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monitoring-tiktok-mentions-of-brand — Claude Code skill

Tracks brand mentions and product discussions on TikTok using Apify, analyzing sentiment and viral clips. Helps PR and marketing teams catch reputation shifts early.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add apidojo-io/apidojo-skills --skill monitoring-tiktok-mentions-of-brand -a claude-code
Open on GitHub

What monitoring-tiktok-mentions-of-brand does

The skill searches TikTok for company hashtags and targeted terms through the apidojo/tiktok-scraper actor on Apify. It retrieves post metadata, applies date filters, and groups clips into unboxings or buyer feedback. Search requests handle regional ISO country codes and order entries by post date or relevance.

Tone classification relies on lexical signals balanced by like tallies to gauge audience agreement. When a clip's reach surges past the creator's follower baseline, the process labels it trending. Crisis alerts flag severe backlash. They surface when negative feedback spikes within 48 hours or when comment activity signals community debate.

The resulting summary outlines sentiment distributions, viral footage, and key advocate accounts. Users can launch the workflow using scripts/run_actor.js, through the Apify MCP tool apify:run-actor, or via direct REST endpoints. Without setting the APIFY_TOKEN environment variable, execution stops with an error.

What it helps with

  • track brand and competitor mentions across TikTok
  • categorize TikTok videos into reviews and customer complaints
  • detect sudden reputation crises and complaint spikes
  • identify viral videos exceeding creator audience reach
  • extract creator handles, view counts, and engagement stats

Example prompts for the agent

  • Track mentions of brand Acme on TikTok this week and check overall sentiment.
  • Monitor TikTok hashtags for our product launch and flag any viral complaints.
  • Find creators discussing competitor brand Acme and show top trending posts.

How to install monitoring-tiktok-mentions-of-brand

1Skills CLI (Vercel)

npx skills add apidojo-io/apidojo-skills --skill monitoring-tiktok-mentions-of-brand -a claude-code

2Manually: put the files in place

~/.claude/skills/monitoring-tiktok-mentions-of-brand/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/monitoring-tiktok-mentions-of-brand/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about monitoring-tiktok-mentions-of-brand

How do I install monitoring-tiktok-mentions-of-brand in Claude Code?

Run npx skills add apidojo-io/apidojo-skills --skill monitoring-tiktok-mentions-of-brand -a claude-code in your terminal. The file is saved to ~/.claude/skills/monitoring-tiktok-mentions-of-brand/SKILL.md.

What does monitoring-tiktok-mentions-of-brand do?

It queries TikTok via Apify to find posts discussing designated brands or keywords. The skill analyzes sentiment, categorizes clip types, highlights viral content, and alerts on crisis signals.

What are the prerequisites for this skill?

You need an APIFY_TOKEN environment variable to authenticate with Apify. The skill runs through scripts/run_actor.js, the Apify MCP tool apify:run-actor, or direct REST requests.

Source text

SKILL.md · Apache-2.0
---
name: monitoring-tiktok-mentions-of-brand
description: >
  Monitors TikTok for brand mentions and product discussions using apidojo's TikTok scraper on Apify.
  Triggers when the user asks to: track mentions of a brand on TikTok, monitor TikTok hashtags for
  brand content, find TikTok videos talking about a product or company, see what TikTok says about
  a brand this week, track TikTok reactions to a product launch, find TikTok creators who mentioned
  a competitor brand, monitor brand sentiment on TikTok, or discover viral TikTok content about a
  specific brand or product.
  Returns creator handle, views, likes, comments, sentiment signal, and video caption excerpt.
  Ideal for brand managers, community teams, crisis communications, and product marketing teams.
license: Apache-2.0
metadata:
  author: apidojo
  version: "1.0"
  apify-actor: apidojo/tiktok-scraper
---

# Monitoring TikTok Mentions of a Brand

Tracks TikTok content mentioning a brand via branded hashtags and keyword searches. TikTok is the fastest-moving platform for brand sentiment — viral criticism or praise can emerge in hours.

## Prerequisites

- `APIFY_TOKEN` environment variable set
- Optional: Apify MCP server installed

## Inputs

| Parameter | Type | Required | Default | Notes |
|-----------|------|----------|---------|-------|
| `startUrls` | array | Optional | `[]` | TikTok URLs — user profiles, hashtags, music pages, search, locations |
| `keywords` | array | Optional | `[]` | Search keywords/terms to find posts |
| `sortType` | string | Optional | `RELEVANCE` | Sort order for keyword results: `RELEVANCE`, `MOST_LIKED`, `DATE_POSTED` |
| `location` | string | Optional | — | ISO 3166-1 alpha-2 country code for regional filtering (e.g. `US`, `GB`) |
| `maxItems` | number | Optional | Unlimited | Maximum posts to return across the run |
| `includeSearchKeywords` | boolean | Optional | `false` | Add the matched search keyword field to each post |
| `customMapFunction` | string | Optional | — | JavaScript function to transform each output object |

## Workflow

```
Progress:
- [ ] Step 1: Run tiktok-scraper for branded hashtags
- [ ] Step 2: Filter by view count and date
- [ ] Step 3: Classify sentiment and content type
- [ ] Step 4: Identify trending posts and crisis signals
- [ ] Step 5: Deliver monitoring report
```

### Step 1: Run the Actor


**Recommended — run_actor.js (handles waiting, output, and file saving automatically):**
```bash
# Quick answer (prints table to chat)
node scripts/run_actor.js \
  --actor "apidojo~tiktok-scraper" \
  --input '{"param": "value"}'

# Save as CSV
node scripts/run_actor.js \
  --actor "apidojo~tiktok-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.csv --format csv

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~tiktok-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.json --format json
```
> `APIFY_TOKEN` must be set in environment or `.env` file.

**If Apify MCP is available:**
```
Tool: apify:run-actor
Actor: "apidojo~tiktok-scraper"
Input:
{
  "keywords": ["#[BRAND]", "#[BRAND]review", "#[BRAND]honest"],
  "maxItems": 200
}
```

**REST API fallback:**
```bash
curl -X POST   "https://api.apify.com/v2/acts/apidojo~tiktok-scraper/runs?token=$APIFY_TOKEN"   -H "Content-Type: application/json"   -d '{
    "keywords": ["#[brand]", "#[brand]review", "#[brand]honest"],
    "maxItems": 200
  }'
```

### Step 2: Classify and Score

**Content type:**
```
UNBOXING = "unboxing", "first impression", "first look"
REVIEW = "review", "honest", "real talk", "thoughts on"
COMPLAINT = "disappointed", "refund", "scam", "doesn't work", "returning"
TUTORIAL = "how to", "tips", "tutorial"
ENTERTAINMENT = trend audio, no product focus
```

**Sentiment** (lexical — same model as Twitter sentiment skill):
- Use positive/negative/neutral indicators
- Weight by `diggCount` (likes) as community agreement signal

**Virality signal:**
```
virality = playCount / (follower_count_of_creator + 1)
```
If `virality > 2.0`: post is reaching well beyond the creator's audience — flag as `TRENDING`

### Step 3: Crisis Detection

Flag `CRISIS_ALERT` when:
- Any single post with `playCount > 500K` AND `sentiment = NEGATIVE`
- More than 5 complaint posts in 48 hours
- Comment-to-view ratio > 3% on a negative post (high engagement = controversy)

### Step 4: Edge Cases

- **Hashtag overloaded with unrelated content**: Add brand sub-product or model name to narrow
- **Brand has low TikTok presence** (< 10 posts): This is notable data — report it; brand may need proactive TikTok strategy
- **Duet/Stitch posts about brand**: These count as mentions but are often reactions to original content — classify as `REACTION` and note the source video

## Output Format

```
# TikTok Brand Monitor: [BRAND_NAME]
Period: [DATE_RANGE] | Posts collected: [N] | Total estimated reach: [N] views | Date: [DATE]

## Overall Sentiment
Positive: [X%] | Negative: [X%] | Neutral: [X%]
⚠️ CRISIS ALERTS: [N] (posts above threshold — see below)

## Content Type Distribution
Unboxing: [N] | Reviews: [N] | Complaints: [N] | Tutorials: [N]

## Trending Posts (> 100K Views)
| Creator | @Handle | Views | Likes | Sentiment | Type | Caption Preview |
|---------|---------|-------|-------|-----------|------|----------------|

## CRISIS ALERTS (Negative + High Reach)
| Creator | Views | Complaint Theme | Days Live | Post URL |
|---------|-------|----------------|-----------|---------|

## Top Advocates
| Creator | @Handle | Followers | Views | Post Type | Score |
|---------|---------|-----------|-------|----------|-------|
```

## Troubleshooting

**Very few posts found**: Brand may be using a different hashtag convention. Try product name without brand (`#[product]` not `#[brand]product`).
**All results are unboxing/haul content**: Normal for consumer brands — this is positive. Set monitoring to alert only on negative content.
**Crisis alert triggered by troll campaign**: Check if complaint posts are from a cluster of new accounts (created within 30 days, < 100 followers) — may be coordinated; note this context in report.

© apidojo · Apache-2.0 · apidojo-io/apidojo-skills

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