article-extractor

This skill extracts the main content from web articles and blog posts, removing navigation, ads, newsletter signups, and other clutter. Saves clean, readable text.

Works with

Claude CodeCursorClineWindsurfCodexGooseGitHub CopilotZed

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Install Skill

Run in your terminal

$npx skills add https://github.com/michalparkola/tapestry-skills-for-claude-code --skill article-extractor

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Installation Guide

How to use article-extractor 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 machine
  • Node.js 16+ with npm — verify with node --version
  • Active project directory where you want to add article-extractor
2

Run the install command

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

$npx skills add https://github.com/michalparkola/tapestry-skills-for-claude-code --skill article-extractor

Fetches article-extractor from michalparkola/tapestry-skills-for-claude-code and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/article-extractor

Restart Cursor to activate article-extractor. Access via /article-extractor in your agent's command palette.

Security 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 environment. Always review source, verify the publisher, and test in isolation before production.

Documentation

Article Extractor

This skill extracts the main content from web articles and blog posts, removing navigation, ads, newsletter signups, and other clutter. Saves clean, readable text.

When to Use This Skill

Activate when the user:

  • Provides an article/blog URL and wants the text content
  • Asks to "download this article"
  • Wants to "extract the content from [URL]"
  • Asks to "save this blog post as text"
  • Needs clean article text without distractions

How It Works

Priority Order:

  1. Check if tools are installed (reader or trafilatura)
  2. Download and extract article using best available tool
  3. Clean up the content (remove extra whitespace, format properly)
  4. Save to file with article title as filename
  5. Confirm location and show preview

Installation Check

Check for article extraction tools in this order:

Option 1: reader (Recommended - Mozilla's Readability)

command -v reader

If not installed:

npm install -g @mozilla/readability-cli
# or
npm install -g reader-cli

Option 2: trafilatura (Python-based, very good)

command -v trafilatura

If not installed:

pip3 install trafilatura

Option 3: Fallback (curl + simple parsing)

If no tools available, use basic curl + text extraction (less reliable but works)

Extraction Methods

Method 1: Using reader (Best for most articles)

# Extract article
reader "URL" > article.txt

Pros:

  • Based on Mozilla's Readability algorithm
  • Excellent at removing clutter
  • Preserves article structure

Method 2: Using trafilatura (Best for blogs/news)

# Extract article
trafilatura --URL "URL" --output-format txt > article.txt

# Or with more options
trafilatura --URL "URL" --output-format txt --no-comments --no-tables > article.txt

Pros:

  • Very accurate extraction
  • Good with various site structures
  • Handles multiple languages

Options:

  • --no-comments: Skip comment sections
  • --no-tables: Skip data tables
  • --precision: Favor precision over recall
  • --recall: Extract more content (may include some noise)

Method 3: Fallback (curl + basic parsing)

# Download and extract basic content
curl -s "URL" | python3 -c "
from html.parser import HTMLParser
import sys

class ArticleExtractor(HTMLParser):
    def __init__(self):
        super().__init__()
        self.in_content = False
        self.content = []
        self.skip_tags = {'script', 'style', 'nav', 'header', 'footer', 'aside'}
        self.current_tag = None

    def handle_starttag(self, tag, attrs):
        if tag not in self.skip_tags:
            if tag in {'p', 'article', 'main', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6'}:
                self.in_content = True
        self.current_tag = tag

    def handle_data(self, data):
        if self.in_content and data.strip():
            self.content.append(data.strip())

    def get_content(self):
        return '\n\n'.join(self.content)

parser = ArticleExtractor()
parser.feed(sys.stdin.read())
print(parser.get_content())
" > article.txt

Note: This is less reliable but works without dependencies.

Getting Article Title

Extract title for filename:

Using reader:

# reader outputs markdown with title at top
TITLE=$(reader "URL" | head -n 1 | sed 's/^# //')

Using trafilatura:

# Get metadata including title
TITLE=$(trafilatura --URL "URL" --json | python3 -c "import json, sys; print(json.load(sys.stdin)['title'])")

Using curl (fallback):

TITLE=$(curl -s "URL" | grep -oP '<title>\K[^<]+' | sed 's/ - .*//' | sed 's/ | .*//')

Filename Creation

Clean title for filesystem:

# Get title
TITLE="Article Title from Website"

# Clean for filesystem (remove special chars, limit length)
FILENAME=$(echo "$TITLE" | tr '/' '-' | tr ':' '-' | tr '?' '' | tr '"' '' | tr '<' '' | tr '>' '' | tr '|' '-' | cut -c 1-100 | sed 's/ *$//')

# Add extension
FILENAME="${FILENAME}.txt"

Complete Workflow

ARTICLE_URL="https://example.com/article"

# Check for tools
if command -v reader &> /dev/null; then
    TOOL="reader"
    echo "Using reader (Mozilla Readability)"
elif command -v trafilatura &> /dev/null; then
    TOOL="trafilatura"
    echo "Using trafilatura"
else
    TOOL="fallback"
    echo "Using fallback method (may be less accurate)"
fi

# Extract article
case $TOOL in
    reader)
        # Get content
        reader "$ARTICLE_URL" > temp_article.txt

        # Get title (first line after # in markdown)
        TITLE=$(head -n 1 temp_article.txt | sed 's/^# //')
        ;;

    trafilatura)
        # Get title from metadata
        METADATA=$(trafilatura --URL "$ARTICLE_URL" --json)
        TITLE=$(echo "$METADATA" | python3 -c "import json, sys; print(json.load(sys.stdin).get('title', 'Article'))")

        # Get clean content
        trafilatura --URL "$ARTICLE_URL" --output-format txt --no-comments > temp_article.txt
        ;;

    fallback)
        # Get title
        TITLE=$(curl -s "$ARTICLE_URL" | grep -oP '<title>\K[^<]+' | head -n 1)
        TITLE=${TITLE%% - *}  # Remove site name
        TITLE=${TITLE%% | *}  # Remove site name (alternate)

        # Get content (basic extraction)
        curl -s "$ARTICLE_URL" | python3 -c "
from html.parser import HTMLParser
import sys

class ArticleExtractor(HTMLParser):
    def __init__(self):
        super().__init__()
        self.in_content = False
        self.content = []
        self.skip_tags = {'script', 'style', 'nav', 'header', 'footer', 'aside', 'form'}

    def handle_starttag(self, tag, attrs):
        if tag not in self.skip_tags:
            if tag in {'p', 'article', 'main'}:
                self.in_content = True
        if tag in {'h1', 'h2', 'h3'}:
            self.content.append('\n')

    def handle_data(self, data):
        if self.in_content and data.strip():
            self.content.append(data.strip())

    def get_content(self):
        return '\n\n'.join(self.content)

parser = ArticleExtractor()
parser.feed(sys.stdin.read())
print(parser.get_content())
" > temp_article.txt
        ;;
esac

# Clean filename
FILENAME=

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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

Steps

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

Related Skills

Reviews

4.554 reviews
  • S
    Soo SethiDec 28, 2024

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

  • C
    Chaitanya PatilDec 20, 2024

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

  • N
    Naina AndersonDec 20, 2024

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

  • I
    Ira LopezDec 12, 2024

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

  • A
    Ava SrinivasanDec 4, 2024

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

  • M
    Mia HuangDec 4, 2024

    Useful defaults in article-extractor — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • M
    Meera ChoiNov 27, 2024

    Useful defaults in article-extractor — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • S
    Soo DialloNov 23, 2024

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

  • S
    Sakura HarrisNov 19, 2024

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

  • P
    Piyush GNov 11, 2024

    We added article-extractor from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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