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Finalize

Community
educlopez
finalize

Pre-ship gate — runs detector, verifies brief and tokens, applies the 10-pass finish bar, ranks findings by feedback hierarchy. Use when the user wants to ship, merge, or finalize a surface and needs a verdict (READY / NOT READY / BLOCKED) before committing. Invoke when the user asks for finalize on their UI, or mentions 'finalize' alongside design / UI / frontend work.

Overview

Publishereduclopez
Repositoryui-craft
Skill namefinalize
Stars
344
Forks
17
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by educlopez on GitHub. Read the source before you install it.

Installation

Install the Finalize AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/educlopez/ui-craft.git /tmp/ui-craft
mkdir -p .claude/skills
cp -r /tmp/ui-craft/cli/assets/codex/skills/finalize .claude/skills/finalize
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Finalize in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Finalize on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Finalize is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Context: this sub-skill is one lens of the broader ui-craft skill. If the ui-craft skill is also installed, read its SKILL.md first for Discovery + Anti-Slop + Craft Test, then apply the specific lens below.

Load the ui-craft skill. This command produces findings only — do NOT edit code.


Step 1 — Brief check (gate)

Look for .ui-craft/brief.md.

  • Absent: STOP. Output: "No brief found. Run /brief first. /finalize requires a design brief to distinguish intentional decisions from findings." Do not proceed.
  • Present: load it. Extract principles (numbered, in conflict-resolution order). These are the override registry for Steps 4–5.

Step 2 — Detector

Run npx ui-craft-detect on $ARGUMENTS (or current working surface if no argument).

Capture all output. Every Critical detector finding maps directly to a block-ship finding in the report. Preserve detector source labels.


Step 3 — Token check

Quick three-question audit (no deep read required):

  1. Are all three token layers present (primitives → semantic → component)?
  2. Are both light and dark modes explicitly authored (not inverted)?
  3. Are all seven token categories represented (color, spacing, radius, shadow, typography, motion, z-index)?

Any "no": flag as Major. Do not block ship. Append a recommendation: "Run /tokens to close the gap at [category]."


Step 3b — Craft-intent check (full surfaces only)

Applies only when the target is a complete surface (dashboard, landing, auth, settings shell, portfolio) — skip for single components.

  1. Signature bet present? Scan for exactly one memorable decision from references/craft-intent.md §3 (product / auth / marketing lists). Zero → Major ("no signature — surface is competent but anonymous; run /bolder or /polish"). Two or more competing → Major ("signature bets stack — keep the strongest, cut the rest").
  2. Variance matches surface? Layout risk visibly inconsistent with the surface's DESIGN_VARIANCE default (e.g. experimental asymmetry on an auth form, or a landing that is one centered symmetric column) → Major, unless the brief or spec records the deviation.
  3. Craft Read recorded? If a .ui-craft/spec.md section or session declared a Craft Read, verify the built surface matches it (theme, variance, signature). Mismatch → Major with the specific delta.

These findings are Major, never Critical — they gate NOT READY, not BLOCKED.


Step 3c — Copy self-audit + mechanical counts (marketing surfaces only)

Applies to landings, portfolios, and campaign pages — skip for product shells.

Load references/recipe-landing.md and run its Acceptance-bar mechanical checks against the built surface — thresholds (eyebrow count, layout-family budget, hero discipline) live there, not here; this step only names which checks apply and where a violation lands.

  1. Copy self-audit. Read every visible string (headlines, eyebrows, buttons, captions, alt text, footer). Grammatically broken copy, unclear referents, forced-clever labels ("Field notes" over testimonials, "Quietly trusted by"), or fake-precise numbers with no source → Major per references/copy.md Copy Self-Audit.
  2. Eyebrow count (mechanical) — per references/recipe-landing.md Eyebrow budget. Over budget → Major ("eyebrow flood — delete labels until the count passes").
  3. Layout-family count (mechanical) — per references/recipe-landing.md Layout-family budget. Any repeat or consecutive-split overrun → Major.
  4. CTA intent (mechanical) — per references/copy.md CTAs & Buttons (one label per intent; CTA fits one line at desktop). Violation → Major.
  5. Hero discipline — per references/recipe-landing.md Hero discipline. Over budget → Major.

Step 4 — Finish bar

Load references/finish-bar.md. Run all 10 passes in document order. Do not reorder.

Knob behavior (apply before running):

  • CRAFT_LEVEL ≤ 6 AND user did not explicitly invoke /finalize: run Passes 1, 6, 8 only; mark remaining as N/A (knob-gated).
  • CRAFT_LEVEL ≥ 7 OR user explicitly invoked /finalize: run all 10 passes.
  • MOTION_INTENSITY ≤ 3: Pass 7 = motion-gap audit only; do not fail on absent entrance animations.
  • VISUAL_DENSITY ≥ 8 AND brief documents compressed rhythm: Pass 4 accepts compressed spacing values.

For each pass:

  • Collect findings.
  • Assign severity per the severity map in finish-bar.md (Critical / Major / Minor).
  • If a finding conflicts with a recorded principle in brief.md: downgrade to "Deferred per brief" and cite the exact principle. If no brief principle covers it, it remains a finding regardless.
  • Record file:line when available.

Step 5 — Feedback hierarchy filter

Load references/review.md Feedback Hierarchy section.

Group all findings (detector + token + finish-bar) into three tiers:

  • Value — does the surface solve the problem the brief defines?
  • Ease of Use — can the user accomplish the primary task?
  • Delight — is it polished?

If any Value findings exist: surface them first in the report and add a standing recommendation to defer all Delight findings until Value findings are resolved. This prevents polish work masking broken functionality.


Step 6 — Output

Print this report. Do not add preamble.

## Finalize report — <surface name>

**Ship verdict:** READY / NOT READY / BLOCKED

> BLOCKED = any Critical finding present.
> NOT READY = any Major finding present, no Critical.
> READY = zero Critical, zero Major (Minor explicitly accepted).

### Block-ship findings (must fix before merge)
- [Pass / detector source] — [finding] — [file:line if available]

### Major findings (fix or defer with recorded reason)
- [...]

### Minor findings (polish; ship-okay if explicitly accepted)
- [...]

### Deferred per brief
- [finding] — [principle from brief.md that defers it]

### Recommended next actions
- [ordered, 3–5 items max]

If a tier has no findings, omit the section header entirely.


Step 6b — Convergence mode (explicit opt-in only)

Convergence mode activates ONLY when the user explicitly says "run in convergence mode", "converge until the bar passes", or uses a --converge flag. A vague "keep going", "fix it", or "make it pass" does NOT trigger convergence — the default remains findings-only (Step 7 hard-stop applies).

When explicitly triggered: load skills/ui-craft/references/loops.md and run preset visual-anti-slop (budget the default loop budget defined in loops.md, hard cap 5). Emit the pre-flight cost notice before iteration 1. After each single highest-impact fix, re-run finalize Steps 2–6 (this command's own steps) until READY or budget exhausted. Note: finalize's findings-only hard-stop (Step 7) is lifted ONLY inside convergence mode — fixes are applied during iteration.


Step 7 — Hard stops

Do not edit any file. Do not propose code changes inline. The verdict is the output. If the user asks for fixes after reading the report, respond to that as a separate request.

Next step: Ship. If the gate is red, the findings name the pass to re-run — /polish, /harden or /clarify.

Frequently asked questions

What does the Finalize AI skill do?

Pre-ship gate — runs detector, verifies brief and tokens, applies the 10-pass finish bar, ranks findings by feedback hierarchy. Use when the user wants to ship, merge, or finalize a surface and needs a verdict (READY / NOT READY / BLOCKED) before committing. Invoke when the user asks for finalize on their UI, or mentions 'finalize' alongside design / UI / frontend work.

Why use Finalize on TypingMind?

Because you install it once and use it with any model. Finalize is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Finalize in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/educlopez/ui-craft/tree/main/cli/assets/codex/skills/finalize. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Finalize?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Finalize?

As many as you like. As long as a model supports skills, you can use Finalize with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Finalize AI skill free?

Yes. It is published on GitHub by educlopez under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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