Social Listening Monitoring

v1.0.0

Monitor brand mentions across Twitter/X, Reddit, news, and forums in real-time with sentiment analysis, crisis detection, competitor tracking, and instant al...

⭐ 0· 14·0 current·0 all-time

by@nicemaths123

MIT-0

Security Scan

VirusTotal

Pending

View report →

OpenClaw

Suspicious

medium confidence

ℹ

Purpose & Capability

The described functionality (scraping mentions via Apify, analyzing with Claude/OpenClaw, and alerting via Slack/Telegram/email) is coherent with the code examples in SKILL.md. However, the registry metadata claims no required environment variables or primary credential while the SKILL.md explicitly instructs the user to provide APIFY_TOKEN, CLAUDE_API_KEY, and optional Slack/Telegram creds. The omission in metadata is a notable inconsistency.

Instruction Scope

The SKILL.md contains detailed runtime instructions that (a) install npm packages, (b) call Apify actors to scrape multiple platforms, (c) call a Claude/OpenClaw API for analysis, and (d) post alerts to external webhooks/services. The instructions access several environment variables (APIFY_TOKEN, CLAUDE_API_KEY, SLACK_WEBHOOK_URL, TELEGRAM_BOT_TOKEN, TELEGRAM_CHAT_ID) which are not declared in the skill metadata. The skill will collect and transmit user-provided monitoring data and scraped content to external endpoints — a privacy/secret-exfiltration risk if keys are misused or the skill origin is untrusted.

ℹ

Install Mechanism

This is instruction-only with no install spec or code files in the registry. The SKILL.md asks the user to run `npm install apify-client axios node-cron dotenv`, which is proportionate for the described Node.js implementation. No third-party downloads or obscure URLs are required by the install steps themselves, but the skill does reference Apify actors (third-party services) and an affiliate link; verify those actors and actors' behavior before use.

Credentials

The skill needs multiple sensitive credentials (APIFY_TOKEN, CLAUDE_API_KEY) and optional alerting credentials (Slack webhook URL, Telegram bot token/chat ID). Those variables are appropriate for the described features, but the registry metadata lists no required env vars or primary credential — this mismatch reduces transparency and increases risk. The CLAUDE_API_KEY in particular could grant broad access to a user's model/account if misused; ensure the key scope and ownership are appropriate.

✓

Persistence & Privilege

The skill does not request always:true or any system config paths and is not asking to modify other skills. It can be invoked autonomously (the platform default), which is expected for a monitoring skill; combined with the unreported credential requirements, autonomous invocation would increase the blast radius if keys were supplied, so exercise caution.

What to consider before installing

Do not install or supply secrets yet. Ask the publisher to update the registry metadata to explicitly list required environment variables (APIFY_TOKEN, CLAUDE_API_KEY, SLACK_WEBHOOK_URL, TELEGRAM_BOT_TOKEN, TELEGRAM_CHAT_ID) and to provide a verifiable source/homepage. Verify the skill's origin and review the Apify actors it invokes to ensure they behave as expected. Use least-privilege credentials (create dedicated, scoped API tokens/webhooks that can be revoked), test in a sandbox account, and confirm where scraped content and analysis results are sent and stored. If you rely on the platform's built-in model access, confirm whether CLAUDE_API_KEY should be a user-provided key or the agent's own runtime credential—never paste long-lived unrestricted keys into unclear third-party instruction sets. If the publisher cannot justify the missing metadata or provenance, treat the skill as untrusted.

Like a lobster shell, security has layers — review code before you run it.

latestvk973vxf13dtnzeaskggnc2mpyn844dpw

License

MIT-0

Free to use, modify, and redistribute. No attribution required.

Termshttps://spdx.org/licenses/MIT-0.html

SKILL.md

Social Listening & Brand Reputation Monitor Skill

Overview

This skill builds a real-time brand reputation monitoring system that:

Apify scrapes Twitter/X, Reddit, forums, and news sites for every mention of your brand
Claude (OpenClaw) analyzes sentiment, detects crises, and classifies each mention
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

Monitor Twitter/X, Reddit, forums, and news for brand mentions in real-time
Perform sentiment analysis on every mention (positive / negative / neutral)
Detect crisis signals — sudden spikes in negative mentions
Track competitor mentions for comparative reputation benchmarking
Score reputation health over time with a rolling dashboard score
Alert immediately on Slack/Telegram when a crisis threshold is crossed
Generate weekly reputation reports with trends and actionable insights
Distinguish genuine complaints from spam or bot activity

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

Sign up at https://www.apify.com/?fpr=dx06p
Go to Settings → Integrations

Copy your token:

export APIFY_TOKEN=apify_api_xxxxxxxxxxxxxxxx

Claude / OpenClaw

export CLAUDE_API_KEY=sk-ant-xxxxxxxxxxxxxxxx

Slack Webhook (optional)

Go to api.slack.com/apps → Create App → Incoming Webhooks

Copy the webhook URL:

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(/```json|```/g, '').trim());
}

Crisis Detector

// Rolling sentiment history (use Redis/DB in production)
const sentimentHistory = [];

function detectCrisis(analysis) {
  const crisisAlerts = [];
  const batch = analysis.batchSentiment;
  const signals = analysis.crisisSignals || [];

  // Track history
  sentimentHistory.push({
    score: batch.overallScore,
    negative: batch.negative,
    timestamp: new Date().toISOString()
  });

  const prev = sentimentHistory[sentimentHistory.length - 2];

  // 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

async function generateResponseSuggestions(urgentMentions) {
  if (urgentMentions.length === 0) return [];

  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

async function sendSlackAlert(crisisAlerts, analysis, responses) {
  const isCrisis = crisisAlerts.some(a => a.severity === "critical");
  const color = isCrisis ? "#FF0000" : "#FFA500";
  const icon = isCrisis ? "🚨" : "⚠️";

  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

async function sendTelegramAlert(crisisAlerts, analysis) {
  const severity = crisisAlerts[0]?.severity || "medium";
  const icon = severity === "critical" ? "🚨🚨🚨" : "⚠️";

  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

function generateWeeklyReport(weekData) {
  const avgScore = Math.round(
    weekData.reduce((sum, d) => sum + d.score, 0) / weekData.length
  );
  const totalMentions = weekData.reduce((sum, d) => sum + d.mentions, 0);
  const totalNegative = weekData.reduce((sum, d) => sum + d.negative, 0);
  const date = new Date().toLocaleDateString('en-US', { month: 'long', day: 'numeric', year: 'numeric' });

  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
${weekData.flatMap(d => d.complaints || []).slice(0, 8).map(c => `- ${c}`).join('\n')}

---

## 🟢 Top Praises This Week
${weekData.flatMap(d => d.praises || []).slice(0, 5).map(p => `- ${p}`).join('\n')}

---

## 💡 Recommended Actions
1. Address top recurring complaint systematically — not just one-by-one
2. Amplify positive mentions by engaging with brand advocates
3. Monitor competitor sentiment for positioning opportunities

---
*Generated by Social Listening Bot • Powered by Apify + Claude*
`;
}

Master Orchestrator — Full Pipeline

import cron from 'node-cron';
import { writeFileSync } from 'fs';

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

# .env
APIFY_TOKEN=apify_api_xxxxxxxxxxxxxxxx
CLAUDE_API_KEY=sk-ant-xxxxxxxxxxxxxxxx

# Alerts
SLACK_WEBHOOK_URL=https://hooks.slack.com/services/xxx/xxx/xxx
TELEGRAM_BOT_TOKEN=123456789:AABBccDDeeFFggHH
TELEGRAM_CHAT_ID=-1001234567890

# Optional
ALERT_EMAIL=team@yourbrand.com

Normalized Mention Schema

{
  "source": "twitter",
  "text": "Just tried YourBrand and honestly it is broken...",
  "author": "user123",
  "followers": 12400,
  "engagements": 847,
  "sentiment": "negative",
  "sentimentScore": 2,
  "emotionalTone": "frustrated",
  "category": "product_feedback",
  "urgency": "high",
  "requiresResponse": true,
  "isCrisisSignal": false,
  "keyTopics": ["bug", "login", "performance"],
  "url": "https://twitter.com/user123/status/xxx",
  "createdAt": "2025-02-25T09:00:00Z"
}

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

try {
  const mentions = await scrapeAllMentions();
  return mentions;
} catch (error) {
  if (error.statusCode === 401) throw new Error("Invalid Apify token");
  if (error.statusCode === 429) throw new Error("Rate limit hit — space out scraping intervals");
  if (error.message.includes("TELEGRAM")) throw new Error("Telegram config error — check token and chat ID");
  throw error;
}

Requirements

Apify account → https://www.apify.com/?fpr=dx06p
Claude / OpenClaw API key
Node.js 18+ with apify-client, axios, node-cron
Slack workspace and/or Telegram bot for alerts
Optional: Redis for persistent sentiment history and trend tracking across restarts

Files

1 total

Select a file

Select a file to preview.

Comments

Loading comments…