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agentops-report

Read, regenerate, and explain AgentOps release-gate reports. Trigger on "show report", "explain scores", "regenerate report", "what do these metrics mean", "where is the proof". Operates on results.json and report.md produced by `agentops eval run`.

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Azure/agentops
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2026년 6월 4일 18:53
감지된 SKILL.md 언어
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SKILL.md
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name
agentops-report
description
Read, regenerate, and explain AgentOps release-gate reports. Trigger on "show report", "explain scores", "regenerate report", "what do these metrics mean", "where is the proof". Operates on results.json and report.md produced by `agentops eval run`.
# AgentOps Report Help the user understand a finished AgentOps run and the evidence it provides for the release decision. Reports explain the repo-side gate; they do not replace Foundry Evaluations, Traces, or Monitor drilldown. ## Step 0 - Locate the run Latest run: `.agentops/results/latest/`. Each run produces: - `results.json` - machine-readable metrics, per-row scores, thresholds. - `report.md` - human-readable summary suitable for PR comments. - `cloud_evaluation.json` (when Foundry visibility is enabled) - deep-link to the Foundry Evaluations panel. `mode: classic` when `execution: local` and `publish: true` upload metrics to Classic Foundry; `mode: cloud` when `execution: cloud` runs server-side via the OpenAI Evals API. ## Step 1 - Regenerate report.md if needed ```bash agentops report generate # uses .agentops/results/latest/results.json agentops report generate --in <results.json> --out <report.md> ``` `report generate` always reads the flat 1.0 results schema and emits Markdown. There is no HTML format. ## Step 2 - Explain the metrics Common metrics and their meaning: | Metric | Range | Higher is better? | Notes | |---|---|---|---| | `similarity` | 1-5 | yes | LLM-judged similarity to `expected`. | | `coherence` | 1-5 | yes | Answer is internally consistent. | | `fluency` | 1-5 | yes | Natural language quality. | | `groundedness` | 1-5 | yes | Answer is supported by `context` (RAG). | | `relevance` | 1-5 | yes | Answer is on-topic for `input`. | | `f1_score` | 0-1 | yes | Token overlap with `expected`. | | `tool_call_accuracy` | 0-1 | yes | Predicted tool calls match `tool_calls`. | | `intent_resolution` | 0-1 | yes | User intent was resolved. | | `task_completion` | 0-1 | yes | Multi-step task finished. | | `avg_latency_seconds` | seconds | no | Wall-clock latency per row. | Pass/fail rows are derived from `thresholds:` in `agentops.yaml`. The exit code of the original run reflects the gate: - `0` → all thresholds passed - `2` → one or more thresholds failed - `1` → runtime error ## Step 3 - Help the user act on results - For low scores on a specific metric, point at the lowest-scoring rows in `results.json` (`row_metrics[]` and `item_evaluations[]`) and suggest concrete prompt or retrieval changes. - For latency regressions, look at `run_metrics.avg_latency_seconds` and per-row latency. - To compare a new run against a previous one, re-run with `agentops eval run --baseline <previous-results.json>` and explain the generated **Comparison vs Baseline** section. - For production evidence, open `.agentops/release/latest/evidence.md`. If it includes a `governance` section, explain ASSERT/ACS/red-team entries as read-only proof references (path, hash, status, ACS checkpoint coverage), not as proof that AgentOps executed those frameworks. ## Guardrails - Never invent metric values. If a metric is absent, say so. - Do not edit `results.json` by hand - re-run the eval. - Do not paste raw red-team payloads into the response; summarize only metadata and reviewer/status information.
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