القائمة
Build production-ready AI-powered data collection systems -- three-layer stack (collect, enrich, store), LLM batching, model fallbacks, and GitHub Actions deployment.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots.
Clean Architecture patterns for Android and Kotlin Multiplatform projects -- module structure, dependency rules, UseCases, Repositories, and data layer patterns.
Performance baseline and regression detection -- page performance, API latency, build times, and before/after comparison.
Turn a one-line objective into a step-by-step construction plan for multi-session, multi-agent engineering projects. Each step has a self-contained context brief so a fresh agent can execute it cold.
Automated visual testing and interaction verification -- smoke tests, interaction tests, visual regression, and accessibility audits using browser automation.
Bun as runtime, package manager, bundler, and test runner. When to choose Bun vs Node, migration notes, and Vercel support.
استنادا إلى تصنيف SOC المهني
| name | data-scraper-agent |
| description | Build production-ready AI-powered data collection systems -- three-layer stack (collect, enrich, store), LLM batching, model fallbacks, and GitHub Actions deployment. |
| origin | ECC |
Build production-ready AI-powered data collection systems that run free on GitHub Actions.
requests + BeautifulSoup for HTMLplaywright for JavaScript-rendered sitesAutomatically cascade through models:
Track positive/negative user decisions in feedback.json to bias future scoring without infrastructure changes.
Run entirely on GitHub Actions scheduler (free for public repos). Manage configuration through config.yaml rather than hardcoded values.