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foundry-evaluations
Evaluate agent quality using Foundry OpenAI Evals API. Create evaluations, run them against models, and analyze results.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
メニュー
Evaluate agent quality using Foundry OpenAI Evals API. Create evaluations, run them against models, and analyze results.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Pair with a kars cluster and offload heavy tasks to governed cloud sandboxes with GPU / foundation-model inference / Azure AI services, or communicate with other agents over end-to-end encrypted AgentMesh. Triggers on natural-language intents like "offload to the cloud", "run this on Azure", "ask my cluster to…", "send a message to agent X", "who is on the mesh", "check my inbox", "is my offload done".
Behavioral governance for OpenClaw agents via AGT — tool-level policy, inter-agent trust, audit logging.
Spawn secure isolated sub-agent sandboxes, delegate tasks via AGT mesh, receive results, and destroy sub-agents. Uses the kars_spawn, kars_mesh_send, kars_mesh_inbox, and kars_spawn_destroy tools.
Query and inspect Foundry prompt agents and invoke Foundry tools via the Responses API. OpenClaw is the orchestrator — Foundry provides managed AI services.
Python code execution via Azure AI Foundry Responses API with code_interpreter tool. Data analysis, charts, and math in a managed sandbox.
Manage persistent conversations via Foundry Conversations API. Create conversations, add messages, and maintain history across sessions.
| name | foundry-evaluations |
| description | Evaluate agent quality using Foundry OpenAI Evals API. Create evaluations, run them against models, and analyze results. |
| metadata | {"openclaw":{"requires":{"env":["FOUNDRY_PROJECT_ENDPOINT"]},"primaryEnv":"FOUNDRY_PROJECT_ENDPOINT"}} |
You can evaluate agent and model quality using the Foundry OpenAI Evals API. Create evaluation definitions with testing criteria, run them against models, and analyze pass/fail results.
All requests: http://localhost:8443 with ?api-version=2025-11-15-preview. Auth is automatic.
curl -s 'http://localhost:8443/openai/evals?api-version=2025-11-15-preview'
curl -s -X POST 'http://localhost:8443/openai/evals?api-version=2025-11-15-preview' \
-H 'Content-Type: application/json' \
-d '{"name":"quality-check","data_source_config":{"type":"custom","item_schema":{"type":"object","properties":{"input":{"type":"string"},"expected":{"type":"string"}},"required":["input","expected"]}},"testing_criteria":[{"type":"string_check","name":"exact-match","input":"{{sample.output_text}}","reference":"{{item.expected}}","operation":"eq"}]}'
curl -s -X POST 'http://localhost:8443/openai/evals/eval_abc123/runs?api-version=2025-11-15-preview' \
-H 'Content-Type: application/json' \
-d '{"name":"run-1","data_source":{"type":"jsonl","source":{"type":"file_content","content":[{"item":{"input":"2+2","expected":"4"}}]}}}'
curl -s 'http://localhost:8443/evaluators?api-version=2025-11-15-preview'
curl -s 'http://localhost:8443/evaluationrules?api-version=2025-11-15-preview'