claude-code-basecamp

Use when writing Python error handling code. Provides specific rules for exception chaining and custom error hierarchies.

Use when reviewing code for security issues.

Use when implementing features, fixing bugs, or refactoring code that may invalidate existing docs. Detects stale documentation by matching the code diff against in-repo doc files and applies targeted updates. Relevant when code changes rename, remove, or add APIs, fields, config keys, or CLI flags. Also applies when the user says "update docs", "check docs", or "are the docs stale", or when reviewing a branch for documentation accuracy.

Helps you write better Python code by following clean code principles and software engineering best practices.

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.

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.

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.

Team-specific Python conventions — credential management with dotenv, API client rules, LLM response parsing, TDD workflow, and testing patterns for data pipelines.

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.

Team conventions for Python data pipelines — stage structure, JSON output format, debugging workflow, and anti-patterns. Supplements standard patterns with team-specific rules.