lockfile

Generate a TOML-based llmring.lock with semantic aliases, environment profiles, and model fallbacks.

3|Updated Aug 17, 2025
One-click install
npx skills add https://github.com/juanre/llmring --skill lockfile
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: lockfile
Source: https://github.com/juanre/llmring/tree/main/skills/lockfile
Command: npx skills add https://github.com/juanre/llmring --skill lockfile

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Hardcoding LLM model IDs makes applications brittle, difficult to update, and expensive to manage across different environments (dev, staging, prod). This Skill centralizes model configuration, making your AI applications more flexible and maintainable.

Core Features & Use Cases

  • Semantic Aliases: Map task-based names (e.g., "summarizer", "code-reviewer") to specific LLM models, allowing easy model switching without code changes.
  • Environment Profiles: Configure different models for development, staging, and production environments to optimize cost and performance for each stage.
  • Automatic Fallbacks: Define a chain of models for an alias, ensuring high availability and resilience against provider outages or rate limits by automatically trying alternatives.
  • Use Case: Switch your application from a cheap development model to a high-quality production model by simply changing an environment variable, or automatically fall back to a different provider if the primary one is unavailable, ensuring continuous operation.

Quick Start

Initialize your project's lockfile using 'llmring lock init', then use 'llmring lock chat' for conversational setup of aliases like 'summarizer' and 'analyzer'.

Frequently Asked Questions about lockfile

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

FAQPage Schema
How do I manage different LLM models across development, staging, and production environments?

Create environment profiles in a TOML-based lockfile to configure different models for each stage. Use `llmring lock init` to generate a project-specific llmring.lock file, then define aliases and environment-specific model bindings to switch models by changing environment variables without modifying code.

Can I set up automatic fallback models if my primary LLM provider is unavailable?

Yes. Define a fallback chain for each alias in your lockfile to specify alternative models. The system automatically tries the next model in the chain if the primary provider experiences outages or rate limits, ensuring continuous operation across providers.

How do I use semantic aliases instead of hardcoding model IDs in my application?

Map task-based names like 'summarizer' or 'code-reviewer' to specific models in your lockfile using `llmring lock chat` for conversational setup. Reference aliases by name in your code, allowing you to swap underlying models without changing application logic.

What file format does the lockfile use and how is it resolved?

The lockfile uses TOML format and is resolved either via explicit path or environment variable. This enables flexible configuration management across projects and libraries while maintaining consistent model bindings and profile settings.

Can I configure and update my lockfile interactively without editing TOML directly?

Yes. Use `llmring lock chat` to set up aliases, bindings, and profiles conversationally. The Skill provides both CLI and Python APIs for creating, binding, and listing configurations, supporting both interactive and programmatic approaches.

Do I need prior CLI experience to set up model aliases and environment profiles?

No. The conversational CLI interface (`llmring lock chat`) guides you through alias and profile setup step by step. Start with `llmring lock init` to scaffold a new lockfile, then use the interactive mode to configure your specific requirements.