web-fetcher
Fetch web pages and extract readable content for AI use. Use when reading, summarizing, or crawling a specific URL or small set of URLs. Prefer low-friction URL-to-Markdown services first, then fall back to browser-based retrieval, search snippets, or cached/indexed copies when sites are protected by Cloudflare or similar bot checks.
Best use case
web-fetcher is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Fetch web pages and extract readable content for AI use. Use when reading, summarizing, or crawling a specific URL or small set of URLs. Prefer low-friction URL-to-Markdown services first, then fall back to browser-based retrieval, search snippets, or cached/indexed copies when sites are protected by Cloudflare or similar bot checks.
Teams using web-fetcher should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/aurthes-web-fetcher/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How web-fetcher Compares
| Feature / Agent | web-fetcher | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Fetch web pages and extract readable content for AI use. Use when reading, summarizing, or crawling a specific URL or small set of URLs. Prefer low-friction URL-to-Markdown services first, then fall back to browser-based retrieval, search snippets, or cached/indexed copies when sites are protected by Cloudflare or similar bot checks.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Web Fetcher
Fetch readable web content with a reliability-first fallback chain.
## Core rule
Do **not** promise direct access to every site. Some sites use Cloudflare, login walls, bot detection, or legal restrictions. In those cases, switch to the next fallback instead of insisting the first method should work.
## Preferred fetch order
### 1) Direct readable fetch
Try lightweight conversion services first:
1. **r.jina.ai**
```
https://r.jina.ai/http://example.com
```
2. **markdown.new**
```
https://markdown.new/https://example.com
```
3. **defuddle**
```
https://defuddle.md/https://example.com
```
For deterministic retries, use the bundled script:
```bash
python {baseDir}/scripts/fetch_url.py "https://example.com/article"
```
The script returns JSON with:
- chosen method
- attempt history
- blocked/thin-content detection
- final content when successful
Use these when the user wants article text, page summaries, or structured extraction from normal public pages.
### 2) Detect failure modes early
Treat the fetch as failed or unreliable if you see signs like:
- `Just a moment...`
- `Performing security verification`
- `Enable JavaScript and cookies`
- CAPTCHA / challenge pages
- login wall instead of target content
- obvious truncation / missing article body
When this happens, **stop treating the result as the page content**.
### 3) Browser fallback for protected sites
For sites blocked behind Cloudflare or requiring real browser execution:
- Prefer a real browser session via OpenClaw browser tools when available.
- If the user is using the Chrome relay/extension, ask them to attach the tab and then inspect the live rendered page.
- Snapshot the page and extract only the needed fields.
Use browser fallback for:
- JS-heavy pages
- Cloudflare-protected pages
- sites that render key content after load
- pages where the direct markdown services return verification screens
### 4) Search / indexed fallback
If direct fetch and browser fetch are not available or still fail:
- search for the exact page / journal / article title
- use search snippets, publisher mirror pages, cached summaries, or secondary sources
- prefer official publisher pages when search can surface the needed field
- clearly label data as secondary-source derived if it was not read directly from the target page
This is often enough for metadata tasks like:
- editor-in-chief names
- journal impact factors
- publication frequency
- ISSN
- institutional affiliations
### 5) Partial-completion mode
If a site is inconsistent, return a mixed result instead of stalling:
- fill the rows that can be verified directly
- mark blocked / unresolved rows clearly
- explain what failed and which fallback was used
## Practical extraction strategy
### For one page
1. Try `r.jina.ai`
2. If blocked, try `markdown.new`
3. If blocked, try `defuddle`
4. If still blocked, use browser tools
5. If browser unavailable, use search/indexed fallback
6. Report confidence level
### For many similar pages
1. Fetch the index/list page first
2. Extract all target URLs or codes
3. Process pages in batches
4. Record success/failure per row
5. Retry only failures with stronger fallback methods
6. Deliver the best complete table possible
## Output guidance
When extracting structured data, prefer columns like:
- source URL
- extraction method (`direct`, `browser`, `search`, `secondary`)
- confidence (`high`, `medium`, `low`)
- note for blocked/unverified rows
## Examples
- User: "Read this article" → direct fetch first
- User: "What does this page say?" → direct fetch, then browser fallback if blocked
- User: "Crawl this journal site" → index page first, then batched extraction with fallback chain
- User: "Cloudflare blocked it" → switch to browser or search fallback, do not keep retrying the same failed methodRelated Skills
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