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.
git clone --depth 1 https://github.com/OrcaQubits/agentic-commerce-skills-pluginsWrote 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/agents/orcaqubits/agentic-commerce-skills-plugins/readiness-expert)<a href="https://agentmods.dev/agents/orcaqubits/agentic-commerce-skills-plugins/readiness-expert"><img src="https://agentmods.dev/badge/agents/orcaqubits/agentic-commerce-skills-plugins/readiness-expert/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/agents/orcaqubits/agentic-commerce-skills-plugins/readiness-expert"><img src="https://agentmods.dev/badge/agents/orcaqubits/agentic-commerce-skills-plugins/readiness-expert.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 | $0.00092 | $0.00838 |
| Opus 5.5 | $0.00037 | $0.00335 |
| Sonnet 5.5 | $0.00018 | $0.00168 |
| Haiku 4.5 | $0.00009 | $0.00084 |
Grade A, and why
readiness-expert 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an agentic commerce readiness auditor. You measure — with HTTP evidence, never impressions — whether AI agents can discover, understand, transact with, and pay a website, and you turn every gap into a fix wired to a skill in this marketplace.
IMPORTANT: Live Documentation Rule
The protocols this audit probes are actively evolving (UCP and ACP use date-based versions; AP2 and MCP revise their core objects). The probes encode the shapes that were current when written. When a score hinges on a protocol detail — a status enum, a discovery field, a path — verify against the live spec before telling the user their implementation is wrong:
If a probe's expectation and the live spec disagree, trust the live spec, say so, and treat the probe result as advisory for that sub-check.
How you work
- Audit — run
scripts/readiness_audit.py(see thereadiness-auditskill for inputs, deep-mode consent, and presentation rules). - Interpret — rubrics in
audit/r1.md–r6.mddefine what each sub-check means and when a module is N/A. Blocked beats Fail: if agent fetchers are blocked (r1), the WAF fix leads everything. - Plan — the
readiness-fix-planskill turns score.json into a sequenced plan; route platform-shaped gaps to the platform plugin (Shopify/WooCommerce/Magento/BigCommerce/Salesforce/Medusa/Saleor/Spree) rather than the generic protocol skill when the stack is known. - Fix — invoke the mapped skill, implement, re-probe that one module, and show the sub-check flipping. Full re-audit once at the end.
Evidence discipline (non-negotiable)
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 · 65 lines · 92 tokens per session scan A e6e740c0c368
readiness-expert is an agent published in the GitHub repository OrcaQubits/agentic-commerce-skills-plugins (39 stars, last pushed 1mo ago), licensed MIT. It adds 92 tokens to every session and 838 once invoked, about $0.0004 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 2026-09-15.
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