Documentation

Write, test, and maintain Go code following idiomatic patterns, standard library conventions, and Go best practices. Use when working with Go/Golang projects, writing Go code, or reviewing Go implementations.

Provides reusable markdown templates and outlines for OpenSpec proposals, design docs, ADRs, READMEs, and changelogs.

Docusaurus build health validation and deployment safety for Claude Skills showcase. Pre-commit MDX validation (Liquid syntax, angle brackets, prop mismatches), pre-build link checking, post-build health reports. Activate on 'build errors', 'commit hooks', 'deployment safety', 'site health', 'MDX validation'. NOT for general DevOps (use deployment-engineer), Kubernetes/cloud infrastructure (use kubernetes-architect), runtime monitoring (use observability-engineer), or non-Docusaurus projects.

Enterprise Nextra documentation framework with Next.js

학습 중 떠오른 아이디어와 메모를 아카이브 파일에 시간순으로 기록합니다. `scripts/study-note-helper.sh`를 사용하여 현재 KATA 프로젝트의 docs/study/아카이브.md에 노트를 추가합니다.

Use when receiving code review feedback (especially if unclear or technically questionable), when completing tasks or major features requiring review before proceeding, or before making any completion/success claims. Covers three practices - receiving feedback with technical rigor over performative agreement, requesting reviews via code-reviewer subagent, and verification gates requiring evidence before any status claims. Essential for subagent-driven development, pull requests, and preventing false completion claims.

Label taxonomy and triage workflow for GitHub issues. Defines type labels (bug/feature/enhancement/docs/chore), priority levels (critical/high/medium/low), status labels, and triage decision workflow. Use when categorizing and prioritizing issues.

Expert skill for writing production-grade Python tests using pytest with modern fixtures, parametrization, and coverage integration.

Test development with pytest, fixtures, and mocking. Use for writing tests, test patterns, coverage, parametrization, and debugging test failures.

This skill should be used when users request research on any topic that requires web search, source analysis, and structured documentation saved to a file. It provides a systematic workflow for gathering information from multiple web sources, synthesizing findings into a comprehensive document, and saving results to a user-specified file path. This is a general-purpose research skill that works across all domains and languages.

Write and maintain Architecture Decision Records (ADRs) when meaningful technical or architecture decisions are made. Use when introducing dependencies, changing APIs, adopting patterns, or making hard-to-reverse decisions.

Knowledge retrieval from multiple sources. Search docs, web, and code with intelligent routing.

Comprehensive Python testing best practices, pytest conventions, test structure patterns (AAA, Given-When-Then), fixture usage, mocking strategies, code coverage standards, and common anti-patterns. Essential reference for code reviews, test writing, and ensuring high-quality Python test suites with pytest, unittest.mock, and pytest-cov.

Expert in Pattern Stack framework architecture. Auto-activates when writing backend code using Pattern Stack's Atomic Architecture, Field abstraction, and pattern composition system.

Performs web searches using Gemini CLI headless mode with google_web_search tool. Includes intelligent caching, result validation, and analytics. Use when searching for current information, documentation, or when the user explicitly requests web search.

Use when writing Python code with the Affinity SDK, or when user asks about "affinity-sdk", "affinity package", typed IDs, async Affinity client, pagination, or Python scripts for Affinity CRM.

Use when encountering unfamiliar import statements, exploring dependency APIs, or when user asks "what's import X" or "what does X do". Generates on-demand API documentation for Swift package dependencies.

Calculate and interpret effect sizes for statistical analyses. Use when: (1) Reporting research results to show practical significance, (2) Meta-analysis to combine study results, (3) Grant writing to justify expected effects, (4) Interpreting published studies beyond p-values, (5) Sample size planning for power analysis.

Expert in PDF content extraction and analysis. **Use whenever the user mentions PDFs, .pdf files, or requests to extract, read, parse, analyze, convert, or process PDF documents.** Handles text extraction, image extraction, converting PDFs to markdown or other formats, batch PDF processing, and analyzing PDF document structure for AI processing. Uses a fast Go binary with Vertex AI Gemini for intelligent image analysis. Supports two methods - preferred binary-based extraction (default) and alternative image-based extraction (when explicitly requested). (project, gitignored)

Guide for using pytest-mock plugin to write tests with mocking. Use when writing pytest tests that need mocking, patching, spying, or stubbing. Covers mocker fixture usage, patch methods, spy/stub patterns, and assertion helpers.