OrcaQubits Agentic Commerce Readiness Audit
You score a live site's readiness for AI-agent commerce using the executable probes in this plugin, then turn every gap into a concrete fix wired to the skills in this marketplace.
Prerequisites
Python 3.8+ on PATH (python3 or python). The probes are stdlib-only
— no pip installs. If Python is missing, tell the user how to install it for
their platform and stop; don't degrade to guessing.
Inputs
Ask for these if not provided:
- URL — required.
https://shop.example.comor a bare host. - Deep mode — opt-in. Default probes send GET/OPTIONS (plus MCP
tools/list, a pure read).--deepadds POST probes: a demo-SKU checkout create against ACP and UCP, NLWeb/ask, and a delegate_payment validation call. These are discovery-shaped, but on a live merchant a created session can trigger inventory holds or abandoned-cart emails — ask before enabling deep mode, and prefer a staging host. - Scope — optional:
all(default) or module idsr1,r2,r4.
Process
1. Run the audit
python "${CLAUDE_PLUGIN_ROOT}/scripts/readiness_audit.py" <URL> --out report/
# deep, with user consent:
python "${CLAUDE_PLUGIN_ROOT}/scripts/readiness_audit.py" <URL> --deep --out report/
Outside Claude Code — or on a converted platform bundle where
${CLAUDE_PLUGIN_ROOT} is not set — substitute the path to the
commerce-readiness plugin directory; the scripts have no other
dependency on the harness.
Emit one progress line before running ("Probing — 6 modules…") and one after ("Scored N/W — writing report/READINESS.md"). The runner executes all modules in parallel; a full shallow run takes under a minute.
For a single module, run its probe directly:
python "${CLAUDE_PLUGIN_ROOT}/scripts/probe_transact.py" <URL> --json
2. Interpret — with the rubrics, not your impressions
Read report/score.json. For any module you need to explain, read its
rubric in audit/r{N}.md — the rubric documents what each sub-check means,
its N/A condition, and the fix path. The scripts are the source of truth
for weights; the rubrics for meaning.
Evidence rules (binding):
- Scores come from HTTP responses only. Never upgrade a Fail because the user says the feature exists, the platform "should" provide it, or a repo file suggests it — a file in a repo is not a URL that serves.
- A probe that couldn't complete has already Failed its sub-check with the reason recorded. Surface the reason; offer a re-run, don't hand-wave.
- If r1 found agent fetchers blocked, r2–r5 report Blocked — lead with the WAF fix and do not present the other modules as independent problems.
- Never fabricate or round up a score, and never claim a Pass means agents will buy — the audit measures capability, not adoption.
3. Present
Show the user, in this order:
- Headline — score, stage, and the single most valuable next action. Two sentences; no endpoints or file paths here.
- Action plan — from the report, ordered: r1 blocker first if present,
then by
priority = impact × (6 − complexity). - Gap → skill map — the report's table. For each gap the user wants to
close, name the exact skill (e.g.
/acp-agentic-commerce:acp-setup) and offer to invoke it. Every fixing skill fetches the live spec before writing code, so the fix is always against the current protocol version.
The full findings table stays in report/READINESS.md; don't paste it into
chat unless asked.
4. Fix (only when asked)
When the user picks a gap, invoke the mapped skill and implement the fix in their codebase. After each fix, re-run only that module's probe to confirm the sub-check flipped:
python "${CLAUDE_PLUGIN_ROOT}/scripts/probe_discovery.py" <URL> --json
One fix, one re-probe, one confirmation. Don't re-run the whole audit per fix; run it once at the end to show the before/after score.
Scoring model (for reference)
Pass ≥ 85% of module weight · Partial ≥ 30% · Fail < 30% · Blocked when r1
gates it · N/A per the rubric's condition. Details: audit/README.md.