Build type-safe LLM applications in Rust with modular agents and tool calling.
npx skills add https://github.com/yankeeinlondon/rusty-biscuit --skill rig-yankeeinlondon
Please help me install this Agent Skill. Skill: rig Source: https://github.com/yankeeinlondon/rusty-biscuit/tree/main/.claude/skills/rig Command: npx skills add https://github.com/yankeeinlondon/rusty-biscuit --skill rig-yankeeinlondon
Rig provides a comprehensive, type-safe foundation for building robust, deterministic LLM-powered applications in Rust, enabling reliable tool calling, agent orchestration, and seamless integration with vector stores and multiple providers.
Install the Rig ecosystem, create a client, build an agent, and prompt it to perform a multi-tool workflow.
High-intent search queries and answers about installing and using this skill.
Build type-safe LLM agents in Rust by implementing a trait-driven architecture with schema-driven tool definitions and modular agent orchestration to compose complex, deterministic workflows.
Implement tool calling in a Rust LLM application using schema-driven, type-safe definitions for tool interactions. This ensures reliable execution when the LLM agent invokes external functions.
Use Rust for Retrieval-Augmented Generation by integrating vector stores and embedding models. This allows you to store and retrieve contextual data for dynamic LLM prompts.
Yes, the framework supports OpenAI-compatible APIs through its multi-provider architecture. This extensible design allows you to connect to various LLM providers seamlessly.
Orchestrate multiple LLM tools in Rust using modular agent composition. This approach allows you to build complex workflows across multiple tools deterministically.
Use a trait-driven architecture for Rust LLM apps to enforce type safety across tool definitions, embedding models, and vector stores, ensuring robust and deterministic task execution.
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nimeshgurungDesign and optimize LLM prompts with RAG and agent workflows.
