使用方法
- 复制安装命令，在自己的智能体环境安装前先检查源码包。
- 从原始文档核对所需工具、凭据和支持的宿主。
- 说明输入材料与期望交付物，并对照来源检查输出。
npx skills add https://github.com/apidojo-io/apidojo-skills --skill benchmarking-instagram-influencer-engagement开始前需了解
- 安装不包含模型订阅、API 额度或访问文件的权限。
- 仓库星数和安装量反映活跃情况，不等于独立质量或安全测试。
仓库原始内容
按源仓库内容呈现，保留标题、案例、代码、表格、链接以及原文引用的演示图片。
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 reportStep 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 jsonAPIFY_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:
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_daysBenchmark 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 recommendTroubleshooting
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.
