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 dalroot/agentic-seo --skill influencer-marketinggit clone --depth 1 https://github.com/dalroot/agentic-seoWrote 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/dalroot/agentic-seo/influencer-marketing)<a href="https://agentmods.dev/skills/dalroot/agentic-seo/influencer-marketing"><img src="https://agentmods.dev/badge/skills/dalroot/agentic-seo/influencer-marketing/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/dalroot/agentic-seo/influencer-marketing"><img src="https://agentmods.dev/badge/skills/dalroot/agentic-seo/influencer-marketing.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 | $0.00087 | $0.00867 |
| Opus 5.5 | $0.00035 | $0.00347 |
| Sonnet 5.5 | $0.00017 | $0.00173 |
| Haiku 4.5 | $0.00009 | $0.00087 |
Grade A, and why
influencer-marketing 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 today.
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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Channels: Influencer
Guides influencer marketing strategy for AI/SaaS products. Focus on trust and brand exposure; 2–3x higher conversion and 40%–50% lower CAC than traditional ads. 92% of consumers trust influencers over celebrities.
When invoking: On first use, if helpful, open with 1–2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.
Initial Assessment
Check for project context first: If .claude/project-context.md or .cursor/project-context.md exists, read it for product, audience, and brand voice.
Identify:
- Goal: Brand awareness, trust, or direct conversion
- Platform: Instagram, TikTok, YouTube, LinkedIn
- Target: Nano (1K–10K), micro (10K–100K), mid-tier (100K–1M), macro (1M+)
Influencer vs. Affiliate vs. Creator
| Dimension | Influencer | Affiliate | Creator Program |
|---|---|---|---|
| Goal | Brand exposure, trust | Direct sales | Content co-creation |
| Incentive | Paid, product exchange | Commission | Credits, free use |
| Content | Influencer original | Links sufficient | Must use product |
| Relationship | Short or long-term | Transactional | Long-term |
Influencer Types by Platform
| Platform | Best For | Audience |
|---|---|---|
| Visual, lifestyle | 25–34 | |
| TikTok | Viral, short-form | Under 24 |
| YouTube | Reviews, tutorials | 18–24 |
| B2B, professional | Professionals |
Discovery & Screening
- Platform search: Search by brand/industry terms; check competitors
- Tools: Stormy.ai, Upfluence, Passionfroot
- Form: Application form on site for interested creators
Screening: Engagement rate > follower count; avoid fake followers; content style match.
Cooperation Models
| Model | Use |
|---|---|
| Product exchange | Low cost; testing; requires influencer interest |
| Commission (affiliate) | Lower risk; CPS; hybrid with flat fee common |
| Paid promotion | Direct payment; quick exposure |
| Long-term partnership | Brand ambassador; sustained relationship |
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.
- today First seen · 86 lines · 87 tokens per session scan A 4f01651b4742
influencer-marketing is a skill published in the GitHub repository dalroot/agentic-seo (25 stars, last pushed 6d ago), licensed MIT. It adds 87 tokens to every session and 867 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-10-26.
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