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Angular is Google's TypeScript-based frontend framework for building scalable single-page applications. It provides components, dependency injection, RxJS-based reactivity, routing, forms, HTTP client, and a powerful CLI for development.

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Designs and implements website analytics tracking with Google Analytics 4 and Google Tag Manager: a written tracking plan, event and parameter names that pass GA4's rules, gtag.js or data-layer code, key events, consent mode, UTM conventions, and a verification pass. Use when someone says "set up GA4", "add conversion tracking", "track this form or button", "write a tracking plan", "GTM data layer", "UTM parameters", "why are my events missing or counted twice", or "audit our analytics setup".

日本語の概要は準備中です。原文の説明を表示しています。

Alpine.js is a lightweight JavaScript framework for adding interactivity to HTML markup. It provides reactive data, event handling, and DOM manipulation through HTML attributes — like a modern jQuery replacement with declarative syntax.

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Restructure and optimize alert rules for monitoring platforms (Sentry, PagerDuty, Datadog, OpsGenie). Use when someone asks to "reduce alert noise", "fix alert fatigue", "create alert rules", "set up escalation policies", "tune alerting thresholds", or "create on-call runbooks". Generates platform-specific alert configurations and tiered escalation policies.

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Manage database migrations for SQLAlchemy with Alembic. Use when a user asks to version database schemas, autogenerate migration scripts from models, run or roll back migrations, set up async SQLAlchemy migrations, or handle schema changes safely in production.

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Airtable is a hosted spreadsheet-database; its Web API reads and writes records, table schema, attachments and webhooks over REST. Use when tasks involve reading or writing Airtable data, importing or upserting rows from a CSV or another system, syncing external sources with Airtable bases, reacting to record changes with webhooks, connecting users through Airtable OAuth, or debugging 422 and 429 responses from api.airtable.com.

日本語の概要は準備中です。原文の説明を表示しています。

Apache Airflow is a platform for programmatically authoring, scheduling, and monitoring workflows. Learn to write DAGs, use operators, set up connections, configure scheduling, and deploy with Docker Compose.

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Audit Wi-Fi networks with the aircrack-ng suite. Use when a user asks to test their own wireless network, capture WPA2 handshakes, crack captured handshakes offline, put an adapter into monitor mode, or perform a wireless pentest under an authorized engagement.

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You are an expert in Airbyte, the open-source data integration platform with 300+ pre-built connectors. You help developers sync data from SaaS tools, databases, and APIs into data warehouses and lakes — handling incremental syncs, CDC (Change Data Capture), schema evolution, and error recovery for production data pipelines.

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You are an expert in the Vercel AI SDK, the TypeScript toolkit for building AI-powered applications. You help developers integrate LLMs (OpenAI, Anthropic, Google, Mistral, Ollama) with React Server Components, streaming UI, tool calling, structured output with Zod schemas, RAG pipelines, multi-step agents, and edge-compatible AI features — the standard way to add AI to Next.js, Nuxt, SvelteKit, and any Node.js app.

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Run autonomous AI-driven penetration tests on web applications using tools like Shannon, PentAGI, and similar frameworks. Use when tasks involve setting up automated penetration testing pipelines, combining AI agents with security tools (nmap, subfinder, nuclei, sqlmap), building autonomous exploit chains, generating pentest reports with proof-of-concept exploits, or integrating AI pentesting into CI/CD pipelines. Covers the full pentest lifecycle from reconnaissance to reporting using AI orchestration.

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Implement safety guardrails for AI systems — content filtering, prompt injection detection, output validation, bias mitigation, and responsible AI practices. Use when tasks involve adding safety layers to LLM applications, detecting prompt injection attacks, filtering harmful content, implementing rate limiting for AI APIs, validating LLM outputs against schemas, building moderation pipelines, or ensuring AI systems comply with safety policies.

日本語の概要は準備中です。原文の説明を表示しています。