/brand-snapshot

name: brand-snapshot
version: 2025-01-13
description: Deep-dive brand intelligence analysis using XPOZ MCP. Analyzes sentiment, extracts narratives, identifies influencers, and generates SWOT analysis. Use when asked for "brand snapshot", "brand analysis", "what's the narrative on X", or "sentiment analysis for X brand".

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 c