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 agentrhq/agent-automation-skills --skill find-influencersgit clone --depth 1 https://github.com/agentrhq/agent-automation-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/agentrhq/agent-automation-skills/find-influencers)<a href="https://agentmods.dev/skills/agentrhq/agent-automation-skills/find-influencers"><img src="https://agentmods.dev/badge/skills/agentrhq/agent-automation-skills/find-influencers/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/agentrhq/agent-automation-skills/find-influencers"><img src="https://agentmods.dev/badge/skills/agentrhq/agent-automation-skills/find-influencers.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 | $0.00065 | $0.03145 |
| Opus 5 | $0.00032 | $0.01572 |
| Sonnet 5 | $0.00013 | $0.00629 |
| Haiku 4.5 | $0.00006 | $0.00314 |
Grade A, and why
find-influencers 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Find Influencers
Core contract
Turn the user's request into a confirmed rubric, research with read-only Webcmd commands, qualify only creators who meet every required criterion, and return every evaluated creator in one evidence-backed CSV output. Never follow, subscribe, like, message, post, reply, comment, or contact a creator.
Prerequisites
- Run
node --version; Node.js 20 or newer is required. - Run
webcmd --version. If Webcmd is missing, ask before installing it globally withnpm install -g @agentrhq/webcmd. - Load
webcmd:webcmd-usagebefore live Webcmd work and follow its complete filtered-registry discovery order. - Verify requested commands with
webcmd youtube --helpandwebcmd twitter --help. - Run
webcmd doctor, thenwebcmd youtube whoami -f jsonand/orwebcmd twitter whoami -f json. Use the matching login command and human handoff when authentication is required; rerunwhoamiafter the user reports completion.
Workflow
1. Build the rubric
Extract what the user already supplied and ask only for missing information, one question at a time:
- topic, niche, product, or campaign;
- YouTube, X/Twitter, or both;
- qualified target count;
- numeric and qualitative criteria;
- whether each criterion is required or preferred;
- acceptable evidence and confidence for estimates;
- exclusions and seed accounts.
YouTube-specific inputs
When YouTube is in scope, collect only missing YouTube-specific inputs: subscriber range with optional minimum and maximum; recent-view metric (median recommended, average, minimum on every sampled video, or a confirmed pass-count); recent-view range with optional floor and ceiling; exact eligible-video sample size; eligible formats (long-form, Shorts, livestreams, or a confirmed mixture); optional upload window or posting frequency; optional likes metric (median or average) or like-to-view ratio; optional public business email; and any user-requested faceless or on-camera requirement. Do not apply these requirements to X/Twitter.
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 · 170 lines · 65 tokens per session scan A 46af5a41a6cc
find-influencers is a skill published in the GitHub repository agentrhq/agent-automation-skills (2 stars, last pushed 23d ago), licensed MIT. It adds 65 tokens to every session and 3,145 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 2026-08-31.
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