| name | apify-influencer-brand-collabs |
| description | Discover Instagram brand–creator partnerships by chaining Apify Actors. Use when the user asks
who collabs with a brand, which brands a creator has done paid posts for, wants to audit an
influencer's branded-content history, or wants to scope a brand's sponsorship roster.
**Triggers:**
- "who collabs with [brand] on Instagram?"
- "what brands has [creator] done sponsored posts for?"
- "find paid partnerships / branded content for [handle]"
- "audit [influencer]'s brand deals"
- "show me [brand]'s influencer roster"
Works in either direction — brand → creators or creator → brands — and detects direction from the
data, so don't ask the user to declare it. Requires Apify MCP tools.
|
| author | Natasha Lekh |
| author_url | https://github.com/natashalekh |
Influencer–Brand Collaborations
Surface Instagram branded-content partnerships by chaining four Apify Actors against Meta's Ad
Library. Distilled from the production influencer-brand-collabs mini-tool.
When to use
- "Who has Nike paid to promote them this quarter?"
- "What brands does @bellahadid do sponsored posts for?"
- Auditing an account's branded-content history
- Building a competitor's influencer roster
Don't use for: organic mentions or tags (use a hashtag/mentions scraper), TikTok or YouTube
collabs (different platforms), generic competitor ads (query Meta Ad Library directly).
Inputs to gather
- Instagram handle or URL —
@adidas or https://www.instagram.com/adidas/
- Lookback window — days; default 90
- Enrichment toggles (each adds cost + time):
- Content insights — likes, comments, views per collab
- Profile enrichment — followers, bio, verified status of the other side
Direction (brand vs creator) is detected empirically. Do not ask.
The pipeline
| # | Actor | Purpose | Required |
|---|
| 1 | apify/instagram-profile-scraper | Resolve the target's Facebook fbid | ✓ |
| 2 | apify/brand-collaboration-scraper | Pull branded-content posts from Meta's Ad Library | ✓ |
| 3 | apify/instagram-post-scraper + apify/instagram-reel-scraper | Engagement metrics | optional |
| 4 | apify/instagram-profile-scraper (again) | Enrich the result-side partners | optional |
Call each via mcp__claude_ai_Apify__call-actor. Use mcp__claude_ai_Apify__fetch-actor-details
first if you've never run one of these and want the exact input schema.
Step 1 — Resolve the target
{ "usernames": ["adidas"] }
Grab fbid from the first item. No fbid → can't query Ad Library → stop and tell the user.
Most common cause: private account.
Step 2 — Build the Meta Ad Library URL
https://www.facebook.com/ads/library/branded_content/?id={fbid}&query={username}&target=instagram&start_date={YYYY-MM-DD}&end_date={YYYY-MM-DD}
Date range = the lookback window (default 90 days, ending today).
Step 3 — Fetch collaborations
{ "startUrls": ["<ad library url>"], "resultsLimit": 10 }
Schema is fixed: every item has creator (always the influencer side) and brandPartners[0]
(always the brand side). Do not try to infer direction from these fields.
Step 4 — Detect direction empirically
Count how often the target username appears on each side of the results:
- target appears more on
creator side → target is the influencer; results are the brands
- target appears more on
brandPartners side → target is the brand; results are the creators
⚠️ Do not use isBusinessAccount to infer this. It's unreliable — e.g. @fifaworldcup is a
business account but appears as the creator of its own branded content.
Step 5 — (optional) Content metrics
Split collab URLs by type:
/reel/... → reel scraper/p/... or /tv/... → post scraper
{ "username": ["<post urls>"], "resultsLimit": 1, "dataDetailLevel": "basicData" }
{ "username": ["<reel urls>"], "resultsLimit": 1 }
Match back to collabs via shortcode in the URL: /(p|reel|tv)/([A-Za-z0-9_-]+).
Engagement formula: likesCount + commentsCount + (videoViewCount ?? videoPlayCount ?? 0).
Run the two scrapers in parallel — they're independent.
Step 6 — (optional) Enrich the result side
Collect unique usernames from the side that is not the target. Then:
{ "usernames": [<unique result-side usernames>] }
Only enrich the side the user actually cares about. The input handle is already known.
What to present
After aggregation, surface:
- Headline counts: total collabs, unique partners, total engagement (if metrics enriched)
- Top 5 collabs by engagement — only meaningful when content insights were toggled on
- Content-type mix: Post vs Reel; Reels usually dominate engagement
- Weekly timeline across the date range — spikes reveal campaign launches
- Per-partner card (when profiles enriched): handle, full name, followers, verified, category,
collabs in this run, avg engagement
For who-questions, the partner list alone is enough. Metrics only matter for
which-was-best-questions.
URL parsing
Strip Instagram's _u/ and _n/ deep-link prefixes before extracting the handle:
/instagram\.com\/(?:_u\/|_n\/)?([A-Za-z0-9_.]+)/i
These slugs are not usernames — skip them:
explore, reels, stories, direct, accounts, about, p, reel, tv, tags,
locations, _u, _n.
Pitfalls
- Target is private → profile scraper returns no
fbid. Bail early with a clear message.
- No results → try in order: widen the date range, double-check the handle (strip
_u/),
confirm the account actually runs branded content. Meta only indexes ads they've classified as
branded content.
- Engagement is all zeros → user skipped content enrichment. Offer to re-run with it on.
- Direction looks wrong in the output → re-check the empirical count. Don't trust
isBusinessAccount.
- Brand collabs with no metrics are still answer-shaped for who questions — don't gate the
whole flow on enrichment.
Cost & time
Full 4-actor run: ~3–5 minutes, a few cents of Apify compute. Order of magnitude:
| Enrichment | Actors run | Approx time |
|---|
| None | 2 | 1–2 min |
| + Content | 3–4 | 2–4 min |
| + Profiles | +1 | +30–60 s |
If the user just needs a partner list, skip both toggles.
Reference implementation
Production route this skill was distilled from:
mini-tools-main/src/app/api/tools/influencer-brand-collabs/route.ts — full transformation logic,
error handling, and slimmed display shapes for each scraper's output.
