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hyperpuncher/dotagents · updated Apr 8, 2026

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$npx skills add https://github.com/hyperpuncher/dotagents --skill scrapling
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summary

Scrapling is a powerful Python web scraping library with a comprehensive CLI for extracting data from websites directly from the terminal without writing code. The primary use case is the extract command group for quick data extraction.

skill.md

scrapling

Scrapling is a powerful Python web scraping library with a comprehensive CLI for extracting data from websites directly from the terminal without writing code. The primary use case is the extract command group for quick data extraction.

Installation

Install with the shell extras using uv:

uv tool install "scrapling[shell]"

Then install fetcher dependencies (browsers, system dependencies, fingerprint manipulation):

scrapling install

Update to the latest version:

uv tool update "scrapling[shell]"

Extract Commands (Primary Usage)

The scrapling extract command group allows you to download and extract content from websites without writing any code. Output format is determined by file extension:

Note: All examples use --ai-targeted by default. This flag extracts only main body content, strips noise tags (script, style, noscript, svg), removes hidden elements, strips zero-width unicode characters, and removes HTML comments - ideal when output is destined for an AI model.

  • .md - Convert HTML to Markdown
  • .html - Save raw HTML
  • .txt - Extract clean text content

Quick Start

# Basic website download as text
scrapling extract get "https://example.com" page_content.txt --ai-targeted

# Download as markdown
scrapling extract get "https://blog.example.com" article.md --ai-targeted

# Save raw HTML
scrapling extract get "https://example.com" page.html --ai-targeted

Decision Guide: Which Command to Use?

Use Case Command
Simple websites, blogs, news articles get
Modern web apps, dynamic content (JavaScript) fetch
Protected sites, Cloudflare, anti-bot stealthy-fetch
Form submissions, APIs post, put, delete

HTTP Request Commands

GET Request

Most common command for downloading website content:

# Basic download
scrapling extract get "https://news.site.com" news.md --ai-targeted

# Download with custom timeout
scrapling extract get "https://example.com" content.txt --timeout 60 --ai-targeted

# Extract specific content using CSS selectors
scrapling extract get "https://blog.example.com" articles.md --css-selector "article" --ai-targeted

# Send request with cookies
scrapling extract get "https://scrapling.requestcatcher.com" content.md \
    --cookies "session=abc123; user=john" --ai-targeted

# Add user agent
scrapling extract get "https://api.site.com" data.json \
    -H "User-Agent: MyBot 1.0" --ai-targeted

# Add multiple headers
scrapling extract get "https://site.com" page.html \
    -H "Accept: text/html" \
    -H "Accept-Language: en-US" --ai-targeted

# With query parameters
scrapling extract get "https://api.example.com" data.json \
    -p "page=1" -p "limit=10" --ai-targeted

GET options:

-H, --headers TEXT              HTTP headers "Key: Value" (multiple allowed)
--cookies TEXT                  Cookies "name1=value1;name2=value2"
--timeout INTEGER               Request timeout in seconds (default: 30)
--proxy TEXT                    Proxy URL from $PROXY_URL env variable
-s, --css-selector TEXT         Extract specific content with CSS selector
-p, --params TEXT               Query parameters "key=value" (multiple)
--follow-redirects / --no-follow-redirects  (default: True)
--verify / --no-verify          SSL verification (default: True)
--impersonate TEXT              Browser to impersonate (chrome, firefox)
--stealthy-headers / --no-stealthy-headers  (default: True)
--ai-targeted                   Extract main content and sanitize for AI

POST Request

# Submit form data
scrapling extract post "https://api.site.com/search" results.html \
    --data "query=python&type=tutorial" --ai-targeted

# Send JSON data
scrapling extract post "https://api.site.com" response.json \
    --json '{"username": "test", "action": "search"}' --ai-targeted

POST options: (same as GET plus)

-d, --data TEXT                 Form data "param1=value1&param2=value2"
-j, --json TEXT                 JSON data as string

PUT Request

# Send data
scrapling extract put "https://api.example.com" results.html \
    --data "update=info" \
    --impersonate "firefox" --ai-targeted

# Send JSON data
scrapling extract put "https://api.example.com" response.json \
    --json '{"username": "test", "action": "search"}' --ai-targeted

DELETE Request

scrapling extract delete "https://api.example.com/resource" response.txt --ai-targeted

# With impersonation
scrapling extract delete "https://api.example.com/" response.txt \
    --impersonate "chrome" --ai-targeted

Browser Fetching Commands

Use browser-based fetching for JavaScript-heavy sites or when HTTP requests fail.

fetch - Handle Dynamic Content

For websites that load content dynamically or have slight protection:

# Wait for JavaScript to load and network activity to finish
scrapling extract fetch "https://example.com" content.md --network-idle --ai-targeted

# Wait for specific element to appear
scrapling extract fetch "https://example.com" data.txt \
    --wait-selector ".content-loaded" --ai-targeted

# Visible browser mode for debugging
scrapling extract fetch "https://example.com" page.html \
    --no-headless --disable-resources --ai-targeted

# Use installed Chrome browser
scrapling extract fetch "https://example.com" content.md --real-chrome --ai-targeted

# With CSS selector extraction
scrapling extract fetch "https://example.com" articles.md \
    --css-selector "article" \
    --network-idle --ai-targeted

fetch options:

--headless / --no-headless      Run browser headless (default: True)
--disable-resources             Drop unnecessary resources for speed boost
--network-idle                  Wait for network idle
--timeout INTEGER               Timeout in milliseconds (default: 30000)
--wait INTEGER                  Additional wait time in ms (default: 0)
-s, --css-selector TEXT         Extract specific content
--wait-selector TEXT            Wait for selector before proceeding
--locale TEXT                   User locale (default: system)
--real-chrome                   Use installed Chrome browser
--proxy TEXT                    Proxy URL
-H, --extra-headers TEXT        Extra headers (multiple)
--ai-targeted                   Extract main content and sanitize for AI

stealthy-fetch - Bypass Protection

For websites with anti-bot protection or Cloudflare:

# Bypass basic protection
scrapling extract stealthy-fetch "https://example.com" content.md --ai-targeted

# Solve Cloudflare challenges
scrapling extract stealthy-fetch "https://nopecha.com/demo/cloudflare" data.txt \
    --solve-cloudflare \
    --css-selector "#padded_content a" --ai-targeted

# Use proxy for anonymity (set PROXY_URL environment variable)
scrapling extract stealthy-fetch "https://site.com" content.md \
    --proxy "$PROXY_URL" --ai-targeted

# Hide canvas fingerprint
scrapling extract stealthy-fetch "https://example.com" content.md \
    --hide-canvas \
    --block-webrtc --ai-targeted

stealthy-fetch options: (same as fetch plus)

--block-webrtc                  Block WebRTC entirely
--solve-cloudflare              Solve Cloudflare challenges
--allow-webgl / --block-webgl   Allow WebGL (default: True)
--hide-canvas                   Add noise to canvas operations
--ai-targeted                   Extract main content and sanitize for AI

CSS Selector Examples

Extract specific content with the -s or --css-selector flag:

# Extract all articles
scrapling extract get "https://blog.example.com" articles.md -s "article" --ai-targeted

# Extract specific class
scrapling extract get "https://example.com" titles.txt -s ".title" --ai-targeted

# Extract by ID
scrapling extract get "https://example.com" content.md -s "#main-content" --ai-targeted

# Extract links (href attributes)
scrapling extract get "https://example.com" links.txt -s "a::attr(href)" --ai-targeted

# Extract text only
scrapling extract get "https://example.com" titles.txt -s "h1::text" --ai-targeted

# Extract multiple elements with fetch
scrapling extract fetch "https://example.com" products.md \
    -s ".product-card" \
    --network-idle --ai-targeted

Help Commands

scrapling --help
scrapling extract --help
scrapling extract get --help
scrapling extract post --help
scrapling extract fetch --help
scrapling extract stealthy-fetch --help

Resources

how to use scrapling

How to use scrapling on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • ›Cursor installed and configured on your development machine
  • ›Node.js version 16.0+ with npm package manager (verify with node --version)
  • ›Active project directory or workspace where you want to add scrapling
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/hyperpuncher/dotagents --skill scrapling

The skills CLI fetches scrapling from GitHub repository hyperpuncher/dotagents and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/scrapling

Reload or restart Cursor to activate scrapling. Access the skill through slash commands (e.g., /scrapling) or your agent's skill management interface.

⚠

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

List & Monetize Your Skill

Submit your Claude Code skill and start earning

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Use Cases▌

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

✓

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

✓

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

✓

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

✓

Save 3-5 hours/week on communication overhead

Implementation Guide▌

Prerequisites

  • ›Claude Desktop or compatible AI client
  • ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • ›Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • ⚠Not validating competitive research—verify facts before sharing
  • ⚠Accepting user stories without involving engineering team
  • ⚠Over-relying on frameworks without qualitative judgment
  • ⚠Not customizing outputs to company culture and communication style
  • ⚠Skipping stakeholder validation of generated requirements

Best Practices▌

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • −Don't publish competitive analysis without fact-checking
  • −Don't finalize user stories without engineering review
  • −Don't make prioritization decisions solely on AI scoring
  • −Don't skip customer validation of generated requirements
  • −Don't ignore company-specific context and culture

💡 Pro Tips

  • ★Provide context: company goals, constraints, customer feedback
  • ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • ★Use skill for 70% generation + 30% customization to company needs

When to Use This▌

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path▌

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.4★★★★★38 reviews
  • ★★★★★Chaitanya Patil· Dec 20, 2024

    Registry listing for scrapling matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Ama Li· Dec 20, 2024

    scrapling is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • ★★★★★Xiao Sethi· Dec 8, 2024

    Solid pick for teams standardizing on skills: scrapling is focused, and the summary matches what you get after install.

  • ★★★★★Ama Singh· Dec 4, 2024

    scrapling reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Min Bansal· Nov 23, 2024

    scrapling has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • ★★★★★Piyush G· Nov 11, 2024

    Keeps context tight: scrapling is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • ★★★★★William Ghosh· Oct 14, 2024

    scrapling fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★Shikha Mishra· Oct 2, 2024

    I recommend scrapling for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Kwame Taylor· Sep 17, 2024

    Registry listing for scrapling matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Maya Reddy· Sep 13, 2024

    Keeps context tight: scrapling is the kind of skill you can hand to a new teammate without a long onboarding doc.

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