Social Spy Monitor

Social Listening & Brand Reputation Monitor Skill

Overview

This skill builds a real-time brand reputation monitoring system that: 1. Apify scrapes Twitter/X, Reddit, forums, and news sites for every mention of your brand 2. Claude (OpenClaw) analyzes sentiment, detects crises, and classifies each mention 3. Alerts fire instantly to Slack, Telegram, or email when reputation risk is detected

The result: you know what people are saying about your brand the moment they say it — and you can respond before it becomes a crisis.

> 🔗 Apify: https://www.apify.com/?fpr=dx06p

What This Skill Does

Architecture Overview

┌──────────────────────────────────────────────────────────────────┐
│           SOCIAL LISTENING & REPUTATION MONITOR                  │
│                                                                  │
│  ┌──────────────────────────────────────────────────────────┐   │
│  │  LAYER 1 — MENTION SCRAPING (Apify)                      │   │
│  │  Twitter/X │ Reddit │ Hacker News │ Google News           │   │
│  │  Trustpilot │ G2 │ App Store │ Niche Forums               │   │
│  └───────────────────────────┬──────────────────────────────┘   │
│                              │                                   │
│  ┌───────────────────────────▼──────────────────────────────┐   │
│  │  LAYER 2 — REPUTATION ANALYSIS ENGINE (Claude)           │   │
│  │                                                          │   │
│  │  • Sentiment Classifier   → pos / neg / neutral + score  │   │
│  │  • Crisis Detector        → spike in neg mentions        │   │
│  │  • Topic Categorizer      → product | support | pr | etc │   │
│  │  • Influence Scorer       → who is talking (reach)       │   │
│  │  • Response Generator     → suggested reply drafts       │   │
│  └───────────────────────────┬──────────────────────────────┘   │
│                              │                                   │
│  ┌───────────────────────────▼──────────────────────────────┐   │
│  │  LAYER 3 — ALERTS & REPORTING                            │   │
│  │  Slack │ Telegram │ Email │ Dashboard │ Weekly Report     │   │
│  └──────────────────────────────────────────────────────────┘   │
└──────────────────────────────────────────────────────────────────┘

Step 1 — Get Your API Keys

Apify

   export APIFY_TOKEN=apify_api_xxxxxxxxxxxxxxxx
   

Claude / OpenClaw

export CLAUDE_API_KEY=sk-ant-xxxxxxxxxxxxxxxx

Slack Webhook (optional)

   export SLACK_WEBHOOK_URL=https://hooks.slack.com/services/xxx/xxx/xxx
   

Telegram Bot (optional)

export TELEGRAM_BOT_TOKEN=123456789:AABBccDDeeFFggHH
export TELEGRAM_CHAT_ID=-1001234567890

Step 2 — Install Dependencies

npm install apify-client axios node-cron dotenv

Configuration — Define Your Brand

// config.js
export const BRAND_CONFIG = {
  brandName: "YourBrand",
  keywords: [
    "YourBrand",
    "YourBrand.com",
    "@YourBrandHandle",
    "#YourBrand",
    "your brand common misspelling"
  ],
  competitors: ["CompetitorA", "CompetitorB"],
  crisisThreshold: {
    negativeSpike: 5,       // alert if 5+ negative mentions in one scan
    sentimentDrop: 20,      // alert if sentiment score drops 20 points
    viralThreshold: 1000    // alert if a negative post hits 1000+ engagements
  },
  language: "en",
  timezone: "America/New_York"
};

Layer 1 — Multi-Platform Mention Scraper (Apify)

Scrape Twitter/X Mentions

import ApifyClient from 'apify-client';
import { BRAND_CONFIG } from './config.js';
const apify = new ApifyClient({ token: process.env.APIFY_TOKEN });
async function scrapeTwitterMentions() {
  console.log("🐦 Scraping Twitter/X mentions...");
  const run = await apify.actor("apify/twitter-scraper").call({
    searchTerms: BRAND_CONFIG.keywords,
    maxTweets: 100,
    addUserInfo: true,
    startUrls: [],
    languageFilter: BRAND_CONFIG.language
  });
  const { items } = await run.dataset().getData();
  return items.map(t => ({
    source:      "twitter",
    id:          t.id,
    text:        t.fullText || t.text,
    author:      t.author?.userName,
    authorName:  t.author?.name,
    followers:   t.author?.followers || 0,
    verified:    t.author?.isVerified || false,
    likes:       t.likeCount || 0,
    retweets:    t.retweetCount || 0,
    replies:     t.replyCount || 0,
    engagements: (t.likeCount || 0) + (t.retweetCount || 0) * 2 + (t.replyCount || 0),
    url:         t.url,
    createdAt:   t.createdAt,
    scrapedAt:   new Date().toISOString()
  }));
}

Scrape Reddit Mentions

async function scrapeRedditMentions() {
  console.log("👽 Scraping Reddit mentions...");
  const searchQueries = BRAND_CONFIG.keywords.map(k =>
    apify.actor("apify/reddit-search-scraper").call({
      queries: [k],
      maxItems: 30,
      sort: "new"
    }).then(run => run.dataset().getData())
      .then(d => d.items)
  );
  const results = await Promise.all(searchQueries);
  return results.flat().map(p => ({
    source:      "reddit",
    id:          p.id,
    text:        p.title + " " + (p.selftext || ""),
    title:       p.title,
    author:      p.author,
    subreddit:   p.subreddit,
    score:       p.score,
    comments:    p.numComments,
    upvoteRatio: p.upvoteRatio,
    engagements: p.score + p.numComments * 2,
    url:         p.url,
    createdAt:   new Date(p.created * 1000).toISOString(),
    scrapedAt:   new Date().toISOString()
  }));
}

Scrape News & Review Platforms

async function scrapeNewsAndReviews() {
  console.log("📰 Scraping news and reviews...");
  const brandQuery = BRAND_CONFIG.brandName;
  const [news, trustpilot, hackerNews] = await Promise.all([
    // Google News
    apify.actor("apify/google-search-scraper").call({
      queries: ["${brandQuery}" news],
      maxPagesPerQuery: 2,
      resultsPerPage: 20,
      dateRange: "pastWeek"
    }).then(run => run.dataset().getData())
      .then(d => d.items.map(r => ({
        source:    "google_news",
        text:      r.title + " " + r.snippet,
        title:     r.title,
        url:       r.url,
        createdAt: r.date || new Date().toISOString(),
        scrapedAt: new Date().toISOString()
      }))),
    // Trustpilot reviews
    apify.actor("apify/trustpilot-scraper").call({
      startUrls: [{ url: https://www.trustpilot.com/review/${brandQuery.toLowerCase()}.com }],
      maxReviews: 50,
      filterScore: [1, 2, 3]   // focus on negative/neutral
    }).then(run => run.dataset().getData())
      .then(d => d.items.map(r => ({
        source:    "trustpilot",
        text:      r.reviewBody,
        title:     r.reviewTitle,
        rating:    r.ratingValue,
        author:    r.author,
        url:       r.url,
        createdAt: r.datePublished,
        scrapedAt: new Date().toISOString()
      }))).catch(() => []),  // graceful fail if brand not on Trustpilot
    // Hacker News
    apify.actor("apify/hacker-news-scraper").call({
      searchQuery: brandQuery,
      maxItems: 20,
      type: "story"
    }).then(run => run.dataset().getData())
      .then(d => d.items.map(r => ({
        source:    "hacker_news",
        text:      r.title + " " + (r.text || ""),
        title:     r.title,
        author:    r.by,
        score:     r.score,
        comments:  r.descendants,
        url:       r.url || https://news.ycombinator.com/item?id=${r.id},
        createdAt: new Date(r.time * 1000).toISOString(),
        scrapedAt: new Date().toISOString()
      }))).catch(() => [])
  ]);
  return [...news, ...trustpilot, ...hackerNews];
}

Aggregate All Mentions

async function scrapeAllMentions() {
  const [twitter, reddit, newsReviews] = await Promise.all([
    scrapeTwitterMentions(),
    scrapeRedditMentions(),
    scrapeNewsAndReviews()
  ]);
  const all = [...twitter, ...reddit, ...newsReviews];
  // Deduplicate by URL
  const seen = new Set();
  return all.filter(m => {
    if (seen.has(m.url)) return false;
    seen.add(m.url);
    return true;
  });
}

Layer 2 — Reputation Analysis Engine (Claude)

Sentiment Classifier

import axios from 'axios';
const claude = axios.create({
  baseURL: 'https://api.anthropic.com/v1',
  headers: {
    'x-api-key': process.env.CLAUDE_API_KEY,
    'anthropic-version': '2023-06-01',
    'Content-Type': 'application/json'
  }
});
async function analyzeSentiment(mentions) {
  const prompt = 
You are a brand reputation analyst. Analyze each mention and classify it.
BRAND: ${BRAND_CONFIG.brandName}
MENTIONS TO ANALYZE:
${JSON.stringify(mentions.slice(0, 30), null, 2)}
Respond ONLY in this JSON format:
{
  "analyzedMentions": [
    {
      "id": "mention id or url",
      "sentiment": "positive | negative | neutral | mixed",
      "sentimentScore": 7,
      "confidenceLevel": "high | medium | low",
      "emotionalTone": "angry | frustrated | disappointed | happy | excited | neutral | sarcastic",
      "category": "product_feedback | customer_support | pr_crisis | competitor_comparison | spam | praise | question | bug_report",
      "urgency": "critical | high | medium | low",
      "isInfluencer": true,
      "requiresResponse": true,
      "suggestedResponseTone": "apologetic | informative | appreciative | ignore",
      "keyTopics": ["topic1", "topic2"],
      "isCrisisSignal": false,
      "summary": "one-line summary of what was said"
    }
  ],
  "batchSentiment": {
    "positive": 0,
    "negative": 0,
    "neutral": 0,
    "mixed": 0,
    "overallScore": 65,
    "trend": "improving | declining | stable"
  },
  "crisisSignals": [
    {
      "signal": "description of the risk",
      "severity": "critical | high | medium",
      "source": "platform",
      "url": "url of the post",
      "recommendedAction": "what to do right now"
    }
  ],
  "topComplaintsThisRound": ["complaint 1", "complaint 2"],
  "topPraisesThisRound": ["praise 1", "praise 2"]
}
;
  const { data } = await claude.post('/messages', {
    model: "claude-opus-4-5",
    max_tokens: 4000,
    messages: [{ role: "user", content: prompt }]
  });
  return JSON.parse(data.content[0].text.replace(/

Crisis Detector

// CRISIS TRIGGER 1 — Spike in negative mentions if (batch.negative >= BRAND_CONFIG.crisisThreshold.negativeSpike) { crisisAlerts.push({ type: "negative_spike", severity: "critical", message: 🚨 ${batch.negative} negative mentions detected in this scan, threshold: BRAND_CONFIG.crisisThreshold.negativeSpike, current: batch.negative }); }

// CRISIS TRIGGER 2 — Sentiment score drop if (prev && (prev.score - batch.overallScore) >= BRAND_CONFIG.crisisThreshold.sentimentDrop) { crisisAlerts.push({ type: "sentiment_drop", severity: "high", message: 📉 Sentiment dropped from ${prev.score} to ${batch.overallScore} (-${prev.score - batch.overallScore} pts), previousScore: prev.score, currentScore: batch.overallScore }); }

// CRISIS TRIGGER 3 — High-engagement negative post const viralNegative = analysis.analyzedMentions?.filter(m => m.sentiment === "negative" && m.urgency === "critical" ) || [];

if (viralNegative.length > 0) { crisisAlerts.push({ type: "viral_negative", severity: "high", message: 🔥 ${viralNegative.length} high-urgency negative mention(s) detected, mentions: viralNegative.map(m => m.id) }); }

// Add explicit crisis signals from Claude signals.forEach(signal => { if (signal.severity === "critical" || signal.severity === "high") { crisisAlerts.push({ ...signal, type: "claude_signal" }); } });

return crisisAlerts; }

Response Suggestion Generator

const prompt = You are a brand communications expert. Write response suggestions for these urgent mentions. Be empathetic, on-brand, and action-oriented. Never defensive.

BRAND: ${BRAND_CONFIG.brandName}

URGENT MENTIONS REQUIRING RESPONSE: ${JSON.stringify(urgentMentions.slice(0, 5), null, 2)}

Respond ONLY in this JSON format: { "suggestions": [ { "mentionId": "id or url", "platform": "twitter | reddit | etc", "originalText": "what they said (summarized)", "sentiment": "negative | mixed", "responseOptions": [ { "tone": "apologetic", "response": "full suggested response text", "bestFor": "when the issue is your fault" }, { "tone": "informative", "response": "full suggested response text", "bestFor": "when it is a misunderstanding" } ], "doNotDo": "what to avoid saying in this specific case", "priority": "respond within 1h | 4h | 24h" } ] } ;

const { data } = await claude.post('/messages', { model: "claude-opus-4-5", max_tokens: 2500, messages: [{ role: "user", content: prompt }] });

return JSON.parse(data.content[0].text.replace(/`json|`/g, '').trim()); }

Layer 3 — Alerts & Reporting
Slack Alert Publisher

const payload = { attachments: [{ color, blocks: [ { type: "header", text: { type: "plain_text", text: ${icon} Brand Alert: ${BRAND_CONFIG.brandName} } }, { type: "section", fields: [ { type: "mrkdwn", text: *Sentiment Score:*\n${analysis.batchSentiment.overallScore}/100 }, { type: "mrkdwn", text: *Trend:*\n${analysis.batchSentiment.trend} }, { type: "mrkdwn", text: *Negative Mentions:*\n${analysis.batchSentiment.negative} }, { type: "mrkdwn", text: *Requires Response:*\n${responses?.suggestions?.length || 0} mentions } ] }, ...crisisAlerts.map(alert => ({ type: "section", text: { type: "mrkdwn", text: *${alert.severity?.toUpperCase()}:* ${alert.message}\n${alert.recommendedAction || ""} } })), { type: "section", text: { type: "mrkdwn", text: *Top Complaints:*\n${analysis.topComplaintsThisRound?.map(c => • ${c}).join('\n') || "None"} } } ] }] };

await axios.post(process.env.SLACK_WEBHOOK_URL, payload); }

Telegram Crisis Alert

const message = ${icon} *BRAND ALERT: ${BRAND_CONFIG.brandName}*

📊 *Reputation Score:* ${analysis.batchSentiment.overallScore}/100 (${analysis.batchSentiment.trend}) 😡 *Negative:* ${analysis.batchSentiment.negative} | 😊 *Positive:* ${analysis.batchSentiment.positive}

*🔴 Crisis Signals:* ${crisisAlerts.map(a => • [${a.severity?.toUpperCase()}] ${a.message}).join('\n')}

*📢 Top Complaints:* ${analysis.topComplaintsThisRound?.slice(0, 3).map(c => • ${c}).join('\n') || "• None"}

*✅ Top Praises:* ${analysis.topPraisesThisRound?.slice(0, 2).map(p => • ${p}).join('\n') || "• None"}

⏰ ${new Date().toLocaleString()} .trim();

await axios.post( https://api.telegram.org/bot${process.env.TELEGRAM_BOT_TOKEN}/sendMessage, { chat_id: process.env.TELEGRAM_CHAT_ID, text: message, parse_mode: "Markdown" } ); }

Weekly Reputation Report

return # 📣 Weekly Reputation Report — ${BRAND_CONFIG.brandName} Week ending: ${date}

📊 At a Glance

| Metric | Value | |---|---| | Reputation Score | ${avgScore}/100 | | Total Mentions | ${totalMentions} | | Negative Mentions | ${totalNegative} (${Math.round(totalNegative/totalMentions*100)}%) | | Crisis Events | ${weekData.filter(d => d.hadCrisis).length} | | Trend | ${avgScore >= 70 ? "✅ Healthy" : avgScore >= 50 ? "⚠️ Watch" : "🚨 At Risk"} |

📈 Day-by-Day Sentiment

${weekData.map(d => ${d.date} — Score: ${d.score}/100 | Mentions: ${d.mentions} | Neg: ${d.negative} ).join('\n')}

🔴 Top Complaints This Week

🟢 Top Praises This Week

💡 Recommended Actions

Master Orchestrator — Full Pipeline

async function runSocialListening() { console.log(\n👂 Social Listening scan — ${new Date().toISOString()});

try { // STEP 1 — Scrape all platforms console.log("[1/5] Scraping mentions..."); const mentions = await scrapeAllMentions(); console.log( ✅ ${mentions.length} mentions collected);

if (mentions.length === 0) { console.log(" ℹ️ No new mentions found"); return; }

// STEP 2 — Analyze sentiment console.log("[2/5] Analyzing sentiment with Claude..."); const analysis = await analyzeSentiment(mentions); const score = analysis.batchSentiment.overallScore; console.log( ✅ Score: ${score}/100 | Neg: ${analysis.batchSentiment.negative} | Trend: ${analysis.batchSentiment.trend});

// STEP 3 — Detect crisis console.log("[3/5] Checking for crisis signals..."); const crisisAlerts = detectCrisis(analysis); console.log( ✅ ${crisisAlerts.length} crisis signal(s) detected);

// STEP 4 — Generate response suggestions for urgent mentions const urgentMentions = analysis.analyzedMentions?.filter(m => m.requiresResponse && (m.urgency === "critical" || m.urgency === "high") ) || []; let responses = { suggestions: [] };

if (urgentMentions.length > 0) { console.log([4/5] Generating ${urgentMentions.length} response suggestions...); responses = await generateResponseSuggestions(urgentMentions); console.log( ✅ ${responses.suggestions?.length} response drafts ready); }

// STEP 5 — Send alerts if needed if (crisisAlerts.length > 0) { console.log("[5/5] Sending crisis alerts..."); if (process.env.SLACK_WEBHOOK_URL) { await sendSlackAlert(crisisAlerts, analysis, responses); } if (process.env.TELEGRAM_BOT_TOKEN) { await sendTelegramAlert(crisisAlerts, analysis); } console.log(" ✅ Alerts sent"); } else { console.log("[5/5] No alerts needed — reputation looks healthy"); }

// Save report const report = { scannedAt: new Date().toISOString(), mentionsFound: mentions.length, sentimentScore: score, trend: analysis.batchSentiment.trend, crisisAlerts, topComplaints: analysis.topComplaintsThisRound, topPraises: analysis.topPraisesThisRound, responseSuggestions: responses.suggestions };

writeFileSync(./reputation-log-${Date.now()}.json, JSON.stringify(report, null, 2)); return report;

} catch (err) { console.error("Listening error:", err.message); } }

// Scan every hour cron.schedule('0 * * * *', runSocialListening);

// Run immediately on startup runSocialListening();

Environment Variables

Normalized Mention Schema

Best Practices
Scan every 30–60 minutes for real-time monitoring, every 4 hours for standard tracking
Always monitor competitor brand names in parallel for benchmarking opportunities
Set crisisThreshold.negativeSpike based on your normal daily volume — not a fixed number

Flag and ignore spam/bot mentions — Claude's confidenceLevel field helps filter these

Route critical alerts to on-call Slack/phone, high alerts to the team channel

Use the response suggestions as drafts only — always have a human review before posting
Archive all mention logs for quarterly trend analysis and PR reporting
Error Handling