WCAG specialist focused on criterion-level evidence across perceivable, operable, understandable, and robust behavior, without overstating automated scan results as conformance.
A perspective available to CARBON—not a claim that this tester has reviewed your project. Findings require execution evidence.
Compatibility specialist focused on browser, device, viewport, operating-system, assistive-technology, and support-matrix evidence, with explicit coverage gaps rather than assumed portability.
A perspective available to CARBON—not a claim that this tester has reviewed your project. Findings require execution evidence.
Performance specialist focused on user-visible latency, Core Web Vitals, payload and request cost, caching, API timing, scalability, mobile constraints, memory, and leaks.
A perspective available to CARBON—not a claim that this tester has reviewed your project. Findings require execution evidence.
Content and UX-writing specialist focused on page identity, clear copy, information architecture, credibility, navigation, status communication, readability, and content quality.
A perspective available to CARBON—not a claim that this tester has reviewed your project. Findings require execution evidence.
Forms specialist focused on input contracts, validation, boundaries, state, submission, recovery, data quality, conversion barriers, and accessible interaction.
A perspective available to CARBON—not a claim that this tester has reviewed your project. Findings require execution evidence.
First-impression and conversion specialist focused on value clarity, trust, navigation, calls to action, responsive composition, dead ends, and page credibility.
A perspective available to CARBON—not a claim that this tester has reviewed your project. Findings require execution evidence.
Checkout and payment specialist focused on order accuracy, address and payment input, trust, retry safety, pricing truth, completion, and conversion-blocking failures.
A perspective available to CARBON—not a claim that this tester has reviewed your project. Findings require execution evidence.
AI
Virtual AI tester
Priya
Shopping Cart Tester
Shopping-cart specialist focused on line-item state, quantities, promotions, totals, inventory changes, persistence, accessibility, and safe transition to checkout.
A perspective available to CARBON—not a claim that this tester has reviewed your project. Findings require execution evidence.
AI
Virtual AI tester
Mateo
Pricing Page Tester
Pricing and subscription specialist focused on plan clarity, comparison, currency and locale, hidden conditions, billing cadence, conversion paths, and truthful claims.
A perspective available to CARBON—not a claim that this tester has reviewed your project. Findings require execution evidence.
AI-generated-code specialist focused on logic, boundaries, null and empty states, failure handling, API use, security, privacy, tests, code smells, state, and misleading AI shortcuts.
A perspective available to CARBON—not a claim that this tester has reviewed your project. Findings require execution evidence.
Decide what must be true before deciding how much testing to do.
Jason Arbon CEO, testers.ai
/carbon-confidence-plan
AIJasonAI Code ReviewAIRichardForms
An evaluation suite can be beautifully organized and still answer the
wrong question.
Before generating cases, ask what decision the evidence needs to
support. Are you shipping a new assistant, changing a model, or enabling
an agent to take actions it could not take before?
/carbon-confidence-plan creates a risk-based validation
plan around that scope.
Replace vague confidence
with claims
Consider a support assistant that can issue credits. Answer quality
matters, but so do authorization, amount limits, tool arguments,
duplicate actions, and recovery when the tool response is uncertain.
The plan should identify those boundaries, rank plausible failures by
consequence and uncertainty, and define what evidence would support each
claim. Some checks need exact assertions. Others need repeated sampled
evaluation, calibrated reviewers, or an operational signal.
Tests generated from the implementation alone may share its
assumptions. An independence plan identifies where another evidence
source or review perspective is necessary. That is especially important
for consequential or irreversible behavior.
Ask for a decision-sized
plan
/carbon-confidence-plan assess the proposed credit-issuing assistant; define release claims, unacceptable outcomes, and the evidence needed before enabling writes
The output is a plan and durable risk context, not execution proof.
It should show prerequisites, blocked evidence, high-risk slices, and
the smallest useful investigation order.
Generating cases comes later. The first task is deciding which claims
deserve them.
AI makes lists easy. The value of planning is choosing the evidence
that would actually change your decision.
Install CARBON at testers.ai/carbon for a supported
coding agent such as Claude, Codex, or Cursor.
