xclaw_skill

Overview

This skill is the XClaw Intelligence module that delivers real-time social signals and creator analytics powered by the CryptoHunt engine. It surfaces trending tweets, KOL (key opinion leader) analysis, live user crawling, and content ideation tailored by region and topic. The skill is built to help creators identify viral opportunities and produce high-conversion tweet drafts quickly.

How this skill works

XClaw queries CryptoHunt endpoints to fetch top-performing tweets for selectable time windows and regions, with tag-based filtering for focused discovery. It maintains a KOL database for fast insights and uses live crawling as a fallback to gather fresh data for new or uncatalogued users. Individual tweet lookups return full content, thread structure, and engagement metrics to support deep dives and quotation-ready drafting.

When to use it

• Identify trending topics and top tweets in the last 1, 4, or 24 hours for a region or tag.
• Perform a performance and behavior analysis of a specific influencer or KOL.
• Retrieve full tweet content and thread metrics before quoting or replying natively on X.
• Generate ready-to-post tweet drafts tailored by region, time window, and tag.
• Archive or back up historical versions of skill outputs for reporting and audit.

Best practices

• Provide a clear time window (e.g., 1, 4, 24 hours) and region when fetching hot trends to reduce noise.
• Use tag filters (AI, meme, ethereum, etc.) to focus discovery on relevant communities.
• When analyzing a user, include usernames and allow live crawl fallback to ensure up-to-date results.
• Verify engagement metrics for high-value posts before amplification to avoid promoting bots or spam.
• Store returned original tweet links to enable native quote/reply workflows and attribution.

Example use cases

• A creator scans the last 4 hours of Chinese crypto tweets to quickly repurpose a viral hook into three different tweet drafts.
• A social strategist analyzes a KOL’s recent output to build a collaboration pitch with evidence-backed engagement metrics.
• A content team fetches full thread data and metrics for a candidate tweet before composing a native quote reply.
• A community manager monitors global AI-related trends across 24 hours to plan breakout content for the next day.