Evaluate AI agent behavior with deterministic test suites and AI-assisted rubrics.
npx skills add https://github.com/pynbj1001/alpha-sense --skill evals-pynbj1001
Please help me install this Agent Skill. Skill: Evals Source: https://github.com/pynbj1001/alpha-sense/tree/main/.pai_runtime/.claude/skills/Evals Command: npx skills add https://github.com/pynbj1001/alpha-sense --skill evals-pynbj1001
Evals provides a comprehensive, repeatable framework to evaluate AI agents across prompts, models, and workflows, including transcript capture, pass@k metrics, and ALGORITHM integration.
Run a quick evaluation by selecting a use case and executing a single run via the EvalServer CLI.
High-intent search queries and answers about installing and using this skill.
Evaluating AI agent behavior for regression testing requires deterministic test suites and AI-assisted rubrics to generate reproducible assessments. This framework uses multiple grading strategies, task pipelines, and saturation monitoring to ensure reliable model comparisons.
Comparing AI models using reproducible experiments involves executing end-to-end evaluations with versioned prompts and golden outputs. This approach enforces governance through saturation monitoring and auditable outputs to produce science-backed model selection decisions.
Code-based, model-based, and human graders are supported for AI testing through deterministic scoring mechanisms. This framework evaluates AI agent capacity by capturing transcripts and applying pass@k metrics to ensure comprehensive assessment across use cases.
Running a quick evaluation for an AI agent use case requires selecting a target scenario and executing a single run via the EvalServer CLI. This process evaluates tasks, prompts, and golden outputs to generate reproducible results and saturation monitoring data.
AI agent evaluation works without external dependencies, utilizing built-in task pipelines and result storage to generate assessments. The framework enforces governance through saturation monitoring, versioned prompts, and auditable outputs to ensure reproducible capacity testing.
Saturation monitoring for AI capacity testing is needed to enforce governance and detect performance plateaus across model comparisons. This mechanism works with versioned prompts and auditable outputs to ensure reproducible regression testing and science-backed decisions.
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