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/agui-author

Author live dashboard UI from an agent via the `emit_ui` MCP tool. Emit

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cli-agent-orchestrator
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$ npx -y skills add awslabs/cli-agent-orchestrator --skill agui-author --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/agui-author

Context preview

The summary Claude sees to decide when to auto-load this skill.

Author live dashboard UI from an agent via the `emit_ui` MCP tool. Emit

SKILL.md

agui-author.SKILL.md
name: agui-author
description: Author live dashboard UI from an agent via the `emit_ui` MCP tool. Emit
  one of six allow-listed components (approval_card, choice_prompt, diff_summary,
  progress, metric, agent_card) with JSON props and it renders in any AG-UI client
  watching the fleet. Use when you want the operator to see a decision, a diff, or a
  status readout instead of scrolling terminal text. Arbitrary HTML/markup is refused.

Authoring generative UI over AG-UI

CAO exposes an **AG-UI** stream (`GET /agui/v1/stream`) that any dashboard — CopilotKit, the AG-UI Dojo, or a plain `EventSource` — renders without CAO-specific code. As an agent you can push a **declarative UI intent** onto that stream with the `emit_ui` MCP tool. The operator sees a rendered card, not raw text — and because every provider's intents render uniformly, they can't tell (and don't need to) which CLI agent produced which card.

The surface must be enabled on the server (`CAO_AGUI_ENABLED=true` or `CAO_MCP_APPS_ENABLED=true` — the two surfaces share one event source). When it is disabled, `emit_ui` returns `{"ok": false, "reason": "AG-UI surface disabled…"}` — treat that as a no-op, not an error.

Safety model (why this is always safe to call)

You may emit **only** a closed allow-list of named components with JSON props. There is **no HTML, no script, no `eval`, no iframe**. The intent is validated **server-side** against the allow-list before it reaches the stream:

  • An **off-list** component (e.g. `iframe`, `script`) is **refused** — the tool

raises a `ValueError`; nothing is rendered.

  • `props` must be **JSON-serializable** and are **bounded to 8 KB** — an oversized

or non-serializable payload is **rejected** at the `emit_ui` boundary (HTTP 400, the tool raises a `ValueError`), so a bad payload never reaches the bus.

  • If the AG-UI surface is disabled on the server, the tool **degrades gracefully**

(no error) — so calling it is never fatal.

  • The AG-UI stream is **metadata-only by contract**: never put message bodies,

credentials, or file contents in props. Reference paths, not contents.

The tool

emit_ui(component: str, props: dict) -> {"ok", "event_id", "component"}

`component` must be one of: `approval_card`, `choice_prompt`, `diff_summary`, `progress`, `metric`, `agent_card`.

When to use which component

Props below are what a conformant client renderer will display; unknown extra keys are ignored, not refused.

| Component | Use it when… | Props | |---|---|---| | `approval_card` | you need a human to approve/reject a risky action before you proceed | `title` (str), `detail` (str, optional), `risk` (`"low"`/`"medium"`/`"high"`, optional) | | `choice_prompt` | you want the operator to pick among options | `question` (str), `choices` (list of `{"label", "value"}` or plain strings) | | `diff_summary` | you changed files and want a compact review | `title` (str), `files` (list of `{"path", "additions", "deletions"}`) | | `progress` | a long step is running | `label` (str), `value` (0.0–1.0; omit for an indeterminate bar) | | `metric` | you want to surface a single number | `label` (str), `value` (str/number), `unit` (str, optional) | | `agent_card` | you want to advertise your identity/status in the fleet view | `name` (str), `provider` (str), `status` (str, optional) |

Examples

# Gate a risky action on human approval.
emit_ui("approval_card", {
    "title": "Deploy to production?",
    "detail": "3 files changed, 1 DB migration",
    "risk": "high",
})

# Ask the operator to choose.
emit_ui("choice_prompt", {
    "question": "Which base branch?",
    "choices": [{"label": "main", "value": "main"},
                {"label": "release", "value": "release"}],
})

# Summarize a change set.
emit_ui("diff_summary", {
    "title": "Refactor auth",
    "files": [{"path": "security/auth.py", "additions": 74, "deletions": 3}],
})

# Show progress / a metric / your identity.
emit_ui("progress", {"label": "Indexing repository", "value": 0.42})
emit_ui("metric", {"label": "tokens used", "value": 12840, "unit": "tok"})
emit_ui("agent_card", {"name": "reviewer", "provider": "claude_code", "status": "working"})

L2 constructs (Phase 2)

The AG-UI surface also exposes **L2 constructs** — higher-level projections that fold the raw event stream into structured views. As an agent you don't author L2 constructs, but you should know they exist because your `emit_ui` intents feed them:

  • **`SupervisorDashboardStream`** — folds `STATE_SNAPSHOT`/`STATE_DELTA` + your

`agent_card` emits into a live fleet hierarchy view.

  • **`MultiAgentSessionTimeline`** — reconstructs delegation/message timeline

from `TOOL_CALL` lifecycle events.

  • **`AgentHandoffWithApproval`** — the full interrupt lifecycle: provider prompt

→ reason classification → interrupt → approve/deny/edit → delivery.

  • **`CrossProviderStateSync`** — convergence proof across providers.

The **run plane** (`POST /agui/v1/run`) streams these as stock AG-UI wire frames. Interrupts (approval prompts) route through `POST /agui/v1/interrupts/{id}/resume`.

For details: [references/l2-constructs.md](references/l2-constructs.md) and [references/run-plane.md](references/run-plane.md).

Gotchas

1. **Emitting to a disabled surface** — if `CAO_AGUI_ENABLED` is unset, `emit_ui` returns `{"ok": false}` gracefully. Don't treat this as an error or retry — it's a no-op by design. The fix: always check `ok` in the return but never fail on it.

2. **Props over 8 KB are rejected** — the tool raises a `ValueError` and nothing renders. The fix: reference file paths instead of embedding content. Keep props to metadata (paths, counts, labels).

3. **No HTML sink exists** — strings in props render as plain text. Attempting to smuggle markup through props (e.g. `<script>`, `<iframe>`) won't render and looks broken. The fix: use structured props, not markup.

4. **One intent per meaningful moment** — emitting

Read more
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CLI Agent Orchestrator (CAO) coordinates multiple AI coding CLIs so a supervisor can delegate work to specialist agents in parallel or sequence. 📚 Documentation — guides, reference, and two interactive courses.

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