learning, predictive modeling, and big data techniques

Complete guide for Apache Spark data processing including RDDs, DataFrames, Spark SQL, streaming, MLlib, and production deployment

Implement comprehensive audit logging for all admin actions, capturing user ID, action type, entity changes, IP address, and user agent. Use when tracking system activities or adding audit trails.

Analyzes bear-put-spread debit spreads for bearish directional plays with defined risk. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0, scipy>=1.10.0. Use when expecting moderate price decline, comparing put spread configurations, analyzing debit spread opportunities, or evaluating defined-risk bearish positions on mid to large-cap stocks.

Apache Spark, Hadoop, distributed computing, and large-scale data processing for petabyte-scale workloads

Price fixed income securities using present value, yield-to-maturity, and market conventions. Handles treasuries, corporates, municipals with various coupon frequencies. Requires numpy>=1.24.0, pandas>=2.0.0, scipy>=1.10.0. Use when valuing bonds, calculating accrued interest, or analyzing price sensitivity to yield changes.

Analyzes bull-call-spread debit spreads for bullish directional plays with defined risk. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0, scipy>=1.10.0. Use when expecting moderate price increase, comparing vertical spread configurations, analyzing debit spread opportunities, calculating Greeks for multi-leg positions, or evaluating defined-risk bullish strategies on liquid optionable securities.

Answer analytical questions about carbon accounting data using internal datasets, APIs, and emission factor calculations.

This skill provides systematic fact-checking, source verification, and quality assurance methodologies for competitive research deliverables. It works as a companion to the competitive-research-bright

Assess credit risk and default probability for bonds using credit spreads, rating transitions, and recovery analysis. Requires numpy>=1.24.0, pandas>=2.0.0, scipy>=1.10.0. Use when evaluating corporate bonds, analyzing credit events, estimating default probabilities, or managing credit portfolio risk.

Use when creating maps, working with geographic projections, or processing GeoJSON data. Invoke for world maps, choropleth maps, projection types, geo path generators, spherical geometry, or geographic feature manipulation.

Master machine learning, data engineering, AI engineering, MLOps, and prompt engineering. Build intelligent systems from data pipelines to production AI applications with LLMs, agents, and modern frameworks.

Master machine learning, data engineering, AI engineering, LLMs, prompt engineering, and MLOps. Build intelligent systems with Python.

Use this skill when the user needs to analyze, clean, or prepare datasets. Helps with listing columns, detecting data types (text, categorical, ordinal, numeric), identifying data quality issues, and cleaning values that don't fit expected patterns. Invoke when users mention data cleaning, data quality, column analysis, type detection, or preparing datasets.

Clean and standardize vehicle insurance data following established business rules.

Data cleaning, preprocessing, and quality assurance techniques

Data governance strategy, quality validation rules, and data dictionary management for vehicle insurance platform. Use when defining data quality standards, implementing validation rules, managing field mappings, resolving data conflicts, or establishing data governance processes. Covers data cleaning standards, quality metrics, and mapping management.

Process JSON with jq and YAML/TOML with yq. Filter, transform, query structured data efficiently. Triggers on: parse JSON, extract from YAML, query config, Docker Compose, K8s manifests, GitHub Actions workflows, package.json, filter data.

Documentation of available data science libraries (scipy, numpy, pandas, sklearn) and best practices for statistical analysis, regression modeling, and organizing analysis scripts. **CRITICAL:** All analysis scripts MUST be placed in reports/{topic}/scripts/, NOT in root scripts/ directory.

Connect your own data source to replace the demo unicorns data. Use when the user wants to use their own database URL or CSV file instead of the sample data. Triggers on requests to connect database, import CSV, change data source, use own data, or switch from demo data.

This skill should be used when reading any tabular data file (Excel, CSV, Parquet, ODS). It automatically detects and fixes common data issues including multi-level headers, encoding problems, empty rows/columns, and data type mismatches. Returns a clean DataFrame ready for analysis with zero user intervention.

Compare two datasets to find differences, added/removed rows, changed values. Use for data validation, ETL verification, or tracking changes.

Merge pandas DataFrames from multiple construction sources. Handle different schemas, keys, and data quality issues.

Differential binding analysis using DiffBind. Compare ChIP-seq peaks between conditions with statistical rigor. Requires replicate samples. Outputs differentially bound regions with fold changes and p-values. Use when comparing ChIP-seq binding between conditions.

Coordinate distributed processing with parallel execution, forked skills, and isolated work units. Use when you need distributed processing or isolated work units. Not for sequential tasks or single-threaded workflows.

Set up earnings tasks for a ticker with CSV persistence and Claude task tracking

Use for creating websets, running searches, importing CSV data, managing items, and adding enrichments to extract structured data.

**Why?** Manually tracking YouTube videos is tedious and error-prone. This skill automates extracting video metadata (titles, durations, URLs) into a CSV for systematic transcript downloading and anal

Expert guidance for Microsoft Fabric Real-Time Intelligence (RTI) using the Fabric RTI MCP Server. Execute KQL queries on Eventhouse, manage Eventstreams for real-time data processing, create Activator triggers for alerting, and manage Map items. Use when working with Fabric RTI, KQL, real-time analytics, streaming data, or event-driven applications.

Gemma PQN Data Processor