Imported from apidojo-io/apidojo-skills (skills/intent/monitoring-instagram-hashtag-trends/SKILL.md). Install upstream with npx skills add apidojo-io/apidojo-skills --skill monitoring-instagram-hashtag-trends. Copyright stays with the author (Apache-2.0).

Monitoring Instagram Hashtag Trends

Analyzes hashtag performance on Instagram to identify which tags drive the best engagement for a content category. Builds a tiered hashtag strategy (broad/mid/niche) based on actual post data.

Prerequisites

• APIFY_TOKEN environment variable set
• Optional: Apify MCP server installed

Inputs

Workflow

Progress:
- [ ] Step 1: Scrape recent posts for each hashtag
- [ ] Step 2: Calculate per-hashtag metrics
- [ ] Step 3: Tier hashtags by competition/opportunity
- [ ] Step 4: Build recommended hashtag set

Step 1: Scrape Hashtags

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 json

APIFY_TOKEN must be set in environment or .env file.

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~instagram-scraper"
Input:
{
  "keywords": ["[HASHTAG_1]", "[HASHTAG_2]", "..."],
  "maxItems": 50
}

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": ["#[hashtag1]", "#[hashtag2]"], "maxItems": 50}'

Run per hashtag (or in batch if MCP supports multiple hashtags in one run).

Step 2: Calculate Metrics

For each hashtag:

avg_likes = mean(likesCount for all sampled posts)
avg_comments = mean(commentsCount for all sampled posts)
engagement_per_post = avg_likes + avg_comments
post_volume_estimate = total posts shown (from platform, if available)

opportunity_score = engagement_per_post / (post_volume_estimate / 10000 + 1)

Higher score = better engagement relative to competition.

Hashtag tier:

• HIGH_COMPETITION: > 1M posts — hard to rank; use rarely
• MID_TIER: 100K–1M posts — good reach/competition balance
• NICHE: < 100K posts — easier to rank, less reach but more targeted

Step 3: Edge Cases

• Hashtag is banned: If scrape returns 0 posts, hashtag may be banned by Instagram — drop from strategy
• Very new hashtag (< 1K posts): Can't calculate reliable metrics; flag as EMERGING — LOW DATA
• Same posts appear across multiple hashtags: Deduplicate when calculating engagement metrics; report true unique post count

Output Format

# Instagram Hashtag Strategy: [NICHE]
Hashtags tested: [N] | Date: [DATE]

## Performance by Hashtag
| Hashtag | Est. Posts | Avg Likes/Post | Avg Comments | Tier | Opportunity Score |
|---------|-----------|---------------|-------------|------|-----------------|
| #[tag] | [N] | [N] | [N] | MID_TIER | [0.XX] |

## Recommended Hashtag Set (Mix Strategy)
Use 20-30 hashtags per post in this ratio:
- 5 HIGH_COMPETITION tags: [list]
- 10 MID_TIER tags: [list]
- 10 NICHE tags: [list]

## Banned / Restricted Hashtags
Avoid: [list of any hashtags that returned 0 results]

Troubleshooting

Engagement data varies widely: Normal for Instagram; use median, not mean, to reduce outlier impact. Hashtag has many posts but low engagement: High volume + low engagement = dominated by bots or spam — low-value for reach; deprioritize. Niche hashtag auto-generation produces no results: Not all niches have well-established hashtag communities — focus on the ones that exist and perform.