Design, simulate, and evaluate LLM agents with pattern-matrix mode selection.
npx skills add https://github.com/northseadl/norix-skills --skill llm-agent-dev
Please help me install this Agent Skill. Skill: llm-agent-dev Source: https://github.com/northseadl/norix-skills/tree/main/llm-agent-dev Command: npx skills add https://github.com/northseadl/norix-skills --skill llm-agent-dev
This skill provides a comprehensive framework for building and evaluating LLM agent systems, enabling pattern-based design, data simulation, and convergence testing.
To start using this skill, deploy it in your agent pipeline and run a representative user prompt to trigger the recommended pattern stack and evaluation workflow.
High-intent search queries and answers about installing and using this skill.
The framework supports pattern-driven architecture selection across 12 agent modes and runs synthetic data generation pipelines to optimize agent performance.
Convergence testing for LLM agents evaluates performance across multiple versions to identify optimal configurations and guardrails. You need it when iterating on agent designs to ensure stability.
You test guardrails by running convergence testing across multiple versions. This process identifies optimal configurations and validates that guardrails perform consistently.
Yes, you can generate synthetic data for LLM agent evaluation pipelines. The framework provides data generation capabilities to simulate inputs and measure agent performance under representative conditions.
No, this framework requires no external dependencies to run agent simulations. It operates independently to deploy pattern-matrix-based mode selection, data generation, and convergence testing workflows.
The limitations of pattern-matrix-based mode selection involve being constrained to 12 predefined agent modes. Complex use cases may require manual configuration outside the provided pattern stack.
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