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foundry-evaluations
Evaluate agent quality using Foundry OpenAI Evals API. Create evaluations, run them against models, and analyze results.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Evaluate agent quality using Foundry OpenAI Evals API. Create evaluations, run them against models, and analyze results.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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'