component‑consulting
Skill · Frontend component consulting · risk S0 · consumes DESIGN.md

The question isn't which component. It's which component serves the message.

Anyone can paste a flashy component. The unsolved part — the part that makes an AI-built frontend actually work — is binding a specific message and specific design tokens to a specific component, and placing it where the argument needs it.

Buy, don't build One signature moment 5-axis fit rubric 15 galleries Reject the average-AI default
Read the SKILL.md → built by the skill it documents ↓
The inversion browse galleries, then justify derive the job from the message, then shop
component-consulting is a consulting hook, not a generator. Given a site's goal and its DESIGN.md tokens, it decides which open-source gallery component fills each job-slot, how to remap it to the tokens, and where to place it — so the page is message-coherent, token-true, and lands in the distinctive band: not bland-safe, not average-AI, not flashy-for-its-own-sake.
01

Why AI frontends look right and behave wrong

They are pretty at a glance and fall apart on inspection. The fix isn't more components — it's a way to choose between them.

Average-AI frontend
  • Buttons that look interactive but do nothing
  • A component chosen because it looked cool, not because it fits
  • Everything shredded into same-size cards (card-soup)
  • Inter + an even 3-card grid + a purple gradient — the default
  • Five loud moments competing; none wins
Message-fit frontend
  • Every interactive element names its real states
  • Every component passes a one-sentence "why it's here" test
  • Hierarchy carried by tables, weighted layouts, typography
  • The obvious average-AI pick is recorded, then consciously rejected
  • Exactly one signature moment; the rest serve it
02

The method: ask → derive → source → score → place → validate

A consulting intake, not a search box. Nothing is recommended before the brief is settled — and the message decides the components, not the gallery.

Step 0
Ask
Goal, audience, intent, tokens. Blocking — max 5 questions.
human-gated
Step 2
Derive
Walk the IA → job-slots. Tag one signature.
ai
Step 3
Source
Name the element in English → route to the right gallery.
ai
Step 4
Score
5-axis rubric, gates first. Message-fight = reject.
ai
Step 5
Place
Rhythm, decision-journey order, mobile + backend honesty.
ai
Step 6
Validate
Ban-check, one-signature cap, self-check loop.
human-reviewed
03

The fit rubric — the part the "use a gallery" advice skips

Five axes, gates first. A component that fights the message is rejected even if its colors are perfect.

AxisWhat it measuresRole
A · Token fitRemap distance from the DESIGN.md tokens — colors, type, radius, elevation, spacing.weight .35
B · Message fitDoes the component's rhetorical intent reinforce or fight the site's stated intent?GATE
C · UX + backend honestyFlow-fit, and whether interactivity is real — a dead control on a critical path is a reject.weight .30
D · DistinctivenessLands in distinctive/awwwards, not bland and not gratuitous. Penalizes average-AI tells.weight .35
E · House overrideWhen a house system (e.g. ACH Thesis) is in play, its patterns become first-choice.GATE
04

One scene, to make the message-gate concrete

Case · the message gate in action

A grief-support service.

The brief: gentle, steady, human. The AI reaches for an aurora-spotlight hero — animated gradient, glowing particles — because it reads as "premium," and it even takes the brand's blue cleanly. Token fit: a 3 out of 4.

The rubric rejects it anyway. Axis B is a gate: a hero whose rhetoric is "hype, kinetic, launch" actively undermines a service whose whole job is to feel calm and safe. Token fit becomes irrelevant. The same component scores a 4 on a brutalist agency launch — fit is never a property of the component alone.

→ fit-rubric.md, Axis B worked pairings
05

Why this needed its own skill

Evidence · the division of labor

Component knowledge already exists. Component judgment didn't.

A component knowledge base knows what a card is. A token scaffolder knows the colors. A slop validator knows what's average. None of them answer: for this message, with these tokens, buy this component, place it here, and here's why a flashier one would fight the statement.

That binding — message × tokens × placement, at a defensible distinctiveness band — is the whole job, and it's the one thing none of the neighbors do.

→ SKILL.md §5, Division of labor
06

The self-check it runs before it ships a recommendation

07

Provenance — this page was built by the skill it describes

Fittingly, in the ACH Thesis design system: AI contribution is declared, never decorative.

AI provenance
What is AI-generated
The layout, copy, and code — produced by component-consulting running on itself, consuming ach-thesis-DESIGN.md.
What is human-decided
The subject (this skill), the archetype (ACH Thesis), and the sign-off. The human chose the constraints; the skill chose the components.
When
2026-06-18 · Axis-E (house-override) mode, so the 14 ACH patterns replaced the gallery catalog.
Restore path
Prescription preserved at consulting-report.md; tokens are the canonical ACH DESIGN.md — revert by re-running with Axis E off.
draftBrief settled — goal, audience, ACH override flagged (Step 0)
prescriptionJob-slots → ACH patterns — ThesisHeader as the one signature, rest quiet
buildImplemented — borders not shadows, blue used twice, zero gradient/glass
verifyChecked — ACH don'ts + impeccable bans, responsive, real interactivity

Stop pasting components. Start prescribing them.

One folder: a SKILL.md, a gallery catalog, a 5-axis rubric, an output template. Risk class S0 — references galleries, vendors no code.

Read the SKILL.md →