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 holds several structurally different variants of a page, app screen, component, flow, or terminal/TUI layout (rendered in-browser), plus a draggable Design D
Supply asset profile
Design and creative production
Design assets, images, video, audio, multimodal media, presentation, and creative production skills.
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
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
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Do not auto-installTrust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
Audit
Needs reviewA 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.
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.
Suited tasks
- Browser automation workflows
- Claude Code teams
- builders willing to evaluate younger projects
- Navigate pages
Suited agents
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-prototypeDo 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
Alternative
Frontend Design
170.9K Stars
npx skills add anthropics/skills --skill frontend-design
Alternative
Taste Skill: Anti-Slop Frontend
79.0K Stars
npx skills add Leonxlnx/taste-skill --skill design-taste-frontend
Alternative
Canvas Design
170.9K Stars
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Alternative
Anthropic Brand Guidelines
170.9K Stars
npx skills add anthropics/skills --skill brand-guidelines
Agent safety v2
21/100 · Avoid automatic install
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.
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.
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-prototypeAgent 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 JSON
/api/agent/resolve?task=Use%20ai-assist-prototype%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20ai-assist-prototype%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/jparkerweb-ai-assist-prototype/install
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.
Install handoff
/api/skills/jparkerweb-ai-assist-prototype/install
LLM text format
/api/skills/jparkerweb-ai-assist-prototype/install?format=text
Find alternatives
/api/skills/search?q=ai-assist-prototype&limit=3
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-prototypeRegistry 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.
Manifest
/api/registry/manifest/jparkerweb-ai-assist-prototype
LLM text
/api/registry/manifest/jparkerweb-ai-assist-prototype?format=text
Install alias
/api/registry/install/jparkerweb-ai-assist-prototype
Recommend
/api/registry/recommend?task=Use%20ai-assist-prototype%20in%20an%20agent%20workflow&limit=3
Agent fit
Browser automation
Use-case tags
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.
Agent decision cockpit
Fallback candidate for Browser automation
Prototype with this skill first; keep a fallback candidate ready.
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
- 1Install it in a sandbox agent and run one Browser automation task end to end.
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 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.
GitHub adoption
CHECK88 GitHub stars
Stars/forks activity
CHECK88 stars, 12 forks; issue activity unavailable in current metadata
Recent maintenance
PASSPushed today
License clarity
CHECKUnknown
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.
Workflow fit
Use this skill in these scenarios
Operate web apps
Browser automation
I need my agent to control a browser, fill forms, and verify web app workflows.
Operate local tools
Local desktop
I need my agent to operate local files and desktop apps in a repeatable workflow.
Automate repeated work
Workflow automation
I need my agent to automate a repeated workflow across tools and files.
Workflow fit
Add it to a complete workflow
Operate and verify web apps
Browser QA agent
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Design, build, test, and ship interfaces
Frontend and UI
A practical workflow for agents that turn product briefs or Figma designs into polished frontend code, review the result, test it in a browser, and prepare a safe deployment.
Scrape, clean, and reuse web data
Web data pipeline
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Alternative shortlist
Compare before you install
Similar skills that may fit this task.
Frontend Design
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Taste Skill: Anti-Slop Frontend
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Anthropic Brand Guidelines
Apply Anthropic official brand colors, typography, and visual standards to appropriate Anthropic-related artifacts.
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
recent repository activity
Audit
Install review
Install and adoption review
- Security
- 62/100
- Maintenance
- 100/100
- Install
- 92/100
Agent-proven evidence
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
- 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
Scenario-led draft for ai-assist-prototype, ready for a manual X post.
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
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
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
- Creator
- jparkerweb
- Indexed by
- OpenAgentSkill community index
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner 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.
Creator backlink kit
Add the evidence badges to your README
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/jparkerweb-ai-assist-prototype)
[](https://www.openagentskill.com/skills/jparkerweb-ai-assist-prototype)
[](https://www.openagentskill.com/skills/jparkerweb-ai-assist-prototype/audit)
[](https://www.openagentskill.com/skills/jparkerweb-ai-assist-prototype)Author
jparkerweb
@jparkerweb
Tags
Platform fit
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
- 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
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