Adhx

ADHX - X/Twitter Post Reader

Fetch any X/Twitter post as structured JSON for analysis using the ADHX API.

Overview

ADHX provides a free API that returns clean JSON for any X post, including full long-form article content. This is far superior to scraping or browser-based approaches for LLM consumption. Works with regular tweets and full X Articles.

When to Use This Skill

• Use when a user shares an X/Twitter link and wants to read, analyze, or summarize the post
• Use when you need structured data from an X/Twitter post (author, engagement, content)
• Use when working with long-form X Articles that need full content extraction

API Endpoint

https://adhx.com/api/share/tweet/{username}/{statusId}

URL Patterns

Extract username and statusId from any of these URL formats:

Workflow

When a user shares an X/Twitter link:

1. Parse the URL to extract username and statusId from the path segments
2. Fetch the JSON using curl:

curl -s "https://adhx.com/api/share/tweet/{username}/{statusId}"

3. Use the structured response to answer the user's question (summarize, analyze, extract key points, etc.)

Response Schema

{
  "id": "statusId",
  "url": "original x.com URL",
  "text": "short-form tweet text (empty if article post)",
  "author": {
    "name": "Display Name",
    "username": "handle",
    "avatarUrl": "profile image URL"
  },
  "createdAt": "timestamp",
  "engagement": {
    "replies": 0,
    "retweets": 0,
    "likes": 0,
    "views": 0
  },
  "article": {
    "title": "Article title (for long-form posts)",
    "previewText": "First ~200 chars",
    "coverImageUrl": "hero image URL",
    "content": "Full markdown content with images"
  }
}

Installation

Option A: Claude Code plugin marketplace (recommended)

/plugin marketplace add itsmemeworks/adhx

Option B: Manual install

curl -sL https://raw.githubusercontent.com/itsmemeworks/adhx/main/skills/adhx/SKILL.md -o ~/.claude/skills/adhx/SKILL.md

Examples

Example 1: Summarize a tweet

User: "Summarize this post https://x.com/dgt10011/status/2020167690560647464"

curl -s "https://adhx.com/api/share/tweet/dgt10011/2020167690560647464"

Then use the returned JSON to provide the summary.

Example 2: Analyze engagement

User: "How many likes did this tweet get? https://x.com/handle/status/123"

1. Parse URL: username = handle, statusId = 123
2. Fetch: curl -s "https://adhx.com/api/share/tweet/handle/123"
3. Return the engagement.likes value from the response

Best Practices

• Always parse the full URL to extract username and statusId before calling the API
• Check for the article field when the user wants full content (not just tweet text)
• Use the engagement field when users ask about likes, retweets, or views
• Don't attempt to scrape x.com directly - use this API instead

Notes

• No authentication required
• Works with both short tweets and long-form X articles
• Always prefer this over browser-based scraping for X content
• If the API returns an error or empty response, inform the user the post may not be available

Additional Resources

• ADHX GitHub Repository
• ADHX Website

🧠 AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \\
  --content "Description of what was decided/solved" \\
  --type decision \\
  --tags adhx <relevant-tags>

Agent Team Collaboration

• This skill can be invoked by the orchestrator agent via intelligent routing.
• In Agent Teams mode, results are shared via Qdrant shared memory for cross-agent context.
• In Subagent mode, this skill runs in isolation with its own memory namespace.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns