analyzing-competitor-instagram-content-strategy
Analyzes a competitor's Instagram content strategy and performance using apidojo's Instagram scraper on Apify. Triggers when the user asks to: analyze what a competitor posts on Instagram, benchmark a competitor's Instagram engagement, see what content types perform best for a competitor on Instagram, reverse-engineer a competitor's Instagram content calendar, identify content gaps vs. a competitor on Instagram, compare posting frequency or content themes, or understand why a competitor's Instagram is growing. Returns post frequency, content type mix, top-performing posts, hashtag strategy, and engagement benchmarks. Ideal for social media managers, brand strategists, and content marketing teams.
- Installs
- 810
- Last 24h
- +12
- Trending rank
- #8124
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/apidojo-io/apidojo-skills --skill analyzing-competitor-instagram-content-strategy- 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 analyzing-competitor-instagram-content-strategy 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.
Analyzing Competitor Instagram Content Strategy
Reverse-engineers a competitor's Instagram content strategy by analyzing their last 50+ posts. Identifies what content formats, themes, and posting patterns drive their highest engagement.
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: Scrape competitor's recent posts
- [ ] Step 2: Classify content types
- [ ] Step 3: Calculate engagement metrics per content type
- [ ] Step 4: Analyze posting patterns
- [ ] Step 5: (Optional) Compare to your account
- [ ] Step 6: Deliver strategy report
Step 1: Scrape Competitor Profile
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": ["[COMPETITOR_HANDLE]"],
"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 '{"usernames": ["competitor_handle"], "maxItems": 50}'
Step 2: Content Classification
For each post, classify:
content_type:
PRODUCT = post primarily shows product
LIFESTYLE = product in context / aspirational
EDUCATIONAL = tips, how-to, facts (carousel with text)
SOCIAL_PROOF = testimonial, user tag, press feature
ENTERTAINMENT = meme, trending audio, humor
PROMOTIONAL = sale, discount, CTA-heavy
BEHIND_SCENES = team, office, process
Format: IMAGE | VIDEO | CAROUSEL
Step 3: Calculate Metrics
engagement_rate = (likes + comments) / follower_count * 100
per_type_avg_engagement = avg(engagement_rate for all posts of that type)
content_type_share = count(posts of type) / total_posts * 100
Top performing post: highest (likes + comments * 3) — comments weighted higher as active signal.
Posting cadence:
posts_per_week = total_posts / (date_range_days / 7)
best_day = day_of_week with highest avg engagement
best_hour = hour_of_day with highest avg engagement (use post `timestamp`)
Step 4: Edge Cases
- Competitor has very few posts (< 20): Report available data; note low sample size; extend to 180-day window
- Engagement rate << 1%: Account may have bot followers or inactive audience; note this as "audience quality concern"
- All posts are product/promo: This competitor is over-indexed on promotional content — opportunity for content that educates or entertains
- Carousel shows as single image: Some scrapers return first image only; note when
type = CAROUSELfor accurate content type count
Output Format
# Competitor Instagram Strategy: @[COMPETITOR_HANDLE]
Posts analyzed: [N] | Followers: [N] | Overall Eng Rate: [X%] | Date: [DATE]
## Content Mix
| Type | % of Posts | Avg Eng Rate | Best Post Example |
|------|-----------|-------------|------------------|
| Product | [X%] | [X%] | [post excerpt] |
| Lifestyle | [X%] | [X%] | |
| Educational | [X%] | [X%] | |
## Format Distribution
Images: [X%] | Carousels: [X%] | Videos/Reels: [X%]
Best format by engagement: [FORMAT] ([X%] eng rate)
## Top 5 Posts (by Engagement)
| # | Type | Format | Likes | Comments | Eng Rate | Caption Preview |
|---|------|--------|-------|----------|----------|----------------|
## Posting Cadence
Frequency: [X] posts/week | Best day: [Day] | Best hour: [HH:00]
## Key Observations
1. [Pattern observation — e.g. "Carousel educational posts get 2× engagement of product posts"]
2. [Observation]
3. [Opportunity gap]
Troubleshooting
Scraper returns only recent 12 posts: Instagram limits API access to recent posts. For 50-post analysis, run scraper and note actual count returned.
Engagement rate seems wrong: Verify follower_count is current — scraper may return the follower count at time of scrape, which could differ from post-date count for historical posts.
Competitor has very high engagement: Distinguish between genuine engagement and pods/bought engagement — genuine engagement shows variety in commenters; pod engagement shows the same accounts commenting repeatedly.
FAQ
What does the analyzing-competitor-instagram-content-strategy skill do?
Analyzes a competitor's Instagram content strategy and performance using apidojo's Instagram scraper on Apify. Triggers when the user asks to: analyze what a competitor posts on Instagram, benchmark a competitor's Instagram engagement, see what content types perform best for a competitor on Instagram, reverse-engineer a competitor's Instagram content calendar, identify content gaps vs. a competitor on Instagram, compare posting frequency or content themes, or understand why a competitor's Instagram is growing. Returns post frequency, content type mix, top-performing posts, hashtag strategy, and engagement benchmarks. Ideal for social media managers, brand strategists, and content marketing teams.
How do I install analyzing-competitor-instagram-content-strategy?
Run npx skills add https://github.com/apidojo-io/apidojo-skills --skill analyzing-competitor-instagram-content-strategy 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/analyzing-competitor-instagram-content-strategy/ (personal) or .claude/skills/analyzing-competitor-instagram-content-strategy/ (project).
Which AI agents can use analyzing-competitor-instagram-content-strategy?
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 analyzing-competitor-instagram-content-strategy 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 analyzing-competitor-instagram-content-strategy?
It is published by apidojo-io in apidojo-io/apidojo-skills. It has 810 recorded installs.
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