Security Check
Scan Python projects for credential leaks, secrets in code, insecure patterns, LLM API key exposure, PII leakage to external AI services, and .env/.gitignore misconfigurations. Focused on data science pipelines handling API keys, tokens, and LLM integrations.
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Bad Skill
A Claude skill from Benkapner/claude-code-basecamp.
Brainstorming
Use when the user asks to design, plan, or explore approaches before implementing — creating features, building components, or adding functionality that would benefit from design exploration first.
Coding Standards
Baseline cross-project coding conventions for naming, readability, immutability, and code-quality review. Use detailed frontend or backend skills for framework-specific patterns.
Refactoring Patterns
Measurement-driven code refactoring — profile before changing, measure after, keep only if metrics improve. Covers complexity reduction, extraction patterns, and bulk refactoring for mechanical changes across many files.
Security Skill
Use when reviewing code for security issues.
Verification Loop
Unified verification engine for Python data science projects. Covers environment checks, type checking, linting, tests, security scans, code review with DS anti-patterns, and notebook checks. Commands (/verify, /quality-gate) invoke different subsets of this skill.
Data Pipeline Patterns
Team conventions for Python data pipelines — stage structure, JSON output format, debugging workflow, and anti-patterns. Supplements standard patterns with team-specific rules.
Good Skill
Use when writing Python error handling code. Provides specific rules for exception chaining and custom error hierarchies.
Clean Code Guide
Helps you write better Python code by following clean code principles and software engineering best practices.
Python Conventions
Team-specific Python conventions — credential management with dotenv, API client rules, LLM response parsing, TDD workflow, and testing patterns for data pipelines.