twitter-reader

daymade/claude-code-skills · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/daymade/claude-code-skills --skill twitter-reader
0 commentsdiscussion
summary

Fetch Twitter/X post content by URL without JavaScript or authentication.

  • Retrieves full post metadata including author, timestamp, text content, images, and thread replies via Jina.ai API
  • Supports both individual tweet fetching and batch operations across x.com and twitter.com URLs
  • Includes bundled Python and Bash scripts for single and multiple tweet retrieval
  • Requires a free Jina API key set as an environment variable to function
skill.md

Twitter Reader

Fetch Twitter/X post and article content with full media support.

Quick Start (Recommended)

For X Articles with images, use the new fetch_article.py script:

uv run --with pyyaml python scripts/fetch_article.py <article_url> [output_dir]

Example:

uv run --with pyyaml python scripts/fetch_article.py \
  https://x.com/HiTw93/status/2040047268221608281 \
  ./Clippings

This will:

  • Fetch structured data via twitter-cli (likes, retweets, bookmarks)
  • Fetch content with images via jina.ai API
  • Download all images to attachments/YYYY-MM-DD-AUTHOR-TITLE/
  • Generate complete Markdown with embedded image references
  • Include YAML frontmatter with metadata

Example Output

Fetching: https://x.com/HiTw93/status/2040047268221608281
--------------------------------------------------
Getting metadata...
Title: 你不知道的大模型训练:原理、路径与新实践
Author: Tw93
Likes: 1648

Getting content and images...
Images: 15

Downloading 15 images...
  ✓ 01-image.jpg
  ✓ 02-image.jpg
  ...

✓ Saved: ./Clippings/2026-04-03-文章标题.md
✓ Images: ./Clippings/attachments/2026-04-03-HiTw93-.../ (15 downloaded)

Alternative: Jina API (Text-only)

For simple text-only fetching without authentication:

# Single tweet
curl "https://r.jina.ai/https://x.com/USER/status/TWEET_ID" \
  -H "Authorization: Bearer ${JINA_API_KEY}"

# Batch fetching
scripts/fetch_tweets.sh url1 url2 url3

Features

Full Article Mode (fetch_article.py)

  • ✅ Structured metadata (author, date, engagement metrics)
  • ✅ Automatic image download (all embedded media)
  • ✅ Complete Markdown with local image references
  • ✅ YAML frontmatter for PKM systems
  • ✅ Handles X Articles (long-form content)

Simple Mode (Jina API)

  • Text-only content
  • No authentication required beyond Jina API key
  • Good for quick text extraction

Prerequisites

For Full Article Mode

  • uv (Python package manager)
  • No additional setup (twitter-cli auto-installed)

For Simple Mode (Jina)

export JINA_API_KEY="your_api_key_here"
# Get from https://jina.ai/

Output Structure

output_dir/
├── YYYY-MM-DD-article-title.md       # Main Markdown file
└── attachments/
    └── YYYY-MM-DD-author-title/
        ├── 01-image.jpg
        ├── 02-image.jpg
        └── ...

What Gets Returned

Full Article Mode

  • YAML Frontmatter: source, author, date, likes, retweets, bookmarks
  • Markdown Content: Full article text with local image references
  • Attachments: All downloaded images in dedicated folder

Simple Mode

  • Title: Post author and content preview
  • URL Source: Original tweet link
  • Published Time: GMT timestamp
  • Markdown Content: Text with remote media URLs

URL Formats Supported

  • https://x.com/USER/status/ID (posts)
  • https://x.com/USER/article/ID (long-form articles)
  • https://twitter.com/USER/status/ID (legacy)

Scripts

fetch_article.py

Full-featured article fetcher with image download:

uv run --with pyyaml python scripts/fetch_article.py <url> [output_dir]

fetch_tweet.py

Simple text-only fetcher using Jina API:

python scripts/fetch_tweet.py <tweet_url> [output_file]

fetch_tweets.sh

Batch fetch multiple tweets (Jina API):

scripts/fetch_tweets.sh <url1> <url2> ...

Migration from Jina API

Old workflow:

curl "https://r.jina.ai/https://x.com/..."
# Manual image extraction and download

New workflow:

uv run --with pyyaml python scripts/fetch_article.py <url>
# Automatic image download, complete Markdown
how to use twitter-reader

How to use twitter-reader 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 twitter-reader
2

Execute installation command

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

$npx skills add https://github.com/daymade/claude-code-skills --skill twitter-reader

The skills CLI fetches twitter-reader from GitHub repository daymade/claude-code-skills 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/twitter-reader

Reload or restart Cursor to activate twitter-reader. Access the skill through slash commands (e.g., /twitter-reader) 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.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

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)
  • No comments yet — start the thread.
general reviews

Ratings

4.843 reviews
  • Sofia Brown· Dec 24, 2024

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

  • Amelia Thomas· Dec 16, 2024

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

  • Harper Jain· Dec 16, 2024

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

  • Isabella Shah· Dec 8, 2024

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

  • Arjun Menon· Nov 27, 2024

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

  • Sofia Chen· Nov 15, 2024

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

  • Aanya Mehta· Nov 7, 2024

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

  • Advait Lopez· Oct 26, 2024

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

  • Naina Chen· Oct 18, 2024

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

  • Piyush G· Sep 21, 2024

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

showing 1-10 of 43

1 / 5