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 adologyai/content-intelligence-plugin --skill influencer-vettinggit clone --depth 1 https://github.com/adologyai/content-intelligence-pluginWrote 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/adologyai/content-intelligence-plugin/influencer-vetting)<a href="https://agentmods.dev/skills/adologyai/content-intelligence-plugin/influencer-vetting"><img src="https://agentmods.dev/badge/skills/adologyai/content-intelligence-plugin/influencer-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/adologyai/content-intelligence-plugin/influencer-vetting"><img src="https://agentmods.dev/badge/skills/adologyai/content-intelligence-plugin/influencer-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 | $0.00207 | $0.07582 |
| Opus 5 | $0.00103 | $0.03791 |
| Sonnet 5 | $0.00041 | $0.01516 |
| Haiku 4.5 | $0.00021 | $0.00758 |
Grade A, and why
influencer-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 10d 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 — 628 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Influencer Vetting & Selection
What This Skill Does
Takes a campaign brief from the user, sources or evaluates creator candidates, scores them on observable content signals, and produces ranked scorecards with adapted briefs for top picks. The scoring is transparent — the user sees every weight and every piece of evidence that drove each score.
The core insight: most influencer vetting is either pure vibes or pure metrics. This skill does neither. It reads a creator's actual content — the way they tell stories, the hooks they use, the emotional register they operate in, how they integrate products, what their audience conversations look like — and assesses fit against the specific campaign, not against generic benchmarks.
What You Can and Cannot Assess
This is the foundation. Be honest about it throughout.
What content intelligence shows you:
- How a creator communicates — their hooks, narrative style, emotional tone, production approach, storytelling patterns
- What topics and themes they consistently cover
- How they integrate products and brands into content (naturally vs. forced)
- Creative range — do they have one trick or many?
- Posting consistency and content volume
- Engagement lift against the creator's own baseline as a proxy for audience quality (not audience identity)
- What their audience says back in comments — the texture of the community
- How their content compares to the broader landscape in their category
What you cannot assess from content alone:
- True audience demographics (age, gender, location, income)
- Fake follower percentages or bot activity
- Conversion rates from past partnerships
- Pricing and rate cards
- Professionalism, responsiveness, contract reliability
- Whether high engagement translates to purchase behavior
What this means for output: Every scorecard includes a "Verify Externally" section that flags exactly what the user needs to check through other means — audience quality tools (HypeAuditor, etc.), pricing negotiations, reference checks with past brand partners. Don't pretend the analysis is complete. It's the content intelligence layer — the deepest, hardest-to-get layer — but it's one layer.
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.
- 10d ago First seen · 628 lines · 207 tokens per session scan A 810264362866
influencer-vetting is a skill published in the GitHub repository adologyai/content-intelligence-plugin (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 207 tokens to every session and 7,582 once invoked, about $0.0010 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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