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x-reply-context

Fetch and analyze tweet replies to surface engagement signals and content gaps.

Updated Apr 10, 2026
Search Tags:#context#engagement#drafting#analyze#social-media#replies
One-click install
npx skills add https://github.com/RealLoveBeatsHateFeelMe/Katelyn-openclaw --skill x-reply-context
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: x-reply-context
Source: https://github.com/RealLoveBeatsHateFeelMe/Katelyn-openclaw/tree/main/workspace-katelyn/skills/x-reply-context
Command: npx skills add https://github.com/RealLoveBeatsHateFeelMe/Katelyn-openclaw --skill x-reply-context

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill fetches existing replies to a candidate tweet, analyzes engagement signals (likeCount and author interactions), and surfaces angles to guide the generation of more relevant, non-redundant replies.

Core Features & Use Cases

  • Fetch and analyze replies: Retrieve replies via twitterapi.io and rank them by engagement to reveal patterns.
  • Detect author engagement: Find replies from the original author and map their top-level engagement to infer potential angles.
  • Surface white-space angles: Identify unaddressed topics or perspectives to differentiate your draft.
  • Workflow integration: Used by x-reply-briefing before generating reply drafts to improve relevance and originality.

Quick Start

Run x-reply-context on a candidate tweet to fetch its replies, analyze engagement patterns, and surface white-space angles before drafting a reply.

Frequently Asked Questions about x-reply-context

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze public tweet replies to improve my engagement strategy?▼

To analyze public tweet replies for engagement strategy, this Skill fetches existing replies, ranks them by engagement signals like likeCount, and detects original author interactions to surface unaddressed content gaps for your drafts.

What is the best way to find white-space angles before drafting social media replies?▼

Finding white-space angles before drafting social media replies involves analyzing existing tweet engagement patterns to identify unaddressed topics, ensuring your generated response is relevant and non-redundant.

How do I detect original author engagement in tweet reply chains?▼

Detecting original author engagement in tweet reply chains requires fetching top-level replies and mapping the author's interactions to infer their preferred angles and topics for context-aware drafting.

Does this reply context tool work with tweets that have varying reply counts?▼

Yes, this reply context tool works with candidate tweets of varying reply counts, automatically deciding whether to fetch replies, summarize them, or proceed with deeper analysis based on the volume.

What are the limitations when fetching reply context for high-volume tweets?▼

Limitations when fetching reply context for high-volume tweets include rate limiting constraints from twitterapi.io, requiring error handling workflows to manage retrieval failures and ensure structured output.

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