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AI Design Blueprint Doctrine

by com.aidesignblueprint

Validate Experience Design

design.validate

Pro/Teams — first-pass surface-craft review of a FRONTEND artefact (component, screen, or flow) against the 8 laws of the Experience Design Blueprint. The surface-craft companion to architect.validate: where architect.validate scores agentic ARCHITECTURE against the 10 agentic principles, design.validate scores the PERCEPTIBLE SURFACE — what the user sees, taps, scans, and remembers (Jakob's familiarity, Hick's choice load, Fitts's targets + the accessibility floor, Miller's working-memory budget, Aesthetic-Usability, Peak-End, Tesler's irreducible complexity, the Mental-Model gap). ON CLIENT TIMEOUT — DO NOT RETRY. Long-running LLM call (~60-180s at high reasoning effort, single-pass). The server mints a run_id, emits it in the FIRST progress event at t=0s (before the LLM call), and persists the run — so on a client timeout, capture that run_id and call me.validation_history(run_id='') to fetch the persisted result instead of retrying (a retry re-runs the full 60-180s call). Runs appear in your validation-history dashboard tagged as the 'surface' dimension, distinct from the 'architecture' and 'spec' runs; pass repository to group them per project. Pass private_session=true to skip the stored run (persistence + recovery disabled); operational security + cost logs are still kept. v1 is single-pass: no certification or consensus mode yet (those stay architect.validate-only). Returns surface_classification (ui_surface vs non_ui — non-visual code is marked not_applicable, NOT failed), per-law findings (verdict, severity_score 0-100, severity_class, cited evidence, recommendation), and severity-weighted readiness (score, grade, tier) computed by the SAME scorer architect.validate uses, so all three lenses grade on one rubric. ACCESSIBILITY IS THE FLOOR: a breach of the Fitts's-Law floor (interactive target below the WCAG 2.2 24×24 minimum, missing focus visibility, an unreachable destructive confirmation) is a production_blocker, not polish. WHEN TO CALL: the user wants a craft/UX/accessibility review or a readiness grade on a frontend artefact they just built or changed. WHEN NOT TO CALL: non-visual code (backend, config, type aliases) returns tier=not_applicable — submit the actual UI surface instead. INPUTS: send the FULL artefact source verbatim as implementation_context (no truncation, no '…' placeholders — they are read as literal code). Auth: Bearer , Pro/Teams plan. UK/EU residency; transient OpenAI processing (no-training); prompt-injection text inside the artefact is treated as inert untrusted data. TYPED FAILURES: same as architect.validate (timed_out, rate_limited, dependency_unavailable, schema_mismatch — each carries retryable + next_action); the services raise the identical typed envelopes on this lens. CALIBRATION DISCLOSURE: the scoring prompt is a v1 first-cut mirroring the architect's contract structure; its score calibration is not yet tuned against a corpus of real runs the way architect.validate was. Treat the grade as directional craft signal, not a certified verdict. DOCTRINE: the eight laws — each law's evidence, craft-surface application, anti-patterns, and the validator questions this tool scores against — live in the experience-design-blueprint skill and docs/business/EXPERIENCE_DESIGN_BLUEPRINT.md (the surface-craft companion to the architect-validation-orchestration skill that orchestrates the agentic validators).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskNoWhat this surface is for (e.g. 'the closed-beta apply form'). Adds evaluation context.
filesNoFile paths relevant to the artefact, for context.
goalsNoSpecific craft/UX goals to weight (e.g. 'WCAG 2.2 AA', 'one primary action per screen').
repositoryNoProject/repository key. Groups this run with prior design.validate runs on the same project in your validation-history dashboard (the same grouping architect.validate uses), under the 'surface' dimension.
session_idNoOptional Governed Session to attach this run to (GEP-M2). Must reference a session YOU own (list via me.sessions; sessions are created in the web app at /app/sessions) — foreign ids are refused before any model call. The run then appears on the session's timeline alongside the other lenses. With private_session=true no run is stored so nothing attaches, but the ownership check still runs FIRST: a session id you don't own fails the call either way.
private_sessionNoSet true to disable persistence AND run_id recovery for this call (a private one-shot that does not appear in the dashboard). Default false.
implementation_contextYesThe frontend artefact under review. SEND FULL SOURCE VERBATIM — the reviewer cites specific elements, values, and structure; any compression destroys evidence and produces findings on code that isn't there. Do NOT replace markup/styles with '…'; do NOT condense multi-line JSX/CSS. If large, split into MULTIPLE calls scoped by component — never truncate one call.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses beyond annotations: long-running LLM call (~60-180s), single-pass, persistent runs with run_id, client timeout handling, private_session option to disable persistence, typed failures (timed_out, etc.), and calibration disclosure. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

While the description is lengthy, each sentence serves a purpose and adds value. The structure is logical (purpose, technical details, usage, limitations), but could benefit from more explicit headings or bullet points for easier parsing by an AI agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers all necessary aspects: purpose, scope, limitations (v1 single-pass, no certification), failure modes, timeout handling, privacy, accessibility floor, calibration disclosure, and references to external documentation. Ties well to sibling tools and provides a complete picture for agent decision-making.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Adds rich context beyond the schema's 100% coverage: implementation_context must be full source verbatim, repository groups under 'surface' dimension, session_id ownership check even with private_session, and private_session disables persistence but still performs ownership validation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it reviews frontend artifacts against the 8 laws of the Experience Design Blueprint, explicitly distinguishing it from architect.validate (which scores architecture). It also specifies when not to call (non-visual code returns not_applicable).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-call (user wants craft/UX/accessibility review) and when-not-to-call (non-visual code). Offers alternatives like architect.validate and spec.validate, and includes specific advice for client timeout (use validation_history instead of retrying).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.6/5.0
Disambiguation5/5

Each tool targets a clearly distinct purpose. For example, architect.validate vs architect.validate_consensus differ in single-shot vs consensus; handoffs.agency, handoffs.operator, and handoffs.partnership are separated by engagement type. No significant overlap.

Naming Consistency4/5

Tools follow a consistent dot-notation grouping (architect.*, clusters.*, examples.*, guides.*, handoffs.*, me.*, principles.*, signals.*, team.*) with predictable verbs (validate, list, get, search, add, etc.). Minor deviation: some underscore within names (e.g., me.add_evidence) but overall pattern holds.

Tool Count4/5

24 tools is on the higher side but justified given the broad domain spanning validation, certification, learning, handoffs, and feedback. Each tool has a clear role, and the count reflects the platform's comprehensive scope without feeling bloated.

Completeness5/5

The tool surface covers the full workflow (validate → consensus → certify), discovery (principles, clusters, examples, guides), personal progress (learning path, coaching, evidence), handoffs (support, partnership, agency), feedback, and team summaries. No obvious gaps for the stated purpose.

Resources