paddleocr
Reads text from scanned pages, photos and PDFs and returns it as structured Markdown or JSON, so you can pull tables, formulas and headers out of a document without retyping them. Covers many languages and works as input for AI tools.
Scrape JavaScript-heavy sites and extract structured data via reusable CSS schemas.
Claude Code
$ npx -y skills add brettdavies/crawl4ai-skill --agent claude-code
Codex
$ npx skills add brettdavies/crawl4ai-skill -a codex
Cursor
$ npx skills add brettdavies/crawl4ai-skill -a cursor
Gemini CLI
$ npx skills add brettdavies/crawl4ai-skill -a gemini-cli
GitHub Copilot
$ npx skills add brettdavies/crawl4ai-skill -a github-copilot
OpenCode
$ npx skills add brettdavies/crawl4ai-skill -a opencode
Windsurf
$ npx skills add brettdavies/crawl4ai-skill -a windsurf
Scrape JavaScript-heavy sites and extract structured data via reusable CSS schemas. A portable agent skill that wraps the Crawl4AI CLI and Python SDK, written in the Anthropic SKILL.md format and consumable by any agent host that loads SKILL.md-format bundles (Claude Code, Codex, Cursor, OpenCode, Cline, and others).
Verified against Crawl4AI library version 0.8.9 (pinned in VERSION).
wait_until=networkidle defaultscrwl command-line tool and the crawl4ai Python SDKClone the repo into the skills directory your agent host loads from:
# Claude Code
git clone https://github.com/brettdavies/crawl4ai-skill.git ~/.claude/skills/crawl4ai
For other agent hosts (Codex, Cursor, OpenCode, Cline, custom agents), clone into whichever directory your host scans
for SKILL.md-format bundles. Refer to your host's documentation for the skills directory location. The bundle root
contains SKILL.md, so the skill registers automatically once the directory is on the host's skills search path.
The skill calls into the Crawl4AI Python library, which must be installed in the runtime your agent uses:
pip install crawl4ai
crawl4ai-setup
crawl4ai-doctor
crawl4ai-doctor validates the install and confirms a headless browser is available.
CLI:
crwl https://example.com -c "wait_until=networkidle,page_timeout=60000" -o markdown
crwl https://example.com -o json -v --bypass-cache
Python SDK:
import asyncio
from crawl4ai import AsyncWebCrawler
async def main():
async with AsyncWebCrawler() as crawler:
result = await crawler.arun("https://example.com")
print(result.markdown[:500])
asyncio.run(main())
| Path | Contents |
|---|---|
SKILL.md | Entry point: trigger conditions, defaults, routing to specialized pipelines |
references/ | Nine reference guides for CLI, SDK, extraction, filtering, anti-detection, URL discovery, escalation |
scripts/ | Six PEP 723 helper scripts for crawl / extract / batch workflows |
templates/ | Reusable YAML/JSON templates for browser, crawler, filters, and extraction strategies |
evals/ | Four eval scenarios for verifying skill behavior end-to-end |
fixtures/ | Schema-generation reference fixture (sample HTML, expected schema, expected JSON output) |
tests/ | Pytest suite covering basic crawling, markdown generation, extraction, advanced patterns, and fixtures |
VERSION | Pinned Crawl4AI library version the skill is verified against |
LICENSE-APACHE, LICENSE-MIT, LICENSE | Dual license texts and summary (SPDX MIT OR Apache-2.0) |
crwl https://docs.example.com -o markdown > docs.md
# Derive the schema once (uses LLM)
./scripts/generate_schema.py https://shop.example.com "products with name, price, image" shop_schema.json
# Apply the saved schema (no LLM cost per request)
./scripts/extract_with_schema.py https://shop.example.com shop_schema.json products.json
for url in news1.com news2.com news3.com; do
crwl "https://$url" -f templates/filter_bm25.yml -o markdown-fit
done
| Script | Purpose |
|---|---|
scripts/basic_crawler.py <url> | One URL → markdown + screenshot |
scripts/batch_crawl.py <urls.txt> | Many URLs → markdown files |
scripts/batch_extract.py <urls.txt> <schema.json> | Many URLs + schema → JSON |
scripts/generate_schema.py <url> "<instruction>" | Derive a reusable CSS schema (one-time LLM call) |
scripts/extract_with_schema.py <url> <schema.json> | Apply a saved schema (no LLM) |
scripts/extract_with_llm.py <url> "<instruction>" | Per-request LLM extraction (expensive; one-off only) |
cd tests
python run_all_tests.py
Dual-licensed under Apache License 2.0 (LICENSE-APACHE) or MIT License (LICENSE-MIT) at
your option. SPDX identifier: MIT OR Apache-2.0. See LICENSE for the full notice.
Contributions welcome. Open a pull request.
See CHANGELOG.md.
Reads text from scanned pages, photos and PDFs and returns it as structured Markdown or JSON, so you can pull tables, formulas and headers out of a document without retyping them. Covers many languages and works as input for AI tools.
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Scrape JavaScript-heavy sites and extract structured data via reusable CSS schemas. Install it with the command on its page. It includes crawl4ai-skill. Its skills do not fire on their own yet. Free and open source.
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