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Exa X Search

核心功能是通过Exa精准搜索Twitter/X平台的推文，分析内容情感、提炼关键主题、识别有影响力的账号及对话趋势。典型场景包括品牌舆情监控、竞品情报收集、产品发布用户反应追踪、热点洞察。需用户提供搜索主题、需求目的、时间范围；输出为结构化推文列表及含整体情感、核心主题等的综合分析报告，仅针对Twitter/X，不支持通用网页搜索。

mariokarras
mariokarras
热度 57.34
最近更新 2026-05-17 15:18:12
分类：搜索依赖：代码执行应用场景：社交媒体 / 研究 / 市场分析

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add abm-exa-x-search
Metadata
name
exa-x-search
description
Search Twitter/X for tweets, discussions, and sentiment on topics, people, or brands using Exa's tweet category search. Use when the user mentions 'search Twitter,' 'find tweets,' 'X search,' 'what are people saying on X,' 'Twitter mentions,' 'tweets about,' or 'Twitter sentiment.' Covers tweet discovery, sentiment analysis, and trend spotting. NOT for general web search. For broader social listening with synthesis, see social-listening. For general web research, see exa-company-research.
metadata
{"version":"1.0.0"}

Exa X Search

You help users search Twitter/X for relevant tweets and discussions using Exa's tweet category search. Your goal is to find what people are saying, identify sentiment patterns, and surface notable voices on any topic.

Before Starting

Check for product marketing context first: If .agents/product-marketing-context.md exists (or .claude/product-marketing-context.md in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.

Understand what the user needs (ask if not provided):

  1. Search topic -- brand, product, person, or topic to search for
  2. Purpose -- sentiment monitoring, competitive intel, trend spotting, or general research
  3. Time focus -- recent tweets only, or all-time?

Workflow

Step 1: Search for Tweets

Run via exec:

node tools/clis/exa.js search --query "[topic or brand]" --category "tweet" --num-results 20

For recent tweets only:

node tools/clis/exa.js search --query "[topic]" --category "tweet" --num-results 20 --start-date [current-year]-01-01  # Use current year

Step 2: Fetch Tweet Content

For the most relevant results, fetch full content:

node tools/clis/exa.js contents --ids "[id1],[id2]" --text

Use the IDs returned from the search results.

Step 3: Analyze Patterns

Review the tweets for:

  • Sentiment -- positive, negative, neutral, mixed
  • Key voices -- who is talking about this and do they have influence?
  • Trending themes -- what subtopics or angles keep coming up?
  • Volume signals -- is this a growing or declining conversation?

Dry Run

To preview the request without making an API call:

node tools/clis/exa.js search --query "[topic]" --category "tweet" --dry-run

Output Format

Individual Tweets

For notable tweets:

  • Author: [handle/name]
  • Content: [tweet text or summary]
  • Sentiment: Positive / Negative / Neutral / Mixed
  • Date: [when posted]

Synthesis

After listing tweets, provide:

  • Overall Sentiment: [summary of sentiment distribution]
  • Key Themes: [3-5 recurring topics or angles]
  • Notable Voices: [influential accounts discussing this topic]
  • Conversation Trend: [growing, stable, or declining interest]
  • Actionable Insights: [what the user can do with this information]

Tips

  • Brand monitoring: Search for both the brand name and common misspellings or abbreviations
  • Competitor intel: Compare tweet sentiment between your brand and competitors
  • Product launches: Search around launch dates to capture initial reactions
  • Hashtags: Include relevant hashtags in the query for more targeted results
  • Negative sentiment: Pay special attention to complaints -- they reveal product gaps

Related Skills

  • social-listening: Broader social listening with cross-platform synthesis
  • social-content: Create social media content based on trends
  • exa-company-research: Research companies beyond social mentions