Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add apify/awesome-skills --skill apify-influencer-brand-collabsgit clone --depth 1 https://github.com/apify/awesome-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/apify/awesome-skills/apify-influencer-brand-collabs)<a href="https://agentmods.dev/skills/apify/awesome-skills/apify-influencer-brand-collabs"><img src="https://agentmods.dev/badge/skills/apify/awesome-skills/apify-influencer-brand-collabs/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/apify/awesome-skills/apify-influencer-brand-collabs"><img src="https://agentmods.dev/badge/skills/apify/awesome-skills/apify-influencer-brand-collabs.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 16 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00168 | $0.01829 |
| Opus 5 | $0.00084 | $0.00915 |
| Sonnet 5 | $0.00034 | $0.00366 |
| Haiku 4.5 | $0.00017 | $0.00183 |
Grade A, and why
apify-influencer-brand-collabs scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 —
@adidasorhttps://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
// 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.
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}
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 184 lines · 168 tokens per session scan A cc03cc121ba0
apify-influencer-brand-collabs is a skill published in the GitHub repository apify/awesome-skills (251 stars, last pushed yesterday), licensed Apache-2.0. It adds 168 tokens to every session and 1,829 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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