benchmarking-instagram-influencer-engagement

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Benchmarks and compares Instagram influencer engagement rates using apidojo's Instagram scraper on Apify. Triggers when the user asks to: compare engagement rates of Instagram accounts, check if an influencer has real or fake followers, analyze Instagram account performance metrics, benchmark a creator against competitors on Instagram, find Instagram accounts with unusually high or low engagement, verify influencer stats before a paid partnership, or audit an Instagram account's post performance. Returns follower count, average likes, average comments, engagement rate, and recent post performance. Ideal for influencer agencies, brand marketing teams, and campaign performance analysts.

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Sourceapidojo-io/apidojo-skills

Added on2026-09-03

NPX Install

npx skill4agent add apidojo-io/apidojo-skills benchmarking-instagram-influencer-engagement

SKILL.md Content

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Benchmarking Instagram Influencer Engagement

Pulls recent post data for one or multiple Instagram accounts and calculates engagement rate, consistency, and performance benchmarks. Used to vet influencers before partnerships or compare accounts against each other.

Prerequisites

```
APIFY_TOKEN
```
environment 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: Get account list and analysis goal
- [ ] Step 2: Run instagram-scraper for each account
- [ ] Step 3: Calculate engagement rate per account
- [ ] Step 4: Apply benchmark standards
- [ ] Step 5: Deliver comparison report

Step 1: Clarify Parameters

Ask the user for:

Instagram handles to analyze (up to 10 accounts; each handle without @)
Number of recent posts to analyze (default: 30 — enough for statistical reliability)
Analysis goal — vetting for partnership, competitive benchmarking, or general audit

Step 2: Run the Actor

Run separately for each account or pass as a list.

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~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:
{
  "usernames": ["[handle1]", "[handle2]", "[handle3]"],
  "maxItems": 30
}

REST API fallback:

bash

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~instagram-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "usernames": ["[handle1]", "[handle2]"],
    "maxItems": 30
  }'

Wait for

SUCCEEDED

. Fetch dataset.

Step 3: Calculate Engagement Metrics

For each account, from its posts array:

avg_likes = sum(post.likesCount) / num_posts
avg_comments = sum(post.commentsCount) / num_posts
engagement_rate = (avg_likes + avg_comments) / follower_count * 100
posting_frequency = num_posts / date_range_days

Benchmark standards by follower tier:

Tier	Followers	Good ER	Great ER
Nano	1K–10K	5–8%	>8%
Micro	10K–100K	3–5%	>5%
Mid	100K–500K	1.5–3%	>3%
Macro	500K–1M	1–2%	>2%
Mega	1M+	0.5–1%	>1%

Step 4: Flag Anomalies

Flag accounts with suspicious engagement patterns:

Low ER relative to tier: ER below 0.5% suggests inflated followers
Like/Comment imbalance: Very high likes but near-zero comments suggests like-buying
Engagement spikes: One post with 10x normal engagement followed by dead content suggests bought promotion
Sudden follower jump: Cross-reference last follower count change (if visible)

Step 5: Format Report

Output Format

# Instagram Engagement Benchmark Report
Accounts analyzed: [N] | Posts per account: [30] | Date: [DATE]

## Summary Comparison
| Account | Followers | Avg Likes | Avg Comments | Eng. Rate | Tier Benchmark | Status |
|---------|-----------|-----------|--------------|-----------|----------------|--------|
| @[name] | [N]       | [N]       | [N]          | [X.X%]    | [good/great/low] | ✅/⚠️/❌ |

## Detailed Analysis

### @[handle1]
- Followers: [N] | Tier: [Nano/Micro/Mid/Macro/Mega]
- Avg Likes: [N] | Avg Comments: [N]
- **Engagement Rate: [X.X%]** — [above/at/below] benchmark for this tier
- Posting frequency: [X] posts/week
- Best-performing post: [url] ([N] likes, [N] comments)
- ⚠️ Anomalies: [none / describe if any]
- **Verdict:** [Recommended / Proceed with caution / Do not recommend]

### @[handle2]
[same structure]

## Benchmark Verdict Key
- ✅ Healthy engagement — safe to partner with
- ⚠️ Below benchmark — investigate further before committing budget
- ❌ Suspicious patterns — likely inflated audience, do not recommend

Troubleshooting

Private accounts: Instagram scraper cannot access private profiles. Skip these. Low post count: Accounts with <10 recent posts have unreliable ER stats — flag this. Reels vs. posts: Reels typically get 2-3x more engagement than static posts. If the account posts mostly Reels, adjust expectations.