quality▌

Principles and practices for writing maintainable, readable, and reliable code.

Quick reference for Python code quality tools: ruff (linting & formatting), ty (type checking).

ln-624-code-quality-auditor

This skill provides expert guidance on ensuring Mapbox style quality through validation, accessibility, and optimization tools.

Production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines.

Comprehensive document control system design, regulatory documentation oversight, and change management for medical device organizations. \n \n Covers document control system architecture, regulatory documentation frameworks for EU MDR, FDA, and ISO standards, plus change control and configuration management workflows \n Includes document management system (DMS) implementation strategy with system selection, design, validation, and deployment guidance \n Addresses advanced applications: technica

You are a senior quality engineer with 15+ years in regulated manufacturing environments — FDA 21 CFR 820 (medical devices), IATF 16949 (automotive), AS9100 (aerospace), and ISO 13485 (medical devices). You manage the full non-conformance lifecycle from incoming inspection through final disposition. Your systems include QMS (eQMS platforms like MasterControl, ETQ, Veeva), SPC software (Minitab, InfinityQS), ERP (SAP QM, Oracle Quality), CMM and metrology equipment, and supplier portals. You sit

Context-driven code reviews prioritizing quality, security, testability, and maintainability. \n \n Organizes feedback into four priority levels: blockers (must fix), major issues (should fix), minor improvements, and suggestions, with clear templates and rationale for each \n Covers five core review areas: logic correctness, security risks, test coverage, performance issues, and error handling; explicitly excludes style nitpicking and formatting \n Recommends reviewing code in chunks under 400

Write-time code quality enforcement with auto-formatting, linting, and Claude-powered subprocess fixes on every file edit. \n \n Three-phase architecture: silent auto-formatting, violation collection, and tiered model routing (Haiku for style, Sonnet for complexity, Opus for types) to fix unfixable issues \n Supports Python, TypeScript, Shell, YAML, JSON, TOML, Markdown, and Dockerfile with 20+ linters including ruff, biome, shellcheck, and hadolint \n Config protection via PreToolUse and Stop h

Validate data pipelines with Great Expectations, dbt tests, and data contracts. \n \n Covers three complementary frameworks: Great Expectations for statistical and schema validation, dbt tests for transformation layer checks, and data contracts for cross-team data agreements \n Includes six core quality dimensions (completeness, uniqueness, validity, accuracy, consistency, timeliness) with ready-to-use expectation patterns and custom test examples \n Provides checkpoint automation for CI/CD inte