Individual skill
genai-hub-prompt-evaluation
Run GenAI evaluations on SAP AI Core: benchmark LLMs and prompts against a test dataset using system-defined and custom (LLM-as-judge) metrics, then retrieve aggregate results. Supports all three evaluation types — prompt registry, orchestration registry, and grounding (RAG) orchestration registry. WHEN: user wants to evaluate or benchmark an LLM / prompt / orchestration config, run a GenAI evaluation, create or list evaluation metrics, define a custom LLM-as-a-judge metric, create a prompt template (prompt registry) or an orchestration-registry config (with or without grounding) for evaluation, create an evaluation configuration or execution, or fetch evaluation results / scores for an execution. DO NOT USE FOR: agent evaluation via the offline aeval service — use `aeval-offline-evaluation`; building a production orchestration/RAG pipeline for inference — use `genai-hub-orchestration`; generic executions, deployments, or artifacts unrelated to evaluation — use `aicore-lifecycle-management`; credentials, resource groups, or object stores — use `aicore-admin-resources`.
plugins/aicore-skills/skills/genai-hub-prompt-evaluation/SKILL.md
Source commit 82b27a81d972aa4f70dc6154605606cfed0b4772
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- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
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- Skill scan complete
- No
- Observed behavior
- Not checked yet
- Last checked
- Nov 4, 2026
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Repository context
- Repository stars (not skill ratings)
- 144
- Repository last updated
- Nov 5, 2026
- License
- Apache-2.0
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SKILL.mdcore · 13,767 bytesSKILL.mdcore · 13,767 bytesSKILL.mdcore · 13,767 bytesSKILL.mdcore · 13,767 bytesSKILL.mdcore · 13,767 bytesSKILL.mdcore · 13,767 bytesSKILL.mdcore · 13,767 bytesSKILL.mdcore · 13,767 bytesSKILL.mdcore · 13,767 bytesdata/sample_dataset.csvasset · 418 bytesdata/sample_dataset_rag.csvasset · 20,688 bytesreferences/GROUNDING_SETUP.mddoc · 3,834 bytesreferences/RESULTS_ANALYSIS.mddoc · 3,677 bytesreferences/WORKFLOW.mddoc · 8,016 bytesscripts/_rest.pyruns · 3,285 bytesscripts/create_evaluation_configuration.pyruns · 9,074 bytesscripts/create_evaluation_metric.pyruns · 5,495 bytesscripts/create_orchestration_registry_config.pyruns · 4,048 bytesscripts/create_prompt_template.pyruns · 4,811 bytesscripts/download_evaluation_results.pyruns · 5,679 bytesscripts/get_evaluation_results.pyruns · 4,003 bytesscripts/list_evaluation_metrics.pyruns · 4,083 bytesscripts/list_orchestration_registry_configs.pyruns · 3,258 bytesscripts/list_prompt_templates.pyruns · 2,992 bytesscripts/register_eval_artifact.pyruns · 4,225 bytestemplates/custom-metric.jsonasset · 2,408 bytestemplates/orchestration-registry-config.jsonasset · 700 bytestemplates/orchestration-registry-grounding-config.jsonasset · 1,413 bytestemplates/prompt-template.jsonasset · 605 bytes
Read excerpt
## Step 0 — ask for credentials first (required)
Before running any other step, confirm the AI Core credentials are set. Invoke
`aicore-admin-resources` and run `check_setup.py`.
- If it succeeds, continue to the workflow.
- If it fails (missing variables, `invalid_client`, or bad credentials), **STOP and ask the user**
to export the variables listed in **Environment variables** below. Do not continue, and do not
look for credentials or workflow steps anywhere else (notebooks, other repos, workspace files) —
this skill's `scripts/` are the only supported path.
---
## Rules