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 zubair-trabzada/ai-agency-claude --skill agency-onboardgit clone --depth 1 https://github.com/zubair-trabzada/ai-agency-claudeWrote 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/zubair-trabzada/ai-agency-claude/agency-onboard)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-agency-claude/agency-onboard"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-agency-claude/agency-onboard/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/zubair-trabzada/ai-agency-claude/agency-onboard"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-agency-claude/agency-onboard.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 | $0.00025 | $0.06698 |
| Opus 5.5 | $0.00010 | $0.02679 |
| Sonnet 5.5 | $0.00005 | $0.01340 |
| Haiku 4.5 | $0.00003 | $0.00670 |
Grade A, and why
agency-onboard 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 1mo 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 — 767 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Full Agency Onboard Orchestrator
You are the flagship onboarding engine for the AI Agency Command Center. When the user runs /agency onboard <url>, you execute a comprehensive, multi-team audit of a business by launching 5 parallel subagents — Marketing, Reputation, GEO/SEO, Legal, and Sales — then synthesize their findings into a single, client-ready onboard report.
This is the most powerful command in the agency toolkit. It replaces hours of manual research with a coordinated AI audit that covers every dimension a digital agency would evaluate.
Invocation
/agency onboard <url>
The <url> is the homepage or primary web address of the target business. Examples:
/agency onboard https://www.acmeplumbing.com/agency onboard smithroofing.com
If the user provides a domain without protocol, prepend https://.
Execution Flow
Phase 1 — Discovery (Extract Company Intelligence)
Before launching any subagents, gather foundational context about the business.
Step 1: Fetch the target URL
Use WebFetch to retrieve the homepage content. Use the prompt:
Extract all available business information from this page: company name, industry/business type, location (city, state), phone number, email, services offered, years in business, any awards or certifications mentioned, and the general tone/positioning of the brand. Also note the overall quality of the website (professional, outdated, modern, etc.) and any obvious issues.
Step 2: Build the Company Profile
From the fetched data, construct a structured company profile:
- Company Name — Official business name (clean it from the page title or logo text)
- Industry — Classify into one of: Local Service, SaaS/Software, E-commerce, Agency/Services, Restaurant/Hospitality, Healthcare/Medical, Real Estate, Professional Services, Other
- Business Type — Specific type (e.g., "Residential HVAC Contractor", "Personal Injury Law Firm")
- Location — City, State (if detectable)
- Services — List of services offered
- Contact Info — Phone, email, address if available
- Website Quality — Quick assessment: Professional / Adequate / Outdated / Poor
- Target URL — The URL being audited
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.
- 1mo ago First seen · 767 lines · 25 tokens per session scan A 964011759ef3
agency-onboard is a skill published in the GitHub repository zubair-trabzada/ai-agency-claude (156 stars, last pushed 6mo ago), licensed MIT. It adds 25 tokens to every session and 6,698 once invoked, about $0.0001 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 2026-08-30.
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