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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionarticle-extractorExecute the skills CLI command in your project's root directory to begin installation:
Fetches article-extractor from michalparkola/tapestry-skills-for-claude-code and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate article-extractor. Access via /article-extractor in your agent's command palette.
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.
Submit your Claude Code skill and start earning
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
0
total installs
0
this week
327
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
327
stars
This skill extracts the main content from web articles and blog posts, removing navigation, ads, newsletter signups, and other clutter. Saves clean, readable text.
Activate when the user:
Check for article extraction tools in this order:
command -v reader
If not installed:
npm install -g @mozilla/readability-cli
# or
npm install -g reader-cli
command -v trafilatura
If not installed:
pip3 install trafilatura
If no tools available, use basic curl + text extraction (less reliable but works)
# Extract article
reader "URL" > article.txt
Pros:
# 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:
Options:
--no-comments: Skip comment sections--no-tables: Skip data tables--precision: Favor precision over recall--recall: Extract more content (may include some noise)# 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.
Extract title for filename:
# reader outputs markdown with title at top
TITLE=$(reader "URL" | head -n 1 | sed 's/^# //')
# Get metadata including title
TITLE=$(trafilatura --URL "URL" --json | python3 -c "import json, sys; print(json.load(sys.stdin)['title'])")
TITLE=$(curl -s "URL" | grep -oP '<title>\K[^<]+' | sed 's/ - .*//' | sed 's/ | .*//')
Clean title for filesystem:
# Get title
TITLE="Article Title from Website"
# Clean for filesystem (remove special chars, limit length)
FILENAME=$(echo "$TITLE" | tr '/' '-' | tr ':' '-' | tr '?' '' | tr '"' '' | tr '<' '' | tr '>' '' | tr '|' '-' | cut -c 1-100 | sed 's/ *$//')
# Add extension
FILENAME="${FILENAME}.txt"
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=Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
article-extractor fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: article-extractor is focused, and the summary matches what you get after install.
I recommend article-extractor for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: article-extractor is focused, and the summary matches what you get after install.
article-extractor has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in article-extractor — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Useful defaults in article-extractor — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
article-extractor fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
article-extractor has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added article-extractor from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
showing 1-10 of 54