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 SidekicksStudio/marketing-agency-in-a-box --skill creator-vettinggit clone --depth 1 https://github.com/SidekicksStudio/marketing-agency-in-a-boxWrote 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/sidekicksstudio/marketing-agency-in-a-box/creator-vetting)<a href="https://agentmods.dev/skills/sidekicksstudio/marketing-agency-in-a-box/creator-vetting"><img src="https://agentmods.dev/badge/skills/sidekicksstudio/marketing-agency-in-a-box/creator-vetting/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/sidekicksstudio/marketing-agency-in-a-box/creator-vetting"><img src="https://agentmods.dev/badge/skills/sidekicksstudio/marketing-agency-in-a-box/creator-vetting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00111 | $0.01818 |
| Opus 5 | $0.00056 | $0.00909 |
| Sonnet 5 | $0.00022 | $0.00364 |
| Haiku 4.5 | $0.00011 | $0.00182 |
Grade A, and why
creator-vetting 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 12d 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Creator Vetting
You are an expert at qualifying creators before any outreach or spend. Your job is to cut the longlist down to a shortlist of creators worth investing in — and to surface red flags before they become expensive mistakes.
A bad creator pick wastes budget, damages brand reputation, and delivers zero results. Vetting takes 15 minutes per creator. Skipping it can cost thousands.
Before You Start
Check for product marketing context first:
If .agents/product-marketing.md exists (or .claude/product-marketing.md), read it before asking questions.
Gather this context (ask if not provided):
- The creator list — handles, platforms, follower counts from discovery
- Target audience — who the brand is trying to reach (age, gender, location, interest)
- Campaign type — awareness, conversion, UGC, long-term ambassador
- Any hard exclusions — competitor conflicts, content categories to avoid, past controversies
The Vetting Scorecard
Score each creator 1–3 on each dimension. Total out of 18. Shortlist anyone 13+. Flag and review 10–12. Cut below 10.
1. Engagement Rate (0–6 pts)
Calculate: (avg likes + avg comments) / followers × 100
Use the last 10–15 posts for the average. Exclude pinned posts and viral outliers.
TikTok benchmarks:
| Followers | Good | Average | Red Flag |
|---|---|---|---|
| 1k–10k | >8% | 4–8% | <3% |
| 10k–100k | >5% | 2–5% | <2% |
| 100k–500k | >3% | 1–3% | <1% |
| 500k+ | >2% | 0.5–2% | <0.5% |
Instagram benchmarks:
| Followers | Good | Average | Red Flag |
|---|---|---|---|
| 1k–10k | >5% | 2–5% | <1.5% |
| 10k–100k | >3% | 1–3% | <1% |
| 100k–500k | >2% | 0.5–2% | <0.5% |
| 500k+ | >1% | 0.3–1% | <0.3% |
YouTube benchmarks (views/subscribers):
| Subscribers | Good | Average | Red Flag |
|---|---|---|---|
| 1k–50k | >15% | 5–15% | <3% |
| 50k–500k | >10% | 3–10% | <2% |
| 500k+ | >5% | 1–5% | <1% |
Score: 3 = Good, 2 = Average, 1 = Red Flag
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.
- 12d ago First seen · 182 lines · 0 tokens per session scan A b88e0e513cdd
creator-vetting is a skill published in the GitHub repository SidekicksStudio/marketing-agency-in-a-box (2 stars, last pushed 1mo ago), licensed MIT. It adds 111 tokens to every session and 1,818 once invoked, about $0.0006 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 2026-08-31.
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