Live Artifact logo

Live Artifact

OrganizationPopular
nexu-io
live-artifact

Create refreshable, auditable OpenDesign artifacts backed by connector or local data. Trigger when the user asks for live dashboards, refreshable reports, synced views, or reusable data-backed artifacts.

Overview

Publishernexu-io
Repositoryopen-design
Skill namelive-artifact
Stars
98.9K
Forks
11.5K
Bundled files
15
LicenseApache-2.0
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.

  • 15 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by nexu-io on GitHub. Read the source before you install it.

Installation

Install the Live Artifact 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/nexu-io/open-design/tree/main/design-templates/live-artifact
  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/nexu-io/open-design.git /tmp/open-design
mkdir -p .claude/skills
cp -r /tmp/open-design/design-templates/live-artifact .claude/skills/live-artifact
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Live Artifact 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 Live Artifact 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 Live Artifact 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.

Live Artifact Skill

Create an OpenDesign live artifact: a project-scoped, previewable HTML artifact whose data can later be refreshed without redesigning the presentation.

Resource map

live-artifact/
├── SKILL.md
└── references/
    ├── artifact-schema.md      ← `references/artifact-schema.md`: artifact files, DTO shape, template binding rules
    ├── connector-policy.md     ← `references/connector-policy.md`: connector safety, redaction, credential boundaries
    └── refresh-contract.md     ← `references/refresh-contract.md`: source metadata, refresh execution, snapshots

Current status

Use the references in this directory as the source of truth for the live artifact file contract. Prefer daemon wrapper commands over raw HTTP when registering or updating live artifacts.

When to use this skill

Use this skill when the user asks for a data-backed view that should remain useful after the first render, for example a live dashboard, refreshable report, synced status page, auditable data view, or artifact that can later be refreshed from local/project data or connectors.

Before creating files, decide whether the user actually wants a live artifact or a normal static artifact:

  • Use a live artifact when the user mentions refresh, sync, recurring updates, connector-backed data, source/provenance tracking, dashboards, reports, or reusable data-backed views.
  • Use a normal static artifact when the user only wants a one-off HTML/mockup/image/file and does not need refresh, source metadata, or data/provenance panels.
  • If the intent is ambiguous, ask one short question: “Should this be refreshable/live, or just a static artifact?”

Workflow

  1. Resolve scope and data source without blocking on connected connectors

    • Identify the preview goal, audience, data freshness expectations, and whether refresh should be possible later.
    • If the user explicitly names a connector/source such as Notion, GitHub, Slack, or Google Drive, do not ask “where should the data come from?” before checking daemon connector tools.
    • Prefer local/project sources or daemon connector tools when available.
    • Do not call provider APIs directly when a daemon connector/wrapper exists.
    • If connector data is needed, first list connectors with "$OD_NODE_BIN" "$OD_BIN" tools connectors list --format compact. If the named connector is present with status: "connected", choose an appropriate read-only auto tool from its catalog and execute it through the connector wrapper.
    • For Notion specifically, a connected notion connector plus a user brief that names Notion is enough to start with notion.notion_search using a query derived from the requested artifact/topic. Use notion.notion_fetch_database only when the user supplied a database id or the search result clearly identifies one.
    • Ask the user a data-source question only when no matching connected connector exists, multiple connected candidates fit equally well, or the requested artifact has no usable topic/query to search for. If you must ask, be specific: ask for the page/database/topic or permission to search broadly, not “where is the Notion data source?”
  2. Author the source files

    • Write template.html as the human-designed HTML template. The daemon hydrates it with data.json using the html_template_v1 binding contract — stay inside it or hydration fails and the raw template ships with visible {{…}} tokens:
      • Scalars: {{data.path.to.value}}. Paths start with data, dot-separated; numeric array indexes are allowed ({{data.kpis.0.value}}). Every binding must resolve to a single string/number, not an object or array.
      • Lists: repeat one element with data-od-repeat="item in data.items", then bind loop-scoped fields inside it as {{item.label}} (or {{item}} for a scalar array). One level only; data.* still works inside the repeat for globals.
      • Do NOT hand-loop in a <script> (scripts are stripped from previews), and do NOT reference a bare loop variable like {{metric.value}} without a matching data-od-repeat scope — that binding is unresolvable. Nested repeats, conditionals, filters, helpers, and raw/triple-brace interpolation are unsupported.
    • Write data.json as the canonical preview data used by those bindings — real values, not placeholders.
    • Write artifact.json with the live artifact metadata, preview declaration, document declaration, and safe source descriptors.
    • Write provenance.json with concise source notes, timestamps, non-sensitive connector references, and transformation notes.
    • Do not author index.html as source. The daemon derives index.html from template.html and data.json.
  3. Keep data compact and preview-oriented

    • Store only normalized values needed by the preview.
    • Summarize large lists, provider responses, or logs before writing them into data.json.
    • Stay within the bounded JSON rules in references/artifact-schema.md.
  4. Apply safety rules before registration

    • Never store credentials, OAuth tokens, API keys, cookies, auth headers, raw provider responses, HTTP envelopes, full payloads, or secret-like fields in artifact.json, data.json, provenance.json, or source metadata.
    • Avoid forbidden key names such as raw, rawResponse, payload, body, headers, cookie, authorization, token, secret, credential, and password anywhere in persisted JSON.
    • Use escaped html_template_v1 interpolation only. Raw/unescaped HTML interpolation is not allowed.
  5. Register or update through daemon wrappers

    • Use the OpenDesign daemon wrapper commands via "$OD_NODE_BIN" "$OD_BIN" instead of raw curl, bare node, or bare od:

      bash
      "$OD_NODE_BIN" "$OD_BIN" tools live-artifacts create --input artifact.json
      "$OD_NODE_BIN" "$OD_BIN" tools live-artifacts list --format compact
      "$OD_NODE_BIN" "$OD_BIN" tools live-artifacts update --artifact-id "$ARTIFACT_ID" --input artifact.json
    • The wrapper reads injected OD_NODE_BIN, OD_BIN, OD_DAEMON_URL, and OD_TOOL_TOKEN; do not print, persist, or override token values.

    • Do not include or invent projectId; the daemon derives project/run scope from the token.

    • Use raw HTTP only for daemon development/debugging when explicitly requested.

  6. Use connector wrappers for connector data

    • Discover available connectors and tools:

      bash
      "$OD_NODE_BIN" "$OD_BIN" tools connectors list --format compact
    • Execute a read-only connector tool with a JSON object input file:

      bash
      "$OD_NODE_BIN" "$OD_BIN" tools connectors execute --connector "$CONNECTOR_ID" --tool "$TOOL_NAME" --input input.json
    • Persist only the compact normalized fields needed by the preview plus non-sensitive connector references (connectorId, toolName, accountLabel). Never persist connector credentials, transport metadata, or raw provider output.

    • Do not ask for connector secrets or duplicate setup. If status is connected, use the listed tools; if it is not connected, tell the user to connect it in the UI.

    • See references/connector-policy.md for listing/execution and credential boundaries, and references/refresh-contract.md for read-only refresh source metadata.

  7. Report concise results

    • On success, return the artifact ID/title and note that index.html is daemon-derived.
    • On validation failure, fix the source files and retry through the wrapper. Do not bypass validation.

Required files

Every live artifact creation flow must produce these source files before registration:

  • template.html — declared skill output and source template for the preview.
  • data.json — compact, canonical preview data.
  • artifact.json — create/update input for daemon validation.
  • provenance.json — safe source and transformation summary.

index.html is the primary preview entry declared in frontmatter, but it is derived daemon output rather than agent-authored source.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Live Artifact AI skill do?

Create refreshable, auditable OpenDesign artifacts backed by connector or local data. Trigger when the user asks for live dashboards, refreshable reports, synced views, or reusable data-backed artifacts.

Why use Live Artifact on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nexu-io/open-design/tree/main/design-templates/live-artifact. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Live Artifact?

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 Live Artifact?

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

Is the Live Artifact AI skill free?

Yes. It is published on GitHub by nexu-io under the Apache-2.0 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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