aep-design-lens

name	aep-design-lens
description	Reviews product UI with theory-grounded guidelines and a 0–4 heuristic severity check. Use for design, usability, heuristic, or accessibility review; not taste capture (/aep-calibrate) or object IA (/aep-model).

Design Lens

Ground a product's UI/UX in established HCI and design theory instead of gut feel. Point it at a product (an idea, a spec, or a running UI); it works out which theoretical lenses apply, then produces concrete design suggestions, a design guideline to build against, and a severity-scored health-check table that audits whether the current design meets human expectations.

"Overview first, zoom and filter, then details-on-demand." — Ben Shneiderman, "The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations" (1996)

Two roles. Invoke it directly to generate a guideline for a new product or run a health-check on an existing one; or, as a library, other skills read references/theory-catalog.md to pull the right lens for a design decision (e.g. /aep-model reaching for the information-seeking and graphical-perception lenses when shaping a data-heavy screen).

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When to Use — and When NOT To

Reach for this skill to produce a design guideline before agents build, to run a usability / heuristic health-check on a drafted or running UI, or when a design decision would benefit from the relevant established theory (which chart encoding, how to structure exploration, how to make an agent's actions legible).

Its boundary against neighboring skills — stated once, here:

• Taste decisions (palette, voice, journey feel) belong to /aep-calibrate. This skill supplies the theory a calibration leans on; it does not decide taste.
• Object / screen IA (which objects, what fields, which screens) belongs to /aep-model.
• Factual correctness of artifacts belongs to /aep-validate; the heuristic health-check is its advisory design-quality complement.
• It does not replace real user testing — heuristics predict where users will struggle; they do not observe actual users. Treat the health-check as evidence, not proof.

A backend-only surface with no human-perceived UI is out of scope — but a CLI that wraps an agent still benefits from family E (Gulf of Envisioning), and any terminal output is a human-perceived UI for family G (color-alone signals, contrast).

Output goes to the conversation by default, or to a standalone markdown doc when the user asks to save it — it never writes to product-context.yaml or any AEP schema file. This is an advisory leaf skill that adds no taxonomy.

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The Method

Product-agnostic, because it classifies tasks and data, not a product type. Full step detail, the lens-selection rules, the worked examples, the live-UI evidence-gathering protocol, and the output templates are in references/method-and-templates.md.

Two depths. A quick check scores only the Baseline Ten — a distilled cross-family checklist (method-and-templates.md#the-baseline-ten-quick-check) — and answers "is this OK?" in minutes; default to it for a lightweight question ("is this good UX?"). A deep audit runs all seven steps below; default to it for a guideline or review request. Escalation rule: any major/catastrophe finding (severity 3–4) upgrades a quick check to a deep audit, so speed never trades away rigor.

1. Characterize — what is the product, who uses it, what are they trying to do? If a running UI or spec exists, point to it.
2. Abstract — classify the primary user tasks (monitor / compare / look-up / diagnose / explore / decide / create / reconstruct-provenance / converse) and the data/content type. (Munzner's domain → abstraction move.)
3. Select lenses — pick the applicable theory families from the catalog based on the task+data profile; families A, B, F, G always fire, C/D/E on triggers (selection rules in the reference). Note why each family fired.
4. Synthesize — per selected lens, turn its Apply line into ≥1 concrete, product-specific suggestion, each tagged (→ lens id) back to its theory. Done when every selected lens carries at least one traced suggestion.
5. Emit a design guideline — prescriptive, grouped by family: "the product should…".
6. Emit a health-check table — one row per checkable expectation, each traced to a theory and carrying a Nielsen 0–4 severity, producing a scored "meets human expectations?" audit.
7. (Opt-in) persist — on request, write the report to a standalone markdown file (default docs/design-review/<scope>-<date>.md).

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The Theory Catalog

Grouped into families so lenses can be selected per product. Full entries — each with core question · when it applies · how to apply · what to check · source — are in references/theory-catalog.md, which is append-only and extensible (it documents how to add a theory).

Family	Lenses (summary)
A. Usability & interaction heuristics	Nielsen's 10 · Shneiderman's Eight Golden Rules · Norman's principles + Gulfs + Seven Stages · Jakob's / Tesler's / Postel's laws
B. Cognitive & perceptual laws	Cognitive Load · Miller's · Hick's · Fitts's · Doherty Threshold · Gestalt · preattentive attributes · Aesthetic-Usability · memory/motivation biases
C. Information seeking & navigation	Shneiderman's Visual Information-Seeking Mantra · Information Foraging (scent) · Focus+Context / multiscale trio · IA / wayfinding
D. Data visualization & encoding	Munzner's Nested Model + task typology · Graphical Perception · Grammar of Graphics / Bertin · Heer & Shneiderman · Tufte
E. Human-AI & agent-specific UX	Gulf of Envisioning · Microsoft's 18 HAI Guidelines · Google PAIR · trust calibration · observability · steerability/reversibility · Grice's maxims · CASA
F. Evaluation & process methods	Heuristic Evaluation · Cognitive Walkthrough · Nielsen 0–4 severity scale · GOMS/KLM · Norman's stages-of-action as a lens
G. Accessibility & inclusive design	WCAG 2.2 POUR · keyboard & focus · contrast & legibility (never color alone) · inclusive design (permanent/temporary/situational impairments)

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How It Fits AEP

A design-lens pass feeds forward; control then returns to the calling context.

• Guideline for a new UI → /aep-model turns it into object/screen structure (drawing families C and D), and /aep-calibrate ux-flow / visual-design captures the human's taste decision (drawing A, B, C, E) — the report seeds the brief and pre-audits the flow before the human calibrates.
• Health-check on a built UI → route findings through /aep-reflect (a "works vs right" gap is a calibration or refinement item), or fix directly if it's a clear defect. /aep-validate points here for its advisory design-quality pass (family F).
• Recurring audit → pair with /loop to re-run the health-check each layer.

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Why it's shaped this way

A product-agnostic task+data method (not a product-type lookup), a leaf skill beside /aep-model and /aep-calibrate rather than inside them, hybrid output, Nielsen 0–4 severity, two depths, and always-on accessibility — the reasoning is recorded in docs/decisions/design-lens-rationale.md.