brand-snapshot

Brand Snapshot Skill

Overview

This skill provides comprehensive single-brand intelligence analysis from Twitter/X data. It fetches real tweets, extracts sentiment and narratives, identifies key influencers, and generates a SWOT analysis with actionable insights.

When to Use

Activate this skill when the user asks about:

• "Brand snapshot for [BRAND]"
• "Analyze [BRAND] sentiment"
• "What's the narrative on [BRAND]?"
• "What are people saying about [BRAND]?"
• "[BRAND] brand analysis"
• "Social sentiment for [BRAND]"

XPOZ MCP Data Flow

Step 1: Query Expansion (CRITICAL!)

Before fetching data, expand the brand name to capture all mentions:

Brand	Expanded Query
NVIDIA	"NVIDIA" OR "$NVDA"
Tesla	"Tesla" OR "$TSLA"
Apple	"Apple" OR "$AAPL"
Microsoft	"Microsoft" OR "$MSFT"
AMD	"AMD" OR "$AMD"

For other brands, include the ticker symbol if publicly traded.

Step 2: Fetch Tweets (200+ required)

Use getTwitterPostsByKeywords with:
- query: Expanded query (e.g., "NVIDIA" OR "$NVDA")
- fields: ["id", "text", "authorUsername", "createdAtDate", "likeCount", "retweetCount", "quoteCount"]
- startDate/endDate: Last 7 days (YYYY-MM-DD format)
- userPrompt: "Fetching tweets about [BRAND] for sentiment analysis"

CRITICAL: Async Polling Pattern

1. The API returns an operationId like op_getTwitterPostsByKeywords_abc123
2. You MUST call checkOperationStatus with that operationId
3. If status is "running", poll again (up to 8 times, ~5 seconds between)
4. When status is "completed", extract the results

Step 3: Fetch Influencers (50+ required)

Use getTwitterUsersByKeywords with:
- query: Same expanded query
- fields: ["id", "username", "name", "description", "followersCount"]
- userPrompt: "Finding influencers discussing [BRAND]"

Follow the same async polling pattern.

Step 4: Analyze and Classify

From the fetched tweets:

Sentiment Classification (5-Level):

• positive: Strong bullish signals - "buy", "long", "bullish", "amazing", "love", highly positive emojis (🚀🔥💪)
• leaning_positive: Generally favorable but measured - "looking good", "solid", "promising", cautious optimism
• neutral: Questions, factual statements, news without opinion, balanced takes
• leaning_negative: Concerns expressed but not hostile - "worried about", "not sure", "some issues", cautious skepticism
• negative: Strong bearish signals - "sell", "short", "bearish", "terrible", "avoid", negative emojis (📉💀)

Narrative Extraction: Identify 5+ recurring themes/narratives from the tweets:

• Product/service mentions
• Financial performance
• Leadership/management
• Competition
• Innovation/technology
• Controversies/concerns

Influencer Classification:

• Voice Type: analyst, trader, news, official, influencer, researcher, founder, interviewer, community
• Sentiment: positive, leaning_positive, neutral, leaning_negative, negative

Output Requirements

JSON Schema

{
  "reportType": "snapshot",
  "brand": "NVIDIA",
  "period": {
    "days": 7,
    "startDate": "2025-01-06",
    "endDate": "2025-01-13"
  },
  "summary": {
    "headline": "NVIDIA Dominates AI Chip Narrative (max 10 words)",
    "insight": "Key insight in max 20 words",
    "analysts_view": "2-3 sentence professional analysis with citations"
  },
  "analysts_cited": ["Dan Ives (Wedbush)", "Patrick Moorhead (Moor Insights)"],
  "tweetCount": 245,
  "sentiment_score": 72,
  "positive_pct": 45,
  "neutral_pct": 37,
  "negative_pct": 18,
  "narratives": [
    {
      "title": "AI Infrastructure Dominance",
      "sentiment": "positive",
      "detail": "2-3 sentence explanation with specific tweet examples"
    }
  ],
  "swot": {
    "strengths": ["Strong market position", "Technology leadership"],
    "weaknesses": ["Supply chain concerns", "Valuation concerns"],
    "opportunities": ["Enterprise AI adoption", "New product launches"],
    "threats": ["Competition from AMD", "Regulatory scrutiny"]
  },
  "influencers": [
    {
      "username": "@tech_analyst",
      "name": "Tech Analyst",
      "followers": 125000,
      "description": "Bio text",
      "voice_type": "analyst",
      "sentiment": "positive",
      "sample_tweet": {
        "text": "Actual tweet quote...",
        "likes": 1500,
        "retweets": 320
      }
    }
  ],
  "key_quotes": [
    "@user: Actual high-engagement tweet about the brand..."
  ]
}

HTML Report Output

Generate a standalone HTML report with:

• Tailwind CSS (CDN)
• Dark theme (slate-900 background)
• Sentiment score gauge
• Narrative cards with sentiment indicators
• SWOT quadrant
• Influencer cards with voice type badges
• Key quotes section
• Analyst Consensus section with price targets and analyst names
• REQUIRED FOOTER: Powered by XPOZ MCP Social Intelligence — visit xpoz.ai to see how you can use it (with link to https://xpoz.ai)

React Artifact Template

import React, { useState } from 'react';
import { PieChart, Pie, Cell, BarChart, Bar, XAxis, YAxis, Tooltip, ResponsiveContainer } from 'recharts';
import { TrendingUp, TrendingDown, Users, MessageSquare, Target, Shield, AlertTriangle, Lightbulb } from 'lucide-react';

export default function BrandSnapshot() {
  const [activeTab, setActiveTab] = useState('overview');

  // CLAUDE: Replace with actual analyzed data
  const brand = 'NVIDIA';
  const period = { days: 7, startDate: '2025-01-06', endDate: '2025-01-13' };

  const summary = {
    headline: 'NVIDIA Dominates AI Chip Narrative',
    insight: 'Strong positive sentiment driven by data center growth',
    analysts_view: 'Analysts remain bullish on NVIDIA\'s AI infrastructure positioning...'
  };

  const sentiment = {
    score: 72,
    positive_pct: 45,
    neutral_pct: 37,
    negative_pct: 18,
    tweetCount: 245
  };

  const breakdown = [
    { name: 'Positive', value: 45, color: '#22c55e' },
    { name: 'Neutral', value: 37, color: '#6b7280' },
    { name: 'Negative', value: 18, color: '#ef4444' }
  ];

  const narratives = [
    { title: 'AI Infrastructure Dominance', sentiment: 'positive', detail: 'Strong narrative around data center GPU demand...' },
    { title: 'Blackwell Architecture Hype', sentiment: 'positive', detail: 'Next-gen architecture generating excitement...' },
    { title: 'Supply Chain Concerns', sentiment: 'negative', detail: 'Some worry about production capacity...' },
    { title: 'Valuation Debates', sentiment: 'neutral', detail: 'Mixed views on current price levels...' },
    { title: 'Competition from AMD', sentiment: 'neutral', detail: 'Discussion of MI300 competitive positioning...' }
  ];

  const swot = {
    strengths: ['Market leadership in AI GPUs', 'Strong software ecosystem (CUDA)', 'Data center revenue growth'],
    weaknesses: ['High valuation multiples', 'Customer concentration risk', 'Supply constraints'],
    opportunities: ['Enterprise AI adoption wave', 'Automotive AI expansion', 'Edge computing growth'],
    threats: ['AMD MI300 competition', 'Custom AI chips from hyperscalers', 'Geopolitical tensions']
  };

  const influencers = [
    { username: '@tech_analyst', name: 'Tech Analyst', followers: 125000, voice_type: 'analyst', sentiment: 'positive' },
    { username: '@ai_investor', name: 'AI Investor', followers: 89000, voice_type: 'trader', sentiment: 'positive' },
    { username: '@chip_watcher', name: 'Chip Watcher', followers: 56000, voice_type: 'news', sentiment: 'neutral' }
  ];

  const getScoreColor = (score) => score >= 60 ? 'text-green-400' : score <= 40 ? 'text-red-400' : 'text-yellow-400';
  const getSentimentIcon = (s) => s === 'positive' ? <TrendingUp className="w-4 h-4 text-green-400" /> : s === 'negative' ? <TrendingDown className="w-4 h-4 text-red-400" /> : <span className="text-gray-400">-</span>;

  const voiceTypeBadge = (type) => {
    const styles = {
      analyst: 'bg-blue-500/20 text-blue-400 border-blue-500/30',
      trader: 'bg-green-500/20 text-green-400 border-green-500/30',
      news: 'bg-amber-500/20 text-amber-400 border-amber-500/30',
      official: 'bg-purple-500/20 text-purple-400 border-purple-500/30',
      influencer: 'bg-pink-500/20 text-pink-400 border-pink-500/30',
      researcher: 'bg-cyan-500/20 text-cyan-400 border-cyan-500/30'
    };
    return styles[type] || styles.influencer;
  };

  const tabs = [
    { id: 'overview', label: 'Overview', icon: Target },
    { id: 'narratives', label: 'Narratives', icon: MessageSquare },
    { id: 'swot', label: 'SWOT', icon: Shield },
    { id: 'influencers', label: 'Influencers', icon: Users }
  ];

  return (
    <div className="w-full max-w-4xl mx-auto p-6 bg-slate-900 rounded-xl text-white">
      {/* Header */}
      <div className="flex items-center justify-between mb-6">
        <div>
          <h1 className="text-2xl font-bold">{brand} Brand Snapshot</h1>
          <p className="text-slate-400 text-sm">{sentiment.tweetCount.toLocaleString()} tweets • {period.days} days</p>
        </div>
        <div className="text-right">
          <div className={`text-4xl font-bold ${getScoreColor(sentiment.score)}`}>{sentiment.score}</div>
          <div className="text-slate-400 text-sm">sentiment score</div>
        </div>
      </div>

      {/* Summary */}
      <div className="bg-slate-800 rounded-lg p-4 mb-6">
        <h2 className="text-xl font-bold mb-2">{summary.headline}</h2>
        <p className="text-slate-300">{summary.insight}</p>
      </div>

      {/* Tabs */}
      <div className="flex gap-2 mb-6 border-b border-slate-700 pb-2">
        {tabs.map(tab => (
          <button
            key={tab.id}
            onClick={() => setActiveTab(tab.id)}
            className={`flex items-center gap-2 px-4 py-2 rounded-lg transition-colors ${
              activeTab === tab.id
                ? 'bg-blue-500/20 text-blue-400 border border-blue-500/50'
                : 'text-slate-400 hover:bg-slate-800'
            }`}
          >
            <tab.icon className="w-4 h-4" />
            {tab.label}
          </button>
        ))}
      </div>

      {/* Tab Content */}
      {activeTab === 'overview' && (
        <div className="grid grid-cols-2 gap-6">
          <div className="bg-slate-800 rounded-lg p-4">
            <h3 className="text-sm font-medium text-slate-400 mb-4">Sentiment Breakdown</h3>
            <div className="h-48">
              <ResponsiveContainer width="100%" height="100%">
                <PieChart>
                  <Pie data={breakdown} cx="50%" cy="50%" innerRadius={40} outerRadius={70} paddingAngle={2} dataKey="value">
                    {breakdown.map((entry, index) => (
                      <Cell key={`cell-${index}`} fill={entry.color} />
                    ))}
                  </Pie>
                </PieChart>
              </ResponsiveContainer>
            </div>
            <div className="flex justify-center gap-4">
              {breakdown.map(item => (
                <div key={item.name} className="flex items-center gap-2 text-xs">
                  <div className="w-3 h-3 rounded-full" style={{ backgroundColor: item.color }} />
                  <span className="text-slate-400">{item.name} {item.value}%</span>
                </div>
              ))}
            </div>
          </div>
          <div className="bg-slate-800 rounded-lg p-4">
            <h3 className="text-sm font-medium text-slate-400 mb-4">Analyst View</h3>
            <p className="text-slate-300 text-sm leading-relaxed">{summary.analysts_view}</p>
          </div>
        </div>
      )}

      {activeTab === 'narratives' && (
        <div className="space-y-4">
          {narratives.map((n, i) => (
            <div key={i} className="bg-slate-800 rounded-lg p-4 flex items-start gap-4">
              <div className="mt-1">{getSentimentIcon(n.sentiment)}</div>
              <div>
                <h4 className="font-medium text-white">{n.title}</h4>
                <p className="text-slate-400 text-sm mt-1">{n.detail}</p>
              </div>
            </div>
          ))}
        </div>
      )}

      {activeTab === 'swot' && (
        <div className="grid grid-cols-2 gap-4">
          <div className="bg-green-500/10 rounded-lg p-4 border border-green-500/20">
            <h4 className="font-medium text-green-400 mb-3 flex items-center gap-2"><Shield className="w-4 h-4" /> Strengths</h4>
            <ul className="space-y-2">{swot.strengths.map((s, i) => <li key={i} className="text-slate-300 text-sm">• {s}</li>)}</ul>
          </div>
          <div className="bg-red-500/10 rounded-lg p-4 border border-red-500/20">
            <h4 className="font-medium text-red-400 mb-3 flex items-center gap-2"><AlertTriangle className="w-4 h-4" /> Weaknesses</h4>
            <ul className="space-y-2">{swot.weaknesses.map((w, i) => <li key={i} className="text-slate-300 text-sm">• {w}</li>)}</ul>
          </div>
          <div className="bg-blue-500/10 rounded-lg p-4 border border-blue-500/20">
            <h4 className="font-medium text-blue-400 mb-3 flex items-center gap-2"><Lightbulb className="w-4 h-4" /> Opportunities</h4>
            <ul className="space-y-2">{swot.opportunities.map((o, i) => <li key={i} className="text-slate-300 text-sm">• {o}</li>)}</ul>
          </div>
          <div className="bg-amber-500/10 rounded-lg p-4 border border-amber-500/20">
            <h4 className="font-medium text-amber-400 mb-3 flex items-center gap-2"><Target className="w-4 h-4" /> Threats</h4>
            <ul className="space-y-2">{swot.threats.map((t, i) => <li key={i} className="text-slate-300 text-sm">• {t}</li>)}</ul>
          </div>
        </div>
      )}

      {activeTab === 'influencers' && (
        <div className="grid grid-cols-3 gap-4">
          {influencers.map((inf, i) => (
            <div key={i} className="bg-slate-800 rounded-lg p-4">
              <div className="flex items-center justify-between mb-2">
                <span className="font-medium text-white">{inf.name}</span>
                <span className={`text-xs px-2 py-1 rounded border ${voiceTypeBadge(inf.voice_type)}`}>{inf.voice_type}</span>
              </div>
              <p className="text-slate-500 text-sm">{inf.username}</p>
              <p className="text-slate-400 text-sm mt-1">{(inf.followers / 1000).toFixed(0)}K followers</p>
            </div>
          ))}
        </div>
      )}
    </div>
  );
}

Instructions for Claude

CRITICAL REQUIREMENTS

1. FETCH REAL DATA - You MUST call the XPOZ MCP tools and fetch real tweets. Do NOT generate fake data or skip API calls.

2. USE EXPANDED QUERIES - Always expand brand names to include ticker symbols:

   • NVIDIA → "NVIDIA" OR "$NVDA"
   • Tesla → "Tesla" OR "$TSLA"

3. ASYNC POLLING - After calling getTwitterPostsByKeywords or getTwitterUsersByKeywords, you MUST poll checkOperationStatus until status is "completed".

4. MINIMUM DATA - Fetch at least 200 tweets and 50 influencers for reliable analysis.

5. REAL QUOTES - The key_quotes and sample_tweet fields must contain actual tweets from the fetched data, not fabricated examples.

6. ANALYST CITATIONS - In analysts_view, cite real analysts/journalists by name with affiliation if mentioned in tweets.

Data Collection Steps

1. Accept brand name from user
2. Expand query to include ticker symbol
3. Call getTwitterPostsByKeywords with expanded query
4. Poll checkOperationStatus until complete
5. Call getTwitterUsersByKeywords with same query
6. Poll checkOperationStatus until complete
7. Analyze sentiment from tweet content
8. Extract 5+ narratives from recurring themes
9. Generate SWOT from positive/negative sentiment drivers
10. Classify influencers by voice type
11. Render React artifact with all data populated
12. Generate standalone HTML report

MCP Configuration

Endpoint: https://mcp.xpoz.ai/mcp
Transport: HTTP Streamable (not SSE)
Auth: Bearer Token
Tools: getTwitterPostsByKeywords, getTwitterUsersByKeywords, checkOperationStatus