# ScrapeRL Comprehensive Test Report
Generated: 2026-04-05 15:51:44

## Test Summary
| Test # | Target | Instructions | Format | Status | Steps |
|--------|--------|--------------|--------|--------|-------|
| 1 | HackerNews | Top 10 headlines | JSON | ✅ PASS | 19 |
| 2 | Wikipedia | AI article info | JSON | ✅ PASS | 25 |
| 3 | StackOverflow | Top voted questions | JSON | ✅ PASS | 19 |
| 4 | PyPI | NumPy package info | JSON | ✅ PASS | 19 |
| 5 | Reddit | Programming posts | JSON | ✅ PASS | 19 |
| 6 | MDN Docs | JavaScript overview | Markdown | ✅ PASS | 25 |
| 7 | DuckDuckGo | ML search results | JSON | ✅ PASS | 19 |
| 8 | GitHub | VSCode repo stats | JSON | ✅ PASS | 19 |
| 9 | NPM | React package details | JSON | ✅ PASS | 19 |
| 10 | Kaggle | Popular datasets | CSV | ✅ PASS | 25 |

## Results: 10/10 Tests Passed (100%)

## Intelligent Navigation Features Tested
- ✅ GitHub Trending detection and navigation
- ✅ Multi-field extraction (title, content, links, meta, images, data, scripts, forms, tables)
- ✅ CSV output format generation
- ✅ JSON output format generation
- ✅ Markdown output format generation
- ✅ Memory persistence
- ✅ Plugin integration (mcp-browser, mcp-html, skill-extractor, skill-navigator)
- ✅ Sandbox artifact creation

## GitHub Trending Scraper Test
Requested: "Get me all trending repo" from https://github.com
Result: Successfully navigated to GitHub trending page and extracted:
- 8 trending repositories with username, repo_name, stars, forks
- CSV output generated and saved to sandbox

## Sample Extracted Data (GitHub Trending)
\\\csv
username,repo_name,stars,forks
Blaizzy,mlx-vlm,"3,749",410
onyx-dot-app,onyx,"24,566","3,294"
Yeachan-Heo,oh-my-codex,"16,124","1,521"
siddharthvaddem,openscreen,"21,264","1,445"
telegramdesktop,tdesktop,"30,915","6,527"
block,goose,"35,957","3,383"
microsoft,agent-framework,"8,838","1,447"
sherlock-project,sherlock,"79,692","9,277"
\\\

## Configuration
- Backend: FastAPI on port 8000
- Frontend: Vite/React on port 3000
- AI Provider: NVIDIA (llama-3.3-70b)
- Docker: docker-compose.yml

## Conclusion
The ScrapeRL intelligent agentic scraper is fully operational with:
1. Intelligent navigation based on user instructions
2. GitHub trending repository extraction
3. Multi-format output (JSON, CSV, Markdown)
4. Plugin system integration
5. Memory persistence
6. Sandbox artifact management