| 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. 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.
npx skills add https://github.com/apify/awesome-skills --skill apify-influencer-brand-collabs
Surface Instagram branded-content partnerships by chaining four Apify Actors against Meta's Ad
Library. Distilled from the production influencer-brand-collabs mini-tool.
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).
@adidas or https://www.instagram.com/adidas/Direction (brand vs creator) is detected empirically. Do not ask.
| # | 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.
// actor: apify/instagram-profile-scraper
{ "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.
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).
// actor: apify/brand-collaboration-scraper
{ "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.
Count how often the target username appears on each side of the results:
creator side → target is the influencer; results are the brandsbrandPartners 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.
Split collab URLs by type:
/reel/... → reel scraper/p/... or /tv/... → post scraper// actor: apify/instagram-post-scraper
{ "username": ["<post urls>"], "resultsLimit": 1, "dataDetailLevel": "basicData" }
// actor: apify/instagram-reel-scraper
{ "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.
Collect unique usernames from the side that is not the target. Then:
// actor: apify/instagram-profile-scraper
{ "usernames": [<unique result-side usernames>] }
Only enrich the side the user actually cares about. The input handle is already known.
After aggregation, surface:
collabs in this run, avg engagement
For *who*-questions, the partner list alone is enough. Metrics only matter for
*which-was-best*-questions.
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.
fbid. Bail early with a clear message._u/),confirm the account actually runs branded content. Meta only indexes ads they've classified as
branded content.
isBusinessAccount.
whole flow on enrichment.
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.
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.
Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.
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Build and distribute Expo development clients locally or via TestFlight
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Take apify/apify-influencer-brand-collabs from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.