Scripting

Python Linter (user)

Name code by what it does in the domain, not how it's implemented or its history

Expert for Claude Code plugin and marketplace development. Use when creating, editing, or optimizing skills (SKILL.md), commands (*.md), agents, hooks (hooks.json), scripts, or plugin.json files. Ensures consistency with official best practices from code.claude.com documentation. Activates for any file in plugins/ directory.

This skill should be used when users request research on topics with compiled results saved to a file. It handles web searches, fetching authoritative sources, synthesizing information, and generating structured reports. Trigger phrases include "research and save", "search and summarize", "compile information about", or requests to investigate topics and save results.

Advanced Python testing strategies with Pytest, covering fixtures, matrix testing with parametrization, and async test architecture. Triggers: pytest, fixtures, parametrize, pytest-asyncio, matrix-testing, yield-fixture.

TypeScript project initialization best practices

Utility scripts directory configuration (/scripts) for MetaSaver monorepos including setup automation, environment management, and cross-platform support. Includes 4 critical standards (setup scripts, cross-platform support, error handling, documentation). Use when creating or auditing /scripts directory with Node.js and shell utility scripts.

파싱된 리서치 데이터를 기반으로 TypeScript 테스트 데이터 파일 생성. 새 테스트 추가, subjects/*.ts 파일 생성 시 사용.

Pulumi infrastructure as code using Python with Pulumi Cloud and ESC integration. Use when working with Pulumi Python projects, ESC environments, dynamic secrets, OIDC credentials, or infrastructure automation with Python.

Use when implementing QR code scanning - provides complete patterns for camera-based scanning, classifying scanned content (Bitcoin addresses, Lightning invoices, npubs, Cashu tokens), handling camera permissions, and paste from clipboard functionality

Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses. This is a test.

Generates production-ready BAML applications from natural language requirements. Creates complete type definitions, functions, clients, tests, and framework integrations for data extraction, classification, RAG, and agent workflows. Queries official BoundaryML repositories via MCP for real-time patterns. Supports multimodal inputs (images, audio), 6 programming languages (Python, TypeScript, Ruby, Java, Go, C#), 10+ frameworks, 50-70% token optimization, and 95%+ compilation success.

Generates Babel configuration for JavaScript transpilation in tests. Creates babel.config.js file.

Generate standalone Python scripts (not packaged projects) with PEP 723 inline metadata, a uv run shebang, and Typer CLI scaffolding. Use when the user wants a one-off executable script, automation tool, or runnable file that should declare dependencies inline and not live inside a package or project.

Modern TypeScript patterns, best practices, and advanced type system features for writing type-safe, maintainable code

Browser automation, debugging, and performance analysis using Puppeteer CLI scripts. Use for automating browsers, taking screenshots, analyzing performance, monitoring network traffic, web scraping, form automation, and JavaScript debugging.

Creates Playwright test scripts from natural language user flow descriptions. This skill should be used when generating E2E tests from scenarios, converting user stories to test code, recording user flows, creating test scripts from descriptions like "user signs up and creates project", or translating acceptance criteria into executable tests. Trigger terms include playwright test, e2e flow, user scenario, test from description, record flow, user journey, test script generation, acceptance test, behavior test, user story test.

Machine learning integration patterns for rRNA-Phylo covering three use cases - rRNA sequence classification (supervised learning with sklearn/PyTorch), multi-tree consensus (ensemble methods), and generative tree synthesis (GNNs/transformers). Includes feature engineering, model training, hyperparameter tuning, model serving, versioning, and evaluation metrics for bioinformatics ML workflows.

Google Apps Script開発スキル。GAS作成、スプレッドシート自動化、Google API連携時に使用。

Data validation and settings management using Python type annotations with Pydantic v2