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model-service

Configure YAML pipelines to call LLM APIs with environment variables.

Updated Aug 23, 2026
Search Tags:#llm#yaml#config#pipeline#providers#api-calling
One-click install
npx skills add https://github.com/AKCqhzdy/dse-subject-grading --skill model-service
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: model-service
Source: https://github.com/AKCqhzdy/dse-subject-grading/tree/main/.github/skills/model-service
Command: npx skills add https://github.com/AKCqhzdy/dse-subject-grading --skill model-service

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Use this skill whenever the task is to call LLM APIs through this repository by writing or adjusting model-service YAML config and running the existing pipeline. This skill should trigger for requests like "call model/api", "write yaml config", "run model services", "test provider/model route", "send prompt/files to llm", or OpenRouter/Gemini/Kimi/Aliyun/Seed invocation. Strong rule: in this skill, modify YAML config only and do not modify Python scripts.

Core Features & Use Cases

  • YAML-based configuration for model-service pipelines to drive API calls across providers (openrouter, gemini, kimi, aliyun, seed)
  • Environment-driven credentials: URLs and keys sourced from env.txt and referenced via $VAR or ${VAR}
  • Guardrails: if a provider's credentials resolve to empty, it is treated as unsupported in the current environment
  • Safe boundaries: editing YAML config only; Python scripts are not modified

Quick Start

Edit the YAML config at .github/scripts/configs/experiments/model_services_template.yaml and run the provided Python snippet to load the config and execute the pipeline.

Frequently Asked Questions about model-service

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

FAQPage Schema
How do I configure LLM API calls using YAML config files?▼

YAML config files drive LLM API calls by defining pipelines for providers and models. You edit the YAML configuration and run the existing pipeline to execute API requests without modifying Python scripts.

How do I securely manage API keys when running model service pipelines?▼

Secure API key management uses environment variables referenced as $VAR or ${VAR} in the YAML config. Credentials are sourced from the environment, preventing hardcoded secrets in the repository.

What happens if an LLM provider's credentials are missing in the environment?▼

If an LLM provider's credentials resolve to empty, the provider is treated as unsupported in the current environment. This guardrail prevents failed API calls and maintains pipeline stability.

Can I test different LLM providers like OpenRouter, Gemini, and Kimi through YAML configuration?▼

Yes, you can test providers like OpenRouter, Gemini, Kimi, Aliyun, and Seed by adjusting the YAML config to define routes and models. Running the pipeline validates the API calls for each provider.

Do I need to modify Python scripts to run model service pipelines?▼

No, you do not need to modify Python scripts. The skill enforces safe boundaries by restricting modifications to YAML config files only, exposing a defined interface to run the pipeline.

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