Dryui Live Feedback logo

Dryui Live Feedback

Community
rob-balfre
dryui-live-feedback

Open the DryUI app for visual feedback, wait for the user to annotate the page, then act on each submission via the dryui-feedback skill. Use when the user asks for live feedback, visual feedback, or to iterate on UI by drawing on it.

Overview

Publisherrob-balfre
Repositorydryui
Skill namedryui-live-feedback
Stars
9
Forks
0
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 rob-balfre on GitHub. Read the source before you install it.

Installation

Install the Dryui Live Feedback 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.
    https://github.com/rob-balfre/dryui/tree/main/skills/dryui-live-feedback
  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/rob-balfre/dryui.git /tmp/dryui
mkdir -p .claude/skills
cp -r /tmp/dryui/skills/dryui-live-feedback .claude/skills/dryui-live-feedback
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dryui Live Feedback 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 Dryui Live Feedback 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 Dryui Live Feedback 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.

Feedback

Orchestrates the visual feedback loop: ensure servers are running, open the app, wait for the user to draw annotations, then act on them.

1. Ensure the Feedback Server Is Running

Check the health endpoint:

bash
curl -s http://localhost:4748/health

If it returns {"status":"ok"}, the server is already running. Otherwise launch it:

bash
bunx dryui-feedback --no-open

In the DryUI monorepo, if you need a repo-internal fallback and dist/ does not exist, build first:

bash
cd packages/feedback-server && bun run build

The server binds port 4748 by default (DEFAULT_FEEDBACK_PORT) and walks up to the first free port if it's taken. State lives under <project>/.dryui/feedback/ (store.db, screenshots, server.json), so each project keeps its own queue.

If dryui.config.json has feedback.defaultAgent, the server uses it as the dispatch default. Users can edit that file directly or ask an agent to add one of the manual MCP paths listed under feedback.manualAgentConfig.

2. Confirm the Widget Is Wired and Find the Dev Server

2a. Verify @dryui/feedback and @dryui/feedback-server are installed and the widget is mounted

dryui-init installs @dryui/feedback and @dryui/feedback-server as dev deps but does NOT mount the widget by default — the mount is opt-in. Confirm the install + mount before continuing:

bash
test -f node_modules/@dryui/feedback/package.json && echo WIDGET_OK || echo WIDGET_MISSING
test -x node_modules/.bin/dryui-feedback && echo SERVER_OK || echo SERVER_MISSING
grep -q "from '@dryui/feedback'" src/routes/+layout.svelte && echo MOUNTED || echo MISSING

If WIDGET_MISSING or SERVER_MISSING, fall through to the dryui-init skill's Apply Setup → Install steps. If MISSING (mount), follow dryui-init → Live Feedback (Opt-In) to add the import + <Feedback serverUrl="http://localhost:4748" /> mount in src/routes/+layout.svelte. A package.json overrides entry alone is not proof of installation — bun only applies overrides when the package is also a dep, so the node_modules check is the source of truth. Skipping this step is the most common way the rest of the flow silently breaks: the import resolves at edit time but vite throws Cannot find module '@dryui/feedback' on first request, or bunx dryui-feedback 404s against the npm registry because no top-level package by that name exists.

@dryui/feedback pulls in lucide-svelte transitively. lucide-svelte 1.0.x ships internal ./icons/index imports without a .js extension, which Node strict ESM rejects, so vite must bundle it for SSR. The dryuiLayoutCss() plugin from @dryui/lint injects ssr.noExternal: ['lucide-svelte'] automatically. If the project's vite.config.* doesn't use dryuiLayoutCss() (custom setups, ejected configs), add ssr: { noExternal: ['lucide-svelte'] } to the vite config manually before starting the dev server -- otherwise the first SSR request crashes with Cannot find module '.../lucide-svelte/dist/icons/index'.

2b. Find the right dev server

Check for vite servers on common ports (5173, 5174, 5198, 5199, 5200):

bash
lsof -iTCP:5173 -iTCP:5174 -iTCP:5198 -iTCP:5199 -iTCP:5200 -sTCP:LISTEN -P 2>/dev/null

When more than one server is listening (a developer with several SvelteKit projects open will routinely have 5173/5174 taken by unrelated apps), match by the listening process's working directory. For each PID:

bash
lsof -p <PID> -a -d cwd -Fn 2>/dev/null | sed -n 's/^n//p'

Pick the URL whose process cwd equals $PWD. Do not assume the first port returned belongs to this project. If no listening server matches the cwd, look for a dev script in this project's package.json and start it.

3. Open the App in a Browser

Use the browser tools available in your environment. Probe one, fail fast, fall through — don't wait out 30s + 120s cascading timeouts.

Probe order:

  1. chrome-devtools-mcp MCP tools (list_pages, new_page, navigate_page, take_screenshot). If list_pages returns within ~5s, use this tool for the rest of the session.
  2. chrome-devtools-axi CLI. Run chrome-devtools-axi pages with a short shell-level timeout (e.g. timeout 5 chrome-devtools-axi pages). If it returns BRIDGE_NOT_READY or hangs, do not retry — the bridge state persists across calls.
  3. Codex playwright skill ($CODEX_HOME/skills/playwright/scripts/playwright_cli.sh open <url>). Reliable fallback when neither Chrome wrapper boots; uses a fresh Playwright browser per session.
  4. If none are available, tell the user to open the dev server URL manually.

Do not retry a browser tool whose bridge or MCP call has already failed once in this session — pivot to the next.

4. Tell the User What to Do

Say:

Draw your feedback on the page using the toolbar in the bottom-right corner. Available tools: pencil, arrow, text, move, and eraser. The toolbar can be dragged to reposition it. You can also toggle drawing mode with Cmd+M (or Ctrl+M).

When you are done annotating, let me know and I will review your drawings.

The feedback component draws SVG overlays on the page. Drawings are automatically persisted to the server. There is no explicit "Send" button -- drawings save as they are created.

5. Wait for Submissions

Use the feedback_get_submissions MCP tool to poll for pending submissions:

feedback_get_submissions(timeoutSeconds: 60)

This blocks until submissions arrive or the timeout expires. If it times out with timedOut: true, ask the user if they need more time.

Important: Use feedback_get_submissions, NOT feedback_get_all_pending or feedback_watch_annotations. The Feedback component creates submissions (screenshots + drawings), not annotations.

6. Review the Submissions

Each submission includes:

  • id -- unique submission ID
  • url -- the page URL where feedback was given
  • screenshotPath -- path to the saved screenshot file (read this with the Read tool to see the page)
  • drawings -- array of drawing objects the user made on the page
  • viewport -- viewport dimensions at time of submission
  • status -- pending or resolved
  • createdAt -- timestamp

Each drawing has a kind:

  • freehand -- strokes highlighting or circling areas (points array)
  • arrow -- pointing at specific elements (start and end points)
  • text -- inline text comments positioned on the page (position and text)

Use this data to identify what the user wants changed:

  1. Read the screenshot file to see the page as the user saw it
  2. Check drawing text labels for the user's comments/instructions
  3. Use arrows to identify which elements the user is pointing at
  4. Cross-reference url with routes to find the relevant page component

7. Act on the Feedback

Based on the submissions, make the necessary code changes. Treat feedback submissions as the highest-priority user intent for the current repair loop.

Use the same UI creation pipeline as the DryUI skill while resolving feedback:

  1. Read the feedback screenshot and drawings.
  2. Confirm any DryUI component APIs you touch with the dryui skill rules, component metadata, docs pages, or existing repo usage.
  3. Apply the requested fix using DryUI components, CSS grid layout, and --dry-* tokens.
  4. Run the relevant deterministic project check/build/test command.

Common actions:

  • Fix styling -- adjust colors, spacing, typography using --dry-* tokens
  • Adjust layout -- modify CSS grid tracks, gap, alignment
  • Move elements -- reorder markup or change grid placement
  • Fix bugs -- address functional issues the user identified
  • Change content -- update text, labels, or placeholder copy

After making changes, run the relevant project check command and fix violations. Then tell the user to refresh the page and verify. If using browser tools, trigger a reload.

8. Resolve Submissions

After acting on each submission, mark it as resolved:

feedback_resolve_submission(submissionId: "<id>")

After resolving all submissions, ask the user if they have more feedback. If yes, loop back to step 4.

Important Notes

  • The feedback HTTP server (port 4748) must be running for drawing persistence and submission storage to work. Without it, the <Feedback> component's drawings will not save.
  • The MCP server (dryui-feedback in .mcp.json) connects to the HTTP server to read/write data. Both must be running.
  • Use /submissions endpoints, NOT /sessions. The Feedback component creates submissions (screenshot + drawings), not sessions/annotations. Querying /sessions will always return empty results for this workflow.
  • The <Feedback> component is toggled with Cmd+M / Ctrl+M by default (configurable via the shortcut prop). The toolbar is hidden until activated.
  • Screenshots are saved as files at screenshotPath -- read them with the Read tool to see the annotated page.
  • Drawing coordinates are relative to the active feedback scroll root. On the docs site this is the main content pane, not the browser window.

Frequently asked questions

What does the Dryui Live Feedback AI skill do?

Open the DryUI app for visual feedback, wait for the user to annotate the page, then act on each submission via the dryui-feedback skill. Use when the user asks for live feedback, visual feedback, or to iterate on UI by drawing on it.

Why use Dryui Live Feedback on TypingMind?

Because you install it once and use it with any model. Dryui Live Feedback 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 Dryui Live Feedback in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rob-balfre/dryui/tree/main/skills/dryui-live-feedback. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Dryui Live Feedback?

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 Dryui Live Feedback?

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

Is the Dryui Live Feedback AI skill free?

Yes. It is published on GitHub by rob-balfre 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇