ai-assist-prototype

REVIEW · 51
Registry indexed

Build self-contained, double-click-to-open HTML prototypes so the user can vet an interface before it gets built. One file holds several structurally different variants of a page, app screen, component, flow, or terminal/TUI layout (rendered in-browser), plus a draggable Design D

Verified installs0
Stars88
Version1.0.0
Quality61/100 · Promising
Trust51/100 · Do not auto-install
Audit69/100 · Needs review

Supply asset profile

Design and creative production

Design assets, images, video, audio, multimodal media, presentation, and creative production skills.

Browse track

Scenario

Design and creative

I need my agent to produce design assets, UI directions, presentations, or creative media workflows.

Agent fit

Claude Code + Cursor + Browser agents

Codex, Claude Code, Cursor, CLI, or custom agents.

Install

Ready

npx skills add jparkerweb/ai-assist-skills --skill ai-assist-prototype

Maintenance

fresh

Pushed today

Risk

Needs review

License is unclear

GitHub quality

88

61/100 Quality · 59/100 Trust

Coverage tags

DesignDesign and creativedesign-creativeagent-skill

Review notes

License is unclear · Dependency or permission surface needs review

Agent adoption scorecard

Trust, audit, and install readiness at a glance

These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.

Quality

Promising
61

Useful candidate, but compare it with alternatives before adopting.

Trust

Do not auto-install
51

Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.

Audit

Needs review
69

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

OpenAgentSkill Trust Score v5

Human review before install

Choose a stronger alternative or inspect the source manually before any install attempt.

CodexClaude CodeCursorOpenAgentSkill CLI

Stars

88 GitHub stars

Repo activity

88 stars, 12 forks

Maintenance

Pushed today

License

Unknown

Install

npx skills add jparkerweb/ai-assist-skills --skill ai-assist-prototype

Install safety

standard package or runtime install path

Permission surface

secrets or environment access, shell or command execution

Agent outcomes

No agent outcome data yet

Docs

Strong README/SKILL.md context

Risk summary

Review before production

  • Repository license is unknown (GitHub detected 'Unknown'), which creates legal ambiguity for users and contributors.
  • Financial research output is not financial advice; require human review before any live investment decision.
  • License is unclear
  • Quality score needs review

Install readiness

Install path available

  • Install path is available
  • Repository evidence is available
  • License is unclear
  • No Agent Proven outcome evidence yet

Agent-readable metadata

Machine-readable decision data for this skill.

Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.

Open JSON

Suited tasks

  • Browser automation workflows
  • Claude Code teams
  • builders willing to evaluate younger projects
  • Navigate pages

Suited agents

CodexClaude CodeCursorOpenAgentSkill CLIBrowser agentsCLI

Install decision

Command
npx skills add jparkerweb/ai-assist-skills --skill ai-assist-prototype
Policy
block
Human review
yes

Trust and risk

Trust
51/100
Audit
69/100
Risk level
Needs review

Outcome loop

Endpoint
/api/agent/outcome
Event ID
resolve
Outcomes
5

Install command

npx skills add jparkerweb/ai-assist-skills --skill ai-assist-prototype

Do not use when

  • teams that need a vendor-supported SLA
  • production agents without a repository review
  • Repository license is unknown (GitHub detected 'Unknown'), which creates legal ambiguity for users and contributors.
  • No OpenAgentSkill engagement data yet
  • High-risk permission hints: Shell or command execution, Secrets or environment access

Agent safety v2

21/100 · Avoid automatic install

Blocked for auto-installblock

This skill should not be selected by an agent without explicit human security review.

Do not auto-install. Inspect the source, dependencies, and permission surface first.

Resolve via API

high

Shell or command execution

Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.

medium

Browser automation

Skill may drive a browser or interact with web pages.

medium

Network access

Skill likely fetches remote pages, APIs, repositories, or external services.

medium

Filesystem access

Skill may read or write project files, documents, generated artifacts, or local workspace state.

  • High-risk permission hints: Shell or command execution, Secrets or environment access
  • License is unclear

Install targets

Install this skill in your agent workflow

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

skill install

OpenAgentSkill CLI

Resolve policy, run the source installer safely, and report a verified install receipt.

$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install jparkerweb-ai-assist-prototype

Agent resolve plan

Let an agent verify fit before installing.

The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.

Open text plan

Agent should check

  • Task fit and alternatives from Resolve API.
  • Audit score, trust score, and safety policy warnings.
  • Install target compatibility for Codex, Claude Code, Cursor, or CLI.

Copy prompt

Task: Use ai-assist-prototype in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20ai-assist-prototype%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/jparkerweb-ai-assist-prototype/install
Install command: npx skills add jparkerweb/ai-assist-skills --skill ai-assist-prototype
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.

Agent handoff

Give an agent the install path, not another directory page.

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

Open install API

Agent prompt

Use ai-assist-prototype for this task. Review https://www.openagentskill.com/api/skills/jparkerweb-ai-assist-prototype/install, then install with: npx skills add jparkerweb/ai-assist-skills --skill ai-assist-prototype

Registry metadata

Agent-readable profile for automatic skill selection.

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open manifest

Agent fit

60/100

Browser automation

Platforms

Claude Code, Cursor, Browser agents

Audit report

Needs review · 69/100

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

View audit reportView eval report

Agent decision cockpit

Fallback candidate for Browser automation

Prototype with this skill first; keep a fallback candidate ready.

60
Readiness
Prototype
Stage

Role in stack

Fallback candidate

Primary fit

Browser automation

Trust label

Prototype first

Install path

Command ready

Use when

  • Browser automation workflows
  • Claude Code teams
  • builders willing to evaluate younger projects

Evidence

  • recent repository activity
  • install command or GitHub repo available
  • 61/100 quality profile

review first

  • Repository license is unknown (GitHub detected 'Unknown'), which creates legal ambiguity for users and contributors.
  • No OpenAgentSkill engagement data yet

Implementation path

  1. 1Install it in a sandbox agent and run one Browser automation task end to end.
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.

Trust profile

Do not auto-install

Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.

51
OpenAgentSkill Trust Score

GitHub adoption

CHECK

88 GitHub stars

Stars/forks activity

CHECK

88 stars, 12 forks; issue activity unavailable in current metadata

Recent maintenance

PASS

Pushed today

License clarity

CHECK

Unknown

Good signals

  • AI review approved
  • Install path is available
  • Repository evidence is available
  • Recently maintained repository
  • Install command has no obvious high-risk pattern
  • Outcome loop is ready but needs first real agent run

Review before install

  • Repository license is unknown (GitHub detected 'Unknown'), which creates legal ambiguity for users and contributors.
  • Financial research output is not financial advice; require human review before any live investment decision.
  • License is unclear
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • GitHub adoption: 88 GitHub stars
  • Stars/forks activity: 88 stars, 12 forks; issue activity unavailable in current metadata
  • License clarity: Unknown
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
  • No real agent outcome reports yet
  • Human review required before unattended installation

Recommended action

Choose a stronger alternative or inspect the source manually before any install attempt.

Quality profile

Promising candidate for agent workflows

Useful candidate, but compare it with alternatives before adopting.

61
GitHub stars
88
Freshness
Today
Install ready
Yes
License
Unknown
Review before install: Repository license is unknown (GitHub detected 'Unknown'), which creates legal ambiguity for users and contributors.

Workflow fit

Use this skill in these scenarios

Workflow fit

Add it to a complete workflow

Alternative shortlist

Compare before you install

Similar skills that may fit this task.

Compare all

Overview

--- name: ai-assist-prototype description: "Build self-contained, double-click-to-open HTML prototypes so the user can vet an interface before it gets built. One file holds several structurally different variants of a page, app screen, component, flow, or terminal/TUI layout (rendered in-browser), plus a draggable Design Deck for flipping between variants, tuning fonts, colors, spacing, shape, motion and 'feel' with live dials, trying vibe presets, checking viewport sizes and light/dark, pinning comments on elements, and an 'Export to LLM' button that copies the chosen variant and every dial value as a handoff block to paste back to the agent. Use this whenever the user wants to prototype, mock up, wireframe, explore options for, or sanity-check a UI (landing page, dashboard, settings page, onboarding, form, mobile screen, component, CLI/TUI layout), or says 'what should this look like', 'show me a few options', 'let me tweak it before we build', 'vet the design'. Also use it when the user pastes a block starting with 'AI-ASSIST PROTOTYPE HANDOFF': that is this skill's export and tells you which variant and settings they chose. Triggers on: prototype, mockup, wireframe, design options, variations, vet the UI, tweak the look, TUI mockup, design handoff." argument-hint: "[what to prototype, e.g. 'settings page, 3 variants' | or paste an AI-ASSIST PROTOTYPE HANDOFF block]" ---

# Prototype

Build a throwaway-but-polished prototype the user can open by double-clicking, explore, tune, and hand back to you with a structured export. The spine is: **brief → plan variants → build one file → verify → hand over → receive the handoff → iterate or implement.**

Three ideas make this useful rather than another mockup generator:

- **Variants explore structure, dials explore feel.** Variants differ in layout, information hierarchy and primary affordance. Colors, fonts, spacing, radius, elevation and motion are live dials that work on every variant, so never spend a variant on a recolor. - **One self-contained file.** No server, no build, no dependencies, no account. It survives being emailed to a PM or designer, who can tune it and export their decision without you in the room. - **The export closes the loop.** "Export to LLM" copies a handoff block (chosen variant, changed dials, notes, pinned comments, resolved CSS variables). The user pastes it back; you read it and either iterate or implement the real thing.

## Modes: detect from the input

| Input | Mode | |---|---| | A brief, a feature, a page, "what should this look like", "show me options" | **Build** (this is the bullseye) | | "add a variant", "make B denser", "tweak the prototype", an existing `prototypes/*.html` mentioned | **Iterate** | | A pasted block starting with `AI-ASSIST PROTOTYPE HANDOFF` or JSON with `"schema": "ai-assist-prototype/handoff@1"` | **Receive** (see "Receiving a handoff") |

## What you produce

- `prototypes/<slug>.parts.html`: the source you author. A JSON manifest plus one `<template data-variant="…">` per variant. Small, readable, diffable. - `prototypes/<slug>.html`: the deliverable, assembled by `scripts/build-prototype.mjs` from the parts file and the harness in `assets/template.html`. Never hand-copy or retype the harness; it is large and must stay intact.

Default location is `prototypes/` at the project root (create it). Put it next to the feature instead if the repo clearly organizes design artifacts elsewhere, and follow an explicit user path over either.

The built file contains the **Design Deck**, a draggable floating panel that:

- flips between variants (◀ ▶, or ← → keys), and between screens inside a variant when the variant declares them - offers vibe presets (Neutral, Calm, Bold, Editorial, Playful, Technical, Midnight, Mono, plus any you add) and ~25 dials: Feel macros (warmth, energy), Type (display/body fonts from a curated Google Fonts list, base size, scale, line height, tracking, weights), Color (light/dark, accent hue/sat/light, secondary hue shift, neutral hue/tint, surface depth, contrast), Shape (radius, border, elevation), Space (density, container width), Motion, and any custom controls the manifest adds - previews at Fit / 390 / 820 / 1280 / 1536 widths (each variant is its own document, so real media queries respond) - collects notes and click-to-pin comments on elements, saves snapshots to compare looks, and persists everything in `localStorage` per prototype - **Export to LLM** copies the handoff block; JSON copies only the JSON; View shows it for manual copy when the clipboard is blocked

TUI prototypes use the same file and deck with a terminal-specific dial set (theme, font, columns/rows, border glyphs, cursor, CRT scanlines).

## Step 1: Get the brief (fast)

Pin these down, from `$ARGUMENTS`, the conversation, and the repo, before planning:

- **Subject and audience**: what is being designed, for whom, and the one job the screen has. - **The design question**: what the prototype should settle ("table or cards?", "wizard or single form?", "does the sidebar earn its space?"). The export carries this brief, so write it as a real sentence. - **Kind**: `web` by default; `tui` when the subject is a terminal/CLI tool (ncurses, Ink, Bubble Tea, Textual, ratatui, a curses dashboard, a CLI wizard). - **Variant count**: default 3, cap 5. Fewer when the question is narrow, never more than five (they stop being different and start being noise). - **Where to save**: default `prototypes/<slug>` as above.

Look around the repo for things that make the prototype feel like *their* product rather than a generic page: a `DESIGN.md` (the `ai-assist-design-creator` output), `tailwind.config.*`, CSS custom properties, an existing nav/header, brand name, real entity names and data shapes, existing copy. Seed the manifest `defaults` and a brand preset from them and use the real content. Say what you reused.

Ask at most one or two questions, and only ones whose answer changes the work (for example, which existing page hosts this, or web vs TUI when genuinely ambiguous). If the user is not around, state your assumptions in the plan and build anyway; a prototype that exists is easy to redirect, a questionnaire is not.

## Step 2: Plan the variants

Read `references/variant-playbook.md` for archetype menus per surface type, content rules, the quality floor and the self-critique checklist. Then write a compact plan:

| id | name | thesis (what this variant bets on) | structure in one line | |---|---|---|---| | `ledger` | Ledger | Power users scan; a dense table beats cards | top nav, KPI strip, full-width table, detail screen | | `beds` | Garden beds | Spatial grouping mirrors the real garden | sidebar of beds, card grid, detail screen | | `journal` | Journal | One thing at a time, phone-first | single column feed, sticky primary action |

Hold the set to a structural-diversity test: if two variants would look alike with the same preset applied, one of them is a recolor, so redo it with an explicit structural constraint ("no card grid", "no sidebar", "list + detail"). Decide up front which screens a variant needs (usually a main screen and one drill-in), which custom controls earn a dial (sidebar width, density of a specific table, a chart style: things a dial can express and the user will actually want to tune), and one or two brand presets.

Show the plan in chat in a few lines and proceed. If the user is present they can redirect before you build; do not block on approval.

## Step 3: Build the parts file and assemble

Read `references/token-contract.md` before writing any CSS. It lists the manifest schema, every dial, every `--pt-*` variable the dials drive, the helper classes, screens, and the `PT` bridge API. For `kind: "tui"` also read `references/tui-prototypes.md`.

Author `prototypes/<slug>.parts.html`:

```html <script type="application/json" id="pt-manifest"> { "schema": "ai-assist-prototype/manifest@1", "id": "garden-dashboard", "name": "Garden Companion · dashboard", "kind": "web", "brief": "Home screen for a garden planner. Hobby gardeners on laptop and phone. Table-first, sidebar cards, or journal feed?", "controls": "web", "defaults": { "accentHue": 140, "fontDisplay": "Fraunces" }, "extraControls": [ { "id": "sidebarWidth", "label": "Sidebar width", "group": "Layout", "type": "range", "min": 200, "max": 340, "step": 10, "default": 260, "unit": "px", "var": "--x-sidebar-w" } ], "presets": { "Garden": { "accentHue": 140, "neutralHue": 90, "radius": 12 } }, "variants": [ { "id": "ledger", "name": "Ledger", "thesis": "Dense table for scanning", "screens": [{ "id": "home", "name": "Today" }, { "id": "plant", "name": "Plant" }] }, { "id": "beds", "name": "Garden beds", "thesis": "Sidebar of beds, cards per plant", "tokens": { "radius": 16 } } ] } </script>

<template data-variant="ledger"> <style> .hero h1 { font-size: var(--pt-t-3xl); } .card { background: var(--pt-surface); border: var(--pt-border) solid var(--pt-border-color); border-radius: var(--pt-radius); padding: var(--pt-s-5); box-shadow: var(--pt-shadow-sm); } @media (max-width: 720px) { .kpis { grid-template-columns: 1fr 1fr; } } </style> <section data-screen="home" data-screen-name="Today"> … </section> <section data-screen="plant" data-screen-name="Plant" hidden> … </section> <script> PT.root.addEventListener('click', e => { const g = e.target.closest('[data-go]'); if (g) PT.go(g.dataset.go); }); </script> </template> ```

Authoring rules that make the dials and the export work:

- **Consume tokens, never hardcode the look.** Colors via `--pt-bg / --pt-surface / --pt-surface-2 / --pt-text / --pt-text-muted / --pt-border-color / --pt-accent / --pt-accent-soft / --pt-accent-2`, type via `--pt-font-display / --pt-font-body` and `--pt-t-xs … --pt-t-5xl`, spacing via `--pt-s-1 … --pt-s-9`, shape via `--pt-radius*`, `--pt-border`, `--pt-shadow-sm/md/lg`, motion via `--pt-dur-*`. Decorative illustration (a gradient thumbnail) may use accent-derived vars; the validator warns on anything else. - **Each variant is its own document.** Plain selectors are safe, `@media` queries work, the Deck's viewport buttons resize the document. Base styles and optional helpers (`.pt-container`, `.pt-card`, `.pt-btn`, `.pt-btn-primary`, `.pt-input`, `.pt-badge`, `.pt-muted`, `.pt-eyebrow`) are present; use them or style your own structure. - **Real content.** The product's words, entities and realistic quantities. No lorem ipsum, no "Item 1". Placeholder art is CSS gradients or inline SVG, never remote images. - **Light interactivity is welcome** (tabs, hover, open a detail screen, toggle a state) with in-memory data, no network, no persistence. Inline `<script>` in a template runs when that variant mounts; `PT.root` is its document, `PT.go(id)` switches screens, `PT.tokens()` / `PT.on('tokens', fn)` react to dials. - **Quality floor**: responsive at 390 / 820 / 1280, visible focus, reduced motion honored (the base styles do this), readable in both modes, no horizontal overflow (`minmax(0, 1fr)` in sidebar grids).

Assemble and validate in one command (`<skill-dir>` is the folder containing this SKILL.md):

```bash node <skill-dir>/scripts/build-prototype.mjs --parts prototypes/<slug>.parts.html --out prototypes/<slug>.html ```

The build splices your parts into the harness, sets the page title, and runs `scripts/validate-prototype.mjs` (manifest shape, every variant has a template and vice versa, self-contained, token usage lint). Fix errors; treat warnings as review notes and clear the ones that are not deliberate.

## Step 4: Verify like a design lead

1. Open it. Windows: `start "" "prototypes\<slug>.html"`; macOS: `open prototypes/<slug>.html`; Linux: `xdg-open prototypes/<slug>.html`. The file works from `file://`. 2. If you have browser automation, drive it instead of guessing: load the file (or serve the folder locally if your tool refuses `file://`), then use `window.__PT__`: `await __PT

Technical details

Version
1.0.0
License
Unknown
Last updated
Aug 22, 2026
Published
Aug 22, 2026

Decision snapshot

Fallback candidate

60
Ready
Prototype
Stage

recent repository activity

Audit

Install review

Install and adoption review

69
Needs review
Security
62/100
Maintenance
100/100
Install
92/100
Open full auditView eval report

Agent-proven evidence

Agent-proven evidence

Outcome reports after resolve, review, install, and one narrow run.

0
Proven
Needs first agent runAuto-install: review firstLast: Unknown
Success rate
Recent failure
Outcomes
0
Output quality
Failed
0
Not relevant
0
Installs
0
Risk blocked
0
Setup needed
0
Production
0

No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.

Install

Add to agent workflow

Free and open source. Review the report before installing into production agents.

Growth loop

Share kit

X

Scenario-led draft for ai-assist-prototype, ready for a manual X post.

Curator note
A practical pick for the next deck:

ai-assist-prototype: Build self-contained, double-click-to-open HTML prototypes so the user can vet an interface before it gets built. One file...

88 stars

https://www.openagentskill.com/skills/jparkerweb-ai-assist-prototype?ref=x
Open X draft
Optional reply with install command
Listing + install path for ai-assist-prototype:
https://www.openagentskill.com/skills/jparkerweb-ai-assist-prototype?ref=x

Install: npx skills add jparkerweb/ai-assist-skills --skill ai-assist-prototype

Listing source

Registry indexed

Claimable

This listing was indexed from public sources and is not marked official until a maintainer claim is approved.

Creator
jparkerweb
Indexed by
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Owner claim

Claim this skill listing

This Registry indexed listing is attributed to jparkerweb but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.

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Add the evidence badges to your README

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/jparkerweb-ai-assist-prototype?metric=listed&label=Listed)](https://www.openagentskill.com/skills/jparkerweb-ai-assist-prototype)
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Author

J

jparkerweb

@jparkerweb

Health signals

GitHub stars
88
Quality score
36/100
Last GitHub push
Aug 22, 2026
Framework hints
Unknown
OpenAgentSkill views
0
Install copies
0
Outbound clicks
0

Community signal

Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.

Trust & safety

Do not auto-install

51
  • GitHub adoption88 GitHub starsCHECK
  • Stars/forks activity88 stars, 12 forks; issue activity unavailable in current metadataCHECK
  • Recent maintenancePushed todayPASS
  • License clarityUnknownCHECK
  • README/SKILL.md completenessMetadata includes enough usage and workflow contextPASS
  • Dependency/runtime riskcommand execution surface, credential or environment accessFIX