agentleFS
Google CloudKnown publisherGoogleCloudPlatform/devrel-demos/agents/elevating-antigravity/skills/multi-step-workflow-reporter/SKILL.md

multi-step-workflow-reporter

Skill397 starsChanged 9 days agoApache-2.0View on GitHub

Orchestrates parallel worker subagents executing a 4-step workflow, streaming mid-flight milestone progress reports back to the parent orchestrator via send_message. Use when running multi-step pipeline tasks with live progress reporting.

The file

Files
SKILL.md

Multi-Step Workflow Reporter

Orchestrates multi-agent parallel workflows with real-time mid-flight milestone reporting via send_message.


Workflow Steps

1. Register Custom Worker Persona (define_subagent)

Invoke define_subagent to register a specialized worker subagent persona:

  • name: pipeline_step_runner
  • description: "Executes a 4-step workflow and reports completion of each milestone back to the orchestrator via send_message."
  • system_prompt: "You are a pipeline step runner subagent. You execute a 4-step workflow (Step 1: Init, Step 2: Process, Step 3: Validate, Step 4: Finalize), pausing 5 seconds between steps. After successfully completing EACH step, call send_message to transmit a progress report to the Parent Orchestrator ID provided in your task prompt. Format your payload as structured JSON: {"worker": "<Role>", "step": <step_number>, "name": "<Step Name>", "status": "COMPLETE"}."
  • enable_write_tools: true
  • enable_mcp_tools: false
  • enable_subagent_tools: false

2. Dispatch Parallel Workers (invoke_subagent)

Retrieve the parent orchestrator's conversation ID (e.g. parent-123).

Dispatch 3 worker subagents concurrently in a single invoke_subagent call using TypeName: "pipeline_step_runner". For each worker (Dataset Ingestion Worker, Image Processing Worker, Model Inference Worker), set Role to the worker name and Prompt to:

"Execute your assigned pipeline for '<Role>'. Transmit step completion payloads via send_message to Recipient '<parent_id>'."


3. Handle Incoming Telemetry & Update Live Dashboard

As send_message events arrive reactively from active subagents:

  1. Parse the incoming JSON telemetry payload.
  2. Render or update a consolidated live status dashboard in the chat window:
⏳ **Live Pipeline Execution Dashboard**

* **Dataset Ingestion Worker**: [██████████░░░░░░░░░░] Step 2/4 Complete (Process)
* **Image Processing Worker**:  [█████░░░░░░░░░░░░░░░] Step 1/4 Complete (Init)
* **Model Inference Worker**:   [███████████████░░░░░] Step 3/4 Complete (Validate)

4. Aggregate & Report Final Results

Upon receiving Step 4 completion callbacks from all background subagents:

  1. Synthesize final metrics and duration totals.
  2. Present a final completion summary to the user with actionable next steps.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.