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Acceptance Orchestrator Skill · Devops

Acceptance Orchestrator: End-to-End Coding Automation

Automate end-to-end coding tasks with 2 max iteration rounds.

Achieve DoD proven results with minimal human re-intervention. Start orchestrating today!

  • State Machine
  • Closed-Loop
  • Verification
  • Automation
  • Orchestration

About This Skill

Master the art of 'DoD proven' development. This skill utilizes 2 max iteration rounds and a structured state machine to drive tasks from intake through final verification with minimal human intervention.

Quick Start

  1. 1Define your issue ID and acceptance criteria (DoD)
  2. 2Configure the target environment (default is 'dev')
  3. 3Trigger the orchestrator to begin the intake phase
Example Command
orchestrate --issue=ID-402 --dod='passed-tests' --env=dev

Core Capabilities

Overview

Orchestrate coding work as a state machine that ends only when acceptance criteria are verified with evidence.

When to Use

Ideal for tasks requiring structured handoff across implementation, review, deployment, and final verification.

Required Sub-Skills

Leverages create-issue-gate, closed-loop-delivery, and verification-before-completion.

Workflow

A multi-stage process covering Intake, Issue gate, Execute, and Review loop.

Usage Examples

Before

Manual tracking of PR feedback and deployment status.

After

Automated polling and verification of DoD criteria.

Before

Developer waits for manual review of docs.

After

Orchestrator polls PR feedback at 3m, 6m, and 10m intervals.

Before

Manual execution of migration scripts.

After

Closed-loop delivery ensures implementation and local verification.

SKILL.md

---
name: acceptance-orchestrator
description: Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.
risk: safe
source: community
date_added: "2026-03-12"
---

# Acceptance Orchestrator

## Overview

Orchestrate coding work as a state machine that ends only when acceptance criteria are verified with evidence or the task is explicitly escalated.

Core rule: **do not optimize for "code changed"; optimize for "DoD proven".**

## When to Use
- The task already has an issue or clear acceptance criteria and should run end-to-end with minimal human re-intervention.
- You need structured handoff across implementation, review, deployment, and final verification.
- You want explicit stop conditions and escalation instead of silent partial completion.

## Required Sub-Skills

- `create-issue-gate`
- `closed-loop-delivery`
- `verification-before-completion`

Optional supporting skills:
- `deploy-dev`
- `pr-watch`
- `pr-review-autopilot`
- `git-ship`

## Inputs

Require these inputs:
- issue id or issue body
- issue status
- acceptance criteria (DoD)
- target environment (`dev` default)

Fixed defaults:
- max iteration rounds = `2`
- PR review polling = `3m -> 6m -> 10m`

## State Machine

- `intake`
- `issue-gated`
- `executing`
- `review-loop`
- `deploy-verify`
- `accepted`
- `escalated`

## Workflow

1. **Intake**
   - Read issue and extract task goal + DoD.

2. **Issue gate**
   - Use `create-issue-gate` logic.
   - If issue is not `ready` or execution gate is not `allowed`, stop immediately.
   - Do not implement anything while issue remains `draft`.

3. **Execute**
   - Hand off to `closed-loop-delivery` for implementation and local verification.

4. **Review loop**
   - If PR feedback is relevant, batch polling windows as:
     - wait `3m`
     - then `6m`
     - then `10m`
   - After the `10m` round, stop waiting and process all visible comments toge

Frequently Asked Questions

FAQ

What tool compatibility does this skill have?
It is compatible with any Git-based workflow and supports standard issue trackers providing issue IDs and bodies.
Who is the target audience for this skill?
It is designed for DevOps engineers, AI Agent developers, and software engineers looking to automate the 'Definition of Done'.
How does this differ from standard CI/CD pipelines?
Unlike standard CI/CD which focuses on 'code changed', this orchestrator optimizes for 'DoD proven' through active state machine management and feedback polling.
Does it support different language environments?
Yes, it is language-agnostic as long as the implementation can be verified via the provided sub-skills and target environment.
What are the expected results of using this skill?
You can expect a significant reduction in human re-intervention during the implementation, review, and deployment phases.

Discussion

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Trigger Phrases

Use these phrases to activate this skill in your AI coding assistant:

drive end-to-endminimize human re-interventionoptimize for DoD provenverify with evidencestructured handoff