Implement self-critique loops and evaluator-optimizer pipelines for AI agent outputs.
npx skills add https://github.com/MoonAxis/azure-stack --skill agentic-eval-moonaxis
Please help me install this Agent Skill. Skill: agentic-eval Source: https://github.com/MoonAxis/azure-stack/tree/main/skills/agentic-eval Command: npx skills add https://github.com/MoonAxis/azure-stack --skill agentic-eval-moonaxis
This Skill addresses the challenge of ensuring AI-generated content meets specific quality standards by enabling iterative refinement through self-evaluation and correction.
Use the agentic-eval skill to implement a basic reflection loop for improving AI-generated text based on provided criteria.
High-intent search queries and answers about installing and using this skill.
Self-critique loops allow agents to evaluate their own outputs against defined criteria and identify areas for iterative improvement. This pattern progressively refines responses by applying feedback cycles before presenting the final result.
The LLM-as-judge approach uses a language model to evaluate AI-generated content against rubric-based criteria. It enables automated quality assurance by scoring outputs and feeding results back into evaluator-optimizer pipelines for refinement.
Test-driven code refinement workflows involve an agent writing code, evaluating it against test cases and style guides, and automatically fixing identified issues. This iterative refinement ensures complex code meets quality standards before delivery.
Yes, rubric-based evaluation measures agent response quality by checking outputs against predefined scoring criteria. This technique supports self-critique loops and helps identify specific areas for agent refinement.
No external dependencies are required to create evaluator-optimizer pipelines. The system provides self-contained patterns and techniques for implementing iterative improvement and self-evaluation directly within your agent workflows.
Iterative refinement is best used when AI-generated content must meet specific quality standards, such as complex code generation. It allows agents to progressively improve outputs based on self-evaluation feedback before final presentation.
ajilantonyEvaluates AI outputs via self-critique loops and iterative refinement.
forgivesam168Evaluate AI agent outputs using structured rubrics and JSON feedback loops.
tygwanGenerate tests, analyze coverage, and suggest test cases for codebases.
MudassarAbrarEvaluate and refine AI agent outputs through iterative generate-evaluate-critique-refine loops.
