Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
Trim instructions: remove redundant Skills Overview, simplify setup wording
What This Skill Does
Analyzes Twitter/X accounts, tweets, trends, and communities using the readx API. Calculates engagement rates, virality ratios, controversy scores, and follower quality metrics rather than returning raw numbers. Works via MCP tools or direct curl API calls when MCP is unavailable.
Automatically computes derived metrics like virality ratio, controversy score, and follower quality instead of requiring manual calculations on raw API data.
When to use it
- Benchmarking a competitor account's tweet engagement over recent weeks
- Finding viral posts on a topic filtered by language or minimum likes
- Comparing follower quality and authority ratio between two accounts
- Detecting backlash on a tweet by checking reply-to-like ratios
- Monitoring a Twitter community for emerging discussions around a keyword
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Analyze @naval's recent tweets and tell me which performed best
- 1Resolve @naval to a user_id via get_user_by_username
- 2Fetch tweet timeline and paginate using next_cursor for broader coverage
- 3Calculate engagement rate, virality ratio, and controversy ratio per tweet
- 4Sort tweets by total engagement and identify the top 3-5 performers
- 5Present a ranked markdown table and generate 3-5 actionable Key Takeaways
Markdown table of top tweets ranked by engagement with computed metrics and a Key Takeaways section highlighting content patterns
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.