server-lifecycle

Name: server-lifecycle
Availability: InStock
Author: ishandhanani

Automate inference server lifecycle management with cleanup, launch, health checks, and benchmarking.

3|2|Updated Sep 3, 2025

Search Tags:#automation #server #benchmark #lifecycle #inference #health-check

Ready to run

npx skills add https://github.com/ishandhanani/dotfiles --skill server-lifecycle-ishandhanani

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: server-lifecycle
Source: https://github.com/ishandhanani/dotfiles/tree/main/agents/skills/server-lifecycle
Command: npx skills add https://github.com/ishandhanani/dotfiles --skill server-lifecycle-ishandhanani

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates end-to-end management of an inference server lifecycle, reducing manual setup, teardown, and inconsistent benchmarking across runs.

Core Features & Use Cases

Coordinated lifecycle management: clean environment, launch server, verify health, run benchmarks, collect results, and perform cleanup.
Cross-backend compatibility: works with common inference servers (SGLang, vLLM, TRT-LLM) and derives project-specific args from CLAUDE.md or AGENTS.md for repeatable experiments.

Quick Start

Launch a lifecycle run for your project by following the steps in this skill.

Frequently Asked Questions about server-lifecycle

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

FAQPage Schema

How do I automate inference server lifecycle management for vLLM or SGLang?▼

Inference server lifecycle management is automated by orchestrating environment cleanup, server launch, health verification, benchmark execution, results collection, and safe teardown. It applies deterministic workflows across backends like vLLM, SGLang, and TRT-LLM using project-specific arguments.

How do I ensure consistent benchmarking across multiple inference server runs?▼

Consistent benchmarking across inference server runs is achieved by sourcing project-specific arguments from CLAUDE.md or AGENTS.md files. This deterministic workflow ensures repeatable experiments by applying identical configurations during server launch and benchmark execution phases.

Does this lifecycle automation tool support TRT-LLM and custom launch scripts?▼

TRT-LLM is supported alongside vLLM and SGLang for cross-backend compatibility. The lifecycle automation supports optional custom launch scripts when project complexity warrants them, deriving arguments from CLAUDE.md or AGENTS.md memory indexes for proper execution.

What is the best way to handle inference server teardown and health checks?▼

The best way to handle inference server teardown and health checks is using a coordinated lifecycle workflow. It verifies server health after launch, executes benchmarks, aggregates results, and performs safe teardown to ensure clean environment cleanup and prevent resource conflicts.

Do I need CLAUDE.md or AGENTS.md files to run server lifecycle automation?▼

CLAUDE.md or AGENTS.md files are required to provide project-specific arguments for the server lifecycle automation. These memory indexes supply the deterministic configurations needed for environment setup, server launch, and benchmark execution across various inference backends.