dev-workflow

Name: dev-workflow
Availability: InStock
Author: sunchendd

Automate end-to-end validation for AI/ML inference services across hardware backends and engines.

3|1|Updated Feb 2, 2026

Search Tags:#workflow #testing #devops #validation #ai #inference #ml

Ready to run

npx skills add https://github.com/sunchendd/good_skills --skill dev-workflow-sunchendd

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dev-workflow
Source: https://github.com/sunchendd/good_skills/tree/main/dev-workflow
Command: npx skills add https://github.com/sunchendd/good_skills --skill dev-workflow-sunchendd

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Streamlines the end-to-end validation of AI/ML inference services, unifying hardware backends, inference engines, and validation steps to reduce setup time and errors.

Core Features & Use Cases

Standardizes project-level and personal Skill deployment for AI/ML services across different platforms (Ascend NPU, GPU) and engines (vLLM, MindIE).
Provides a repeatable workflow for planning, code quality checks, environment preparation, service deployment, performance testing, and results analysis.
Real-world use: teams can quickly spin up a validation pipeline to compare model performance across configurations and ensure baseline compliance.

Quick Start

Copy the dev-workflow directory into your project's Claude skills folder or your personal skills directory to activate the workflow.

Frequently Asked Questions about dev-workflow

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate end-to-end validation for AI/ML inference services?▼

Automating end-to-end validation for AI/ML inference services is done by standardizing workflows for planning, code quality checks, environment preparation, deployment, performance testing, and results analysis across different hardware and engines.

Does this deployment validation workflow support Ascend NPU and vLLM?▼

Yes, this deployment validation workflow supports Ascend NPU and GPU hardware backends, and is compatible with inference engines like vLLM and MindIE to standardize your AI/ML service deployment.

What is the best way to standardize AI inference performance testing across different hardware backends?▼

The best way to standardize AI inference performance testing across different hardware backends is to use a repeatable workflow that unifies configuration, deployment, and results analysis to ensure baseline compliance and reduce setup errors.

How do I set up a repeatable pipeline for code quality checks and ML deployment?▼

You can set up a repeatable pipeline for code quality checks and ML deployment by copying the dev-workflow directory into your project's Claude skills folder or personal skills directory to activate the standardized validation process.

Can I compare model performance across different inference configurations using this workflow?▼

Yes, you can compare model performance across different configurations because the workflow provides a repeatable pipeline for deployment and performance testing, allowing teams to quickly spin up validation and analyze the results.