modbender 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.negativeSpikebased on your normal daily volume — not a fixed number - Flag and ignore spam/bot mentions — Claude's
confidenceLevelfield helps filter these - Route
criticalalerts to on-call Slack/phone,highalerts 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