Mode d’emploi
- Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
- Consultez le document original pour les outils, identifiants et environnements requis.
- Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/apidojo-io/apidojo-skills --skill monitoring-instagram-brand-mentionsAvant de commencer
- L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
- Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.
Monitoring Instagram Brand Mentions
Tracks all public Instagram posts mentioning a brand — via branded hashtags, @mentions, or product name keywords. Classifies mentions by sentiment and type (UGC, complaint, press coverage, competitor comparison).
Prerequisites
APIFY_TOKENenvironment variable set- Optional: Apify MCP server installed
Inputs
| Parameter | Type | Required | Default | Notes |
|---|---|---|---|---|
startUrls | array | ✅ | [] | Instagram URLs — profiles, hashtags, locations, audio pages, reels |
until | string | Optional | — | Scrape posts until this date (YYYY-MM-DD) |
maxItems | number | Optional | Unlimited | Maximum posts to return |
customMapFunction | string | Optional | — | JavaScript function to transform each output object |
Workflow
Progress:
- [ ] Step 1: Build hashtag and keyword list
- [ ] Step 2: Run instagram-scraper for each hashtag
- [ ] Step 3: Classify mention type and sentiment
- [ ] Step 4: Identify top advocates and critics
- [ ] Step 5: Deliver brand health reportStep 1 & 2: Run instagram-scraper
Recommended — run_actor.js (handles waiting, output, and file saving automatically):
# Quick answer (prints table to chat)
node scripts/run_actor.js \
--actor "apidojo~instagram-scraper" \
--input '{"param": "value"}'
# Save as CSV
node scripts/run_actor.js \
--actor "apidojo~instagram-scraper" \
--input '{"param": "value"}' \
--output YYYY-MM-DD_results.csv --format csv
# Save as JSON
node scripts/run_actor.js \
--actor "apidojo~instagram-scraper" \
--input '{"param": "value"}' \
--output YYYY-MM-DD_results.json --format jsonAPIFY_TOKENmust be set in environment or.envfile.
If Apify MCP is available:
Tool: apify:run-actor
Actor: "apidojo~instagram-scraper"
Input:
{
"keywords": ["#[BRAND]", "#[BRAND]review", "#[BRAND]community"],
"maxItems": 100
}REST API fallback:
curl -X POST "https://api.apify.com/v2/acts/apidojo~instagram-scraper/runs?token=$APIFY_TOKEN" -H "Content-Type: application/json" -d '{"keywords": ["#[brand]", "#[brand]review"], "maxItems": 100}'Run for each hashtag cluster. Merge results and deduplicate by postUrl.
Step 2: Classify Mentions
Mention type:
UGC = post contains product photo + brand mention; author is not verified
COMPLAINT = caption contains negative indicators: "broken", "disappointed", "scam", "refund", "terrible", "never again"
POSITIVE_REVIEW = caption contains: "love", "amazing", "best", "recommend", "obsessed"
PRESS/EDITORIAL = author is verified OR follower_count > 100K
COMPETITOR_COMPARISON = caption mentions competitor brand alongside this brandSentiment: Apply same lexical classification as Twitter sentiment skill (positive/negative/neutral indicators).
Step 3: Score Reach
mention_reach = likes + comments * 5 + (followers_of_author / 100)Step 4: Edge Cases
- Official brand account's own posts in results: Drop posts where
ownerUsername= brand's own handle - Hashtag is overloaded (> 1M posts): Use long-tail branded hashtags instead; or filter by date
- Sentiment misclassified for complex posts: Flag posts with both positive and negative indicators as
MIXED; report count - Foreign language mentions dominant: Report language distribution; flag non-English mentions separately
Output Format
# Instagram Brand Mention Monitor: [BRAND]
Posts collected: [N] | Period: [DATE_RANGE] | Date: [DATE]
## Mention Type Distribution
UGC: [N] | Positive Reviews: [N] | Complaints: [N] | Press: [N] | Comparisons: [N]
## Sentiment Summary
Positive: [X%] | Negative: [X%] | Neutral: [X%]
Weighted by reach: Positive [X%] | Negative [X%]
## Top UGC Posts (Most Liked)
| Creator | @Handle | Likes | Type | Caption Excerpt | Post URL |
|---------|---------|-------|------|----------------|---------|
## Complaints to Address
| Creator | Likes | Complaint Summary | Post URL |
|---------|-------|------------------|---------|
## Top Brand Advocates (Most Frequent Positive Posters)
1. @[handle] — [N] positive posts | [N] avg likesTroubleshooting
Hashtag returns generic posts: The brand hashtag may be ambiguous (e.g. "#apple"). Use #[brand]official or #[brand][product] for precision. Mostly competitor posts: This may indicate your brand is being used in comparison posts — analyze COMPETITOR_COMPARISON category for positioning insights. Sentiment skewed by a single viral negative post: Check weighted sentiment vs. raw sentiment; one viral post can shift the raw numbers.
