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Web Scrape

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Barrios88
web-scrape

Web scraping skill for economists and applied researchers. Use this skill whenever the user wants to extract data from websites, scrape tables, download PDFs, access paywalled academic journals (JSTOR, ScienceDirect, Elsevier, NBER, SSRN, AER, etc.), bulk-download papers, scrape government data, hit REST APIs, or build reusable scraping scripts. Triggers on: "scrape", "download PDF", "get data from", "pull from website", "extract table", "journal article", "paywall", "curl", "Playwright", "bulk download", "DOI download", or any request to collect data from an external URL. Always use this skill — even for seemingly simple scrape tasks — because tool selection (curl vs. curl_yale vs. browserless vs. Playwright) matters enormously and the wrong choice silently fails.

Overview

PublisherBarrios88
Repositorybarrios-skills
Skill nameweb-scrape
Stars
64
Forks
30
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Barrios88 on GitHub. Read the source before you install it.

Installation

Install the Web Scrape AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Barrios88/barrios-skills/tree/main/skills/research-tools/web-scrape
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Barrios88/barrios-skills.git /tmp/barrios-skills
mkdir -p .claude/skills
cp -r /tmp/barrios-skills/skills/research-tools/web-scrape .claude/skills/web-scrape
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Web Scrape in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Web Scrape on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Web Scrape is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Barrios Skills — John Barrios's curated workflow for economists and accountants. Prioritize reproducible empirical work, clear identification language, and journal-ready output.

Web Scraping — Economist's Toolkit

Guiding principle: escalate only as needed

Start simple, escalate when blocked. The tiers:

curl / curl_chrome142   →  fastest, works for most static pages and APIs
curl_yale               →  add Yale IP for paywalled journals + PDFs
url-scrape / url-content→  add JS rendering (browserless) for SPAs and dynamic tables
Playwright via WS       →  full browser for login flows, pagination, complex interactions

Never jump to Playwright when curl_chrome142 will do. The escalation cost is real: each tier is slower and more complex.


Quick decision table

TargetTool
Open static page or government datacurl_chrome142
REST API / JSON endpointcurl
JSTOR, ScienceDirect, Elsevier, AER, any paywallcurl_yale
PDF via DOI redirectcurl_yale -sfL -o file.pdf
NBER, SSRN (usually open, sometimes gated)try curl_chrome142 first, then curl_yale
Table that renders via JavaScripturl-scrape -s 'table' or url-content
SPA / React appurl-content -w '.main-selector'
Login flow, multi-page pagination, form submissionPlaywright via WebSocket
Bulk download: list of DOIs / URLsloop + curl_yale (see patterns below)

Tool reference

curl_chrome142 — bot-resistant scraping (no paywall)

Uses curl-impersonate: real Chrome 142 TLS fingerprint, defeats most Cloudflare/Akamai blocks.

bash
# Scrape page text
curl_chrome142 -sf https://example.com | htmlq 'article' --text

# Extract all links
curl_chrome142 -sf https://example.com | htmlq 'a' --attribute href

# Hit a JSON API
curl -sf 'https://api.bls.gov/publicAPI/v2/timeseries/data/' | jq '.Results.series[0].data'

# Download a file
curl_chrome142 -sfL -o paper.pdf https://example.com/paper.pdf

curl_yale — paywalled journal access

Routes through a SOCKS5 proxy on the Yale network. Requires the proxy to be running — see setup-yale.md if curl_yale is unavailable or times out.

bash
# Download a JSTOR article as HTML
curl_yale -sfL 'https://www.jstor.org/stable/10.2307/1234567' -o article.html

# Download a PDF via DOI (follows redirects to publisher PDF)
curl_yale -sfL 'https://doi.org/10.1257/aer.20150154' -o paper.pdf

# Scrape article metadata from ScienceDirect
curl_yale -sf 'https://www.sciencedirect.com/science/article/pii/S0304405X19301234' | htmlq 'meta[name="citation_title"]' --attribute content

# Verify your Yale IP is active
curl_yale -sf https://httpbin.org/ip | jq .

url-scrape — extract specific elements from JS-rendered pages

Returns JSON with text + HTML for each matched CSS selector. Use when the page requires JavaScript to render (inspect page source: if the data isn't there, it's JS-rendered).

bash
# Scrape a dynamic table
url-scrape -s 'table' https://example.com/data | jq '.data[0].results[].html'

# Multiple selectors
url-scrape -s 'h1' -s '.abstract' -s 'table.results' https://journal.com/article

# Wait for content to load, skip images for speed
url-scrape -s '.data-table' -w '.data-table' -r image,stylesheet,font https://spa-app.com

# Save output
url-scrape -s 'table' https://example.com ./tmp/scraped.json

# Extract just the text from results
url-scrape -s 'p.abstract' https://example.com | jq '.data[0].results[].text'

url-content — get full rendered HTML

Use when you need the whole page after JS runs, then pipe to htmlq/pup.

bash
# Get full rendered HTML, extract article
url-content https://example.com | htmlq 'article' --text

# Wait for SPA content
url-content -w '.main-content' -r image,stylesheet,font https://spa-site.com | htmlq 'table'

# Save page for inspection
url-content https://example.com > ./tmp/page.html

Playwright via WebSocket — full browser control

For multi-step scraping only: login flows, pagination clicking, form interaction. See playwright-patterns.md for complete examples.

bash
# Quick template — Python (preferred for data work)
uv run --with playwright python3 -c "
import json, urllib.parse
from playwright.sync_api import sync_playwright

token = '$BROWSERLESS_TOKEN'
host = '$BROWSERLESS_HOST'
launch = urllib.parse.quote(json.dumps({'stealth': True, 'args': ['--disable-blink-features=AutomationControlled']}))
ws = f'wss://{host}/chromium/playwright?token={token}&launch={launch}'

with sync_playwright() as p:
    browser = p.chromium.connect(ws)
    page = browser.new_page()
    page.goto('https://example.com')
    # your logic here
    print(page.content())
    browser.close()
"

Economist patterns

Download a batch of PDFs by DOI

bash
# dois.txt — one DOI per line
while IFS= read -r doi; do
    filename=$(echo "$doi" | tr '/' '_').pdf
    echo "Downloading: $doi → $filename"
    curl_yale -sfL "https://doi.org/${doi}" -o "${OUTPUT_DIR}/${filename}" || echo "FAILED: $doi"
    sleep 1  # be polite
done < dois.txt

Scrape an HTML table to CSV (Python)

bash
python3 -c "
import subprocess, sys
html = subprocess.check_output(['curl_chrome142', '-sf', sys.argv[1]]).decode()
import re
from html.parser import HTMLParser
# Simple: pipe through pandas
" URL

# OR: use pandas directly
python3 -c "
import pandas as pd
tables = pd.read_html('URL')
tables[0].to_csv('data.csv', index=False)
print(f'Found {len(tables)} tables, saved first as data.csv')
"

# For JS-rendered tables:
url-scrape -s 'table' https://example.com | python3 -c "
import json, sys
data = json.load(sys.stdin)
html = data['data'][0]['results'][0]['html']
import pandas as pd, io
df = pd.read_html(io.StringIO(html))[0]
df.to_csv('table.csv', index=False)
print(df.head())
"

Scrape paginated data (static pagination)

bash
# Loop over page numbers
for page in $(seq 1 20); do
    curl_chrome142 -sf "https://example.com/data?page=${page}" | htmlq '.result-item' --text >> results.txt
    sleep 0.5
done

NBER working papers bulk download

bash
# Get paper list for a topic, then download PDFs
curl_chrome142 -sf 'https://www.nber.org/search?q=labor+supply&searchType=Public' | \
  htmlq 'a[href*="/papers/"]' --attribute href | \
  sed 's|/papers/||' | \
  while read paper_id; do
    curl_chrome142 -sfL "https://www.nber.org/papers/${paper_id}.pdf" -o "nber_${paper_id}.pdf"
    sleep 1
  done

SSRN download by paper ID

bash
curl_chrome142 -sfL "https://papers.ssrn.com/sol3/Delivery.cfm/DELIVERY_ID?abstractid=PAPER_ID&type=2" -o paper.pdf
# If blocked: try curl_yale instead

Output format guidance

For analysis (Stata, R, Python): Save scraped tables as CSV. Use pandas.read_html() for HTML tables, or parse JSON from url-scrape → CSV.

For inspection first: Save raw HTML/JSON, then extract. Good for debugging when you're unsure what's on the page.

For bulk PDF datasets: Organize as author_year_keyword.pdf or by DOI slug. Always log successes/failures to a text file when batch downloading.

When choosing output format:

  • Data that looks like a table → CSV
  • Metadata, structured records → JSON or CSV depending on complexity
  • Full article text → plain text or Markdown

Troubleshooting

curl_yale hangs / times out → SOCKS5 proxy not running. See setup-yale.md.

Getting 403 / bot detection → Try curl_yale (adds Yale IP + Chrome fingerprint). If still blocked, escalate to url-scrape / url-content.

Content not in HTML (JS-rendered) → Use url-content or url-scrape. Tip: compare curl_chrome142 -sf URL | htmlq 'body' --text vs. what you see in browser — if they differ, it's JS-rendered.

url-scrape returns empty → Add -w 'SELECTOR' -t 5000 to wait longer for dynamic content.

PDF is HTML (access denied page) → curl_yale not routing through Yale IP. Check proxy. Run curl_yale -sf https://httpbin.org/ip | jq . to verify.

Need help with setup → See setup-general.md for curl-impersonate + htmlq/pup/jq install, and setup-yale.md for the Yale proxy + browserless.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Web Scrape AI skill do?

Web scraping skill for economists and applied researchers. Use this skill whenever the user wants to extract data from websites, scrape tables, download PDFs, access paywalled academic journals (JSTOR, ScienceDirect, Elsevier, NBER, SSRN, AER, etc.), bulk-download papers, scrape government data, hit REST APIs, or build reusable scraping scripts. Triggers on: "scrape", "download PDF", "get data from", "pull from website", "extract table", "journal article", "paywall", "curl", "Playwright", "bulk download", "DOI download", or any request to collect data from an external URL. Always use this s...

Why use Web Scrape on TypingMind?

Because you install it once and use it with any model. Web Scrape is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Web Scrape in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Barrios88/barrios-skills/tree/main/skills/research-tools/web-scrape. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Web Scrape?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Web Scrape?

As many as you like. As long as a model supports skills, you can use Web Scrape with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Web Scrape AI skill free?

Yes. It is published on GitHub by Barrios88 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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