Evaluate agent and model workflows with EvalScope
Run repeatable EvalScope benchmark suites for LLM, VLM, RAG, and agent workflows, then inspect traces and reports before changing models or prompts.
npx skills add agentskillexchange/skills --skill evaluate-agent-and-model-workflows-with-evalscope
Use EvalScope when an operator needs a repeatable evaluation run before swapping models, changing prompts, or trusting an agent workflow. The agent installs EvalScope, selects datasets or agent benchmarks, points the run at an OpenAI-compatible model endpoint or local backend, executes the evaluation or stress test, and reviews scores, traces, and visual reports. Invoke this instead of using the project normally when the goal is benchmark-backed model or agent validation, not exploratory chat. The scope boundary is benchmark execution, agent evaluation mode, performance testing, and report review; it is not a generic ModelScope platform listing or a catch-all evaluation theory entry.
What this skill actually does
Inputs and prerequisites: Python 3.10+, pip, evalscope package, model endpoint or local model backend, API credentials when evaluating hosted models, selected benchmark datasets.
Setup notes: Install with pip install evalscope, configure the target model or OpenAI-compatible API endpoint, choose a benchmark dataset or agent evaluation mode, run evalscope eval or the relevant benchmark command, and inspect the generated score files, traces, and dashboard reports.
Source and verification boundary: use https://evalscope.readthedocs.io/en/latest/ as the canonical reference before running the workflow; keep commands, API calls, CLI usage, and generated outputs reviewable against that upstream source.
Framework fit: publish this as a Multi-Framework workflow only when the operator can invoke the documented toolchain directly, rather than treating the upstream project as a generic product listing.