test-monitor-workflow

name	test-monitor-workflow
description	Workflow for fetching, triaging, analyzing, and reporting on nightly acceptance test results across Beta and GA Google Cloud Terraform providers.

test-monitor-workflow

This document guides the agent through the task of monitoring nightly acceptance test runs.

Overview

The test-monitor workflow provides a non-destructive monitoring lifecycle that tracks nightly acceptance test execution, identifies persistent failures across a 7-day window, correlates failing tests with open GitHub issues, retrieves debug logs, and outputs a comprehensive Markdown monitoring report.

Available Skills Used in This Workflow

• fetch-nightly-results (.agents/skills/fetch-nightly-results/SKILL.md): Downloads nightly test status JSON files for Beta and GA providers from GCS (gs://nightly-test-data/test-metadata/).
• automate-test-triage (.agents/skills/automate-test-triage/SKILL.md): Triages 7-day failure trends, matches open GitHub test-failure issues, sanitizes dynamic error tokens for provider comparison, and outputs reports.

Execution Steps

1. Fetch Test Results

• Execute the fetch-nightly-results skill to retrieve test status JSON files for both GA and Beta providers from Google Cloud Storage (gs://nightly-test-data/test-metadata/) for the latest available run and past 7 days using gcloud storage cat (if GCS authentication or permission fails, verify gcloud auth login and roles/storage.objectViewer access).

2. Triage & Aggregate Failure Trends

• Execute the automate-test-triage skill by running python3 .agents/skills/automate-test-triage/scripts/triage.py from the root workspace (or pass --date YYYY-MM-DD to specify a target end date for the 7-day window).
• Group the target date's latest run failures by error signature and flag High-Impact errors based on:
  1. 🚨 Critical Severity: Provider panic/crash (panic:, runtime error:, SIGSEGV) or API enablement errors in CI test environment projects, prioritized at the top of Section 1 regardless of test count.
  2. ⚠️ High Volume: Error signatures affecting $\ge 3$ tests in the latest run.
• Separate non-actionable errors requiring human intervention (Quota / Rate Limit / Stockout, Internal Error / Error Code 13, Tenant Project Creation) into Section 2.
• Filter for persistent actionable test failures (failing in latest run AND $\ge 4$ out of the past 7 days).

3. Cross-Reference GitHub Issues

• Fetch open issues labelled test-failure from hashicorp/terraform-provider-google using gh issue list.
• Match test names against issue titles to correlate persistent failures with existing open tracking tickets.

4. Categorize Root Causes & Analyze Errors

• Extract error_message and log_link directly from test-metadata entries.
• Analyze API error payloads and debug logs to categorize the failure domain (e.g., Provider Panic, Quota Exceeded, API Permission / IAM, Model Availability, State Mismatch, Flakiness).

5. Generate Monitoring Report

• Save the final Markdown report to tmp/test-status/test-report-<date>.md (e.g., tmp/test-status/test-report-2026-07-28.md).
• Present the executive summary and 4 report sections (where every section table includes clickable GCS debug log links [Log](url), expanded 500-character error summaries, and links to Section 4) to the user: Section 1 (High-Impact Actionable Errors: Panics & API Enablement in Test Env, High Volume), Section 2 (Test Failures Requiring Human Action), Section 3 (Persistent Actionable Failures Grouped by Error Signature), and Section 4 (Detailed Test Failures Grouped by Service Package, showing all latest-run failures with failed out of total tests, percentages, and a Human Action Required? column).

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Boundaries & Guardrails

• Monitoring Only: Do NOT modify source code or template files in magic-modules.
• No Code Fixes / PRs: Do NOT create fix branches or attempt to open pull requests.