AI Coding Skills for Python

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

Directory and router for the marketing skills library. Use when you need to find the right marketing skill for a task, see what marketing capabilities exist, or get oriented in this plugin. 44 specialist skills across 8 pods (content, SEO + AEO, CRO, channels, growth, intelligence, sales enablement, ops), 59 stdlib Python tools. Routes to one skill — it does not execute marketing work itself.

Auto-detects the current project's technology stack and hot-loads context-appropriate skills at session start using Claude Code's SessionStart hook with reloadSkills: true. Detects Node.js/TypeScript, Next.js, Python, Rust, Go, Ruby, and monorepo layouts. Emits a .claude/dynamic-context.md with the detected stack and recommended skill list, then signals Claude Code to reload skills before the first agent turn.

Probe for hardcoded secrets, injection surfaces, unguarded routes, business logic flaws, and platform-specific weaknesses across backend (Node, Go, Java, Python, Rust), frontend (React, Angular, Vue), and mobile (iOS, Android, Flutter) codebases. Use when performing security audits, vulnerability scans, secrets detection, or penetration testing.

Configures Python projects with modern tooling (uv, ruff, ty). Use when creating projects, writing standalone scripts, or migrating from pip/Poetry/mypy/black.

Fetch financial and market data using the yfinance Python library. Use this skill whenever the user asks for stock prices, historical data, financial statements, options chains, dividends, earnings, analyst recommendations, or any market data. Triggers include: any mention of stock price, ticker symbol (AAPL, MSFT, TSLA, etc.), "get me the financials", "show earnings", "what's the price of", "download stock data", "options chain", "dividend history", "balance sheet", "income statement", "cash flow", "analyst targets", "institutional holders", "compare stocks", "screen for stocks", or any request involving Yahoo Finance data. Always use this skill even if the user only provides a ticker — infer intent from context.

MANDATORY ENTRY POINT for generating or writing tests. Invoke this skill before editing files whenever the user asks to generate tests, write/add unit tests, scaffold a test project or suite, improve/achieve coverage, extend an existing suite to cover an untested method, or test an app, API, service, module, library, or package. Applies to a single function, method or file as much as to a whole project — scope changes how much of the workflow runs, never whether the skill applies. Invoke it when the workspace looks sparse, gutted or partially deleted — then test only the source that remains and never restore missing source. Polyglot: C#/.NET, Python, TypeScript/JavaScript, Go, Rust, Java, Ruby. DO NOT USE FOR: running existing tests (use run-tests); analyzing coverage reports (use coverage-analysis or crap-score); MSTest-specific test authoring or modernization (use writing-mstest-tests).

Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.

Expert in FastAPI Python development with best practices for APIs and async operations

Security audit and vulnerability scanner for AI agent skills before installation. Use when: (1) evaluating a skill from an untrusted source, (2) auditing a skill directory or git repo URL for malicious code, (3) pre-install security gate for Claude Code plugins, OpenClaw skills, or Codex skills, (4) scanning Python scripts for dangerous patterns like os.system, eval, subprocess, network exfiltration, (5) detecting prompt injection in SKILL.md files, (6) checking dependency supply chain risks, (7) verifying file system access stays within skill boundaries. Triggers: "audit this skill", "is this skill safe", "scan skill for security", "check skill before install", "skill security check", "skill vulnerability scan".

Configure and operate VS Code-family IDEs (VS Code, VS Code Insiders, Cursor, VSCodium, Trae, Qoder, CodeBuddy) via CLI and structured JSON files. Qoder and CodeBuddy support is limited to their VS Code-compatible configuration surface, not proprietary AI chat, accounts, models, agents, marketplaces, or internal state.

Identifying and exploiting insecure deserialization vulnerabilities in Java, PHP, Python, and .NET applications to achieve remote code execution during authorized penetration tests.

World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking — using Python (NumPy, Pandas, Scikit-learn), R, and SQL. Use when designing or analysing controlled experiments, building and evaluating classification or regression models, performing causal analysis on observational data, engineering features for structured tabular datasets, or translating statistical findings into data-driven business decisions.

Best practices for analytics, data analysis, and visualization using Python, pandas, matplotlib, seaborn, and Jupyter notebooks. Use when performing exploratory data analysis, building data pipelines, creating statistical visualizations, writing Jupyter notebooks, cleaning and transforming datasets, or implementing analytics dashboards.

Python development guidance with code quality standards, error handling, testing practices, and environment management. Use when writing, reviewing, or modifying Python code (.py files) or Jupyter notebooks (.ipynb files).

Best practices for scikit-learn machine learning, model development, evaluation, and deployment in Python

Expert in Python testing with pytest and test-driven development

Expert in Django Python web development with best practices