acceptance-orchestrator

Solid

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.

AI & Automation 131 stars 27 forks Updated 1 weeks ago MIT

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Quality Score: 91/100

Stars 20%
71
Recency 20%
90
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

# 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 verifi...

Details

Author
lingxling
Repository
lingxling/awesome-skills-cn
Created
3 months ago
Last Updated
1 weeks ago
Language
Python
License
MIT

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