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 superamped/ai-marketing-skills --skill influencer-discoverygit clone --depth 1 https://github.com/superamped/ai-marketing-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/superamped/ai-marketing-skills/influencer-discovery)<a href="https://agentmods.dev/skills/superamped/ai-marketing-skills/influencer-discovery"><img src="https://agentmods.dev/badge/skills/superamped/ai-marketing-skills/influencer-discovery/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/superamped/ai-marketing-skills/influencer-discovery"><img src="https://agentmods.dev/badge/skills/superamped/ai-marketing-skills/influencer-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00057 | $0.02060 |
| Opus 5 | $0.00028 | $0.01030 |
| Sonnet 5 | $0.00011 | $0.00412 |
| Haiku 4.5 | $0.00006 | $0.00206 |
Grade A, and why
influencer-discovery 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 — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Influencer Discovery
Usage
Use when finding influencers to partner with for sponsorships, affiliate deals, or co-marketing. Also useful for identifying thought leaders your target audience already follows, building a media list for outreach or PR, or understanding who shapes opinion in your niche before creating content.
Process
Step 1: Gather Inputs
Ask the user for:
- Industry/niche — the topic domain (e.g., "B2B SaaS marketing", "personal finance for millennials", "indie game development")
- Target audience — who follows these influencers, framed as the buyer (e.g., "SaaS founders", "freelance developers", "e-commerce store owners")
- Platform focus (optional) — prioritize specific platforms (YouTube, X/Twitter, Newsletters, Podcasts, Instagram). Default: search all.
- Minimum follower count (optional) — exclude influencers below a threshold. Default: no minimum (micro-influencers included).
Extract from inputs:
- Niche keywords: The 3-5 topic terms that define the industry
- Audience profile: Who the target audience is (role, stage, problem domain)
- Platform priorities: Which platforms to weight more heavily
Step 2: Generate Search Queries
Generate 10 search queries to surface influencers across platform types and discovery angles:
"Top influencers" list queries:
- "top [niche] influencers [current year]"
- "best [niche] creators to follow"
- "top [audience] thought leaders"
Platform-specific queries:
- "top [niche] youtube channels"
- "[niche] youtube [subscribers OR creators]"
- "best [niche] newsletter substack"
- "top [niche] podcast"
- "[niche] twitter thought leaders"
Cross-platform discovery:
- "[niche] conference speakers [current year]"
- "who sponsors [niche] newsletters"
- "[niche] podcast guest list"
Step 3: Search YouTube
Search YouTube for top channels in the niche:
- Use queries: "top [niche] YouTube channels", "[audience] YouTube", "[niche keyword] tutorial/advice channel"
- For each channel found, collect: Channel name, URL, subscriber count, content focus
- Aim for 20-30 YouTube channels across macro (500k+), mid-tier (50k-500k), and micro (<50k) tiers
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 · 213 lines · 57 tokens per session scan A 87d3fe780bfa
influencer-discovery is a skill published in the GitHub repository superamped/ai-marketing-skills (67 stars, last pushed 25d ago), licensed MIT. It adds 57 tokens to every session and 2,060 once invoked, about $0.0003 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.
Other skills, from other repositories
newsjack-detector
Monitor current news and reaction signals, then decide which are credible newsjacking opportunities for a client. Uses the local monitoring engine for evidence, but the skill owns PR judgment, brand safety, standing, decay, angle fit, and handoff.
crisis-holding
Draft crisis holding statements, journalist Q&A posture, and what-not-to-say guidance from confirmed incident facts, with a hard legal-counsel gate. Builds each statement through proven crisis-comms frameworks (holding-statement anatomy, SCCT, CAP order, the legitimate non-answer, bridge/flag/block).
voice-extractor
Capture a user's real writing voice from 5-20 prior samples, store a local voice.yaml fingerprint, and enforce it on newsjack drafts so AI tells disappear. Measures voice with named stylometry lenses (Burrows's Delta function-word vector, MATTR lexical diversity, sentence-length burstiness, Biber Dimension-1 register…
find-journalists
Build, refine, dedupe, and enrich small fit-checked journalist lists for newsjack campaigns. Uses the newsjack CLI (preferred) or the medialyst MCP for news search and journalist enrichment, and falls back to a best-effort local mode with no verified contacts; the agent owns how returned data is organized.
fact-check
Extract factual claims from PR copy, verify each claim independently, attach concrete citations, and warn when certainty is low. Runs each claim through proven newsroom verification methods (lateral reading, source-tier climbing, provenance pillars, triangulation, calibrated rating) and puts the burden of proof on the…
pr-calendar
Turn the Medialyst PR calendar feed into a brand-specific, lead-time-aware plan. Pull source-backed upcoming moments, keep only the few a brand has real standing to own, schedule backward from the event date, screen out tone-deaf hooks, and hand selected moments to angle and journalist prep ahead of time. The planned…