benchmarking-instagram-influencer-engagement
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.
- Installs
- 767
- Last 24h
- +9
- Trending rank
- #9152
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/apidojo-io/apidojo-skills --skill benchmarking-instagram-influencer-engagement- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads benchmarking-instagram-influencer-engagement automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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_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: 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):
# 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_TOKENmust be set in environment or.envfile.
If Apify MCP is available:
Tool: apify:run-actor
Actor: "apidojo~instagram-scraper"
Input:
{
"usernames": ["[handle1]", "[handle2]", "[handle3]"],
"maxItems": 30
}
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 '{
"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.
FAQ
What does the benchmarking-instagram-influencer-engagement skill do?
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.
How do I install benchmarking-instagram-influencer-engagement?
Run npx skills add https://github.com/apidojo-io/apidojo-skills --skill benchmarking-instagram-influencer-engagement in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/benchmarking-instagram-influencer-engagement/ (personal) or .claude/skills/benchmarking-instagram-influencer-engagement/ (project).
Which AI agents can use benchmarking-instagram-influencer-engagement?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is benchmarking-instagram-influencer-engagement safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains benchmarking-instagram-influencer-engagement?
It is published by apidojo-io in apidojo-io/apidojo-skills. It has 767 recorded installs.
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