edge-ocp-ci-generate-dashboard

name: edge-ocp-ci:generate-dashboard description: "Edge OCP Payload Monitor — monitor OpenShift nightly payloads for edge topology (SNO/TNA/TNF) failures with AI-enriched analysis" argument-hint: "[--versions 4.18,4.19,4.20,4.21,4.22,4.23,5.0] [--payloads N] [--skip-prow] [--skip-sippy] [--with-timing]" user-invocable: true

Edge OCP Payload Monitor Skill

You are helping a developer monitor OpenShift nightly payload health for edge topologies (SNO, TNA, TNF). This skill orchestrates the payload-monitor Python tool and existing marketplace CI skills to generate an interactive HTML dashboard report with AI-powered root cause analysis for blocking job failures.

Existing Skills Reference

This skill composes with the following installed marketplace CI skills from the ai-helpers repository. These are used automatically for blocking job analysis:

Data Fetching Skills

Deep Analysis Skills

Action Skills

Workflow

Step 1: Parse Arguments

Parse $ARGUMENTS to determine options:

• --versions X,Y,Z: Override which OCP versions to monitor (e.g., --versions 4.18,4.19)
• --payloads N: Number of payloads to analyze per stream (1-10, default 5)
• --skip-prow: Skip Prow artifact fetching (faster, less detail)
• --skip-sippy: Skip Sippy regression check
• --with-timing: Include install/upgrade timing insights (disabled by default)
• If $ARGUMENTS is empty: use defaults (all configured versions)

Step 2: Install Prerequisites (if needed)

The tool uses a virtual environment at $TOOL_DIR/.venv. Create it and install dependencies if not already present:

PLUGIN_DIR is the directory containing this skill file (i.e., plugins/edge-ocp-ci/skills/generate-dashboard).

TOOL_DIR="$(git -C "$PLUGIN_DIR" rev-parse --show-toplevel)/payload-monitor"

cd "$TOOL_DIR" && (test -d .venv || python3 -m venv .venv) && .venv/bin/python -c "import requests, jinja2, click" 2>/dev/null || .venv/bin/pip install -r requirements.txt

This avoids re-creating the venv or re-running pip install on every invocation when the environment is already set up.

Step 3: Run the Python Tool

Run the payload monitor Python tool to collect data and generate the base report:

cd "$TOOL_DIR" && .venv/bin/python -m payload_monitor --output reports/report-$(date +%Y-%m-%d).html [OPTIONS]

Pass through any relevant flags (--versions, --payloads, --skip-prow, --skip-sippy, --with-timing).

Important: If a report with the same filename already exists, the tool automatically appends a timestamp (e.g., report-2026-03-25-143027.html). Capture the actual output path from the tool's log line:

• Report: /path/to/report-{name}.html

Use this actual path (not the hardcoded date-based name) in all subsequent steps.

The tool outputs:

• An HTML report (self-contained interactive dashboard)
• Blocking job summary printed to stdout (pipe-delimited lines between BLOCKING_JOBS_START and BLOCKING_JOBS_END markers)

Step 4: Parse Blocking Jobs from Output and Analyze Failures

Parse the blocking job lines from the tool's stdout. Each line between the markers has the format:

BLOCKING|job_name|prow_url|topology|version|payload_tag

If no BLOCKING_JOBS_START marker appears in the output, there are no blocking failures — skip to Step 6.

For informing job failures: Do NOT run deep analysis. The HTML report already includes a suggestion to use Claude directly with /ci:prow-job-analyze-test-failure <prow-url>.

Analysis Prompt

Use the following prompt for each blocking job. When there is exactly 1 blocking failure, run it directly in the main agent (no subagent). When there are 2 or more blocking failures, spawn one subagent per job using the Agent tool, all in parallel — this significantly reduces wall-clock time.

Analyze this failing blocking edge job and return a JSON deep_analysis object.

Job: {job_name}
Prow URL: {prow_url}
Topology: {topology}
Version: {version}
Payload: {payload_tag}

Steps:
1. Use `ci:fetch-job-run-summary` with the Prow URL to get all failed tests grouped by SIG
2. Use `ci:fetch-prowjob-json` with the Prow URL to get job metadata

Then based on failure type:
- If install failure (error contains "install should succeed", "bootstrap", or failed in pre/setup phase):
  Use `ci:prow-job-analyze-install-failure` (or `ci:prow-job-analyze-metal-install-failure` if job name contains "metal")
- If test failure (job passed install but failed during test phase):
  Use `ci:prow-job-analyze-test-failure`
- If resource/state failure (etcd issues, operator degraded, node not ready):
  Use `ci:prow-job-analyze-resource`

For JIRA context: use `ci:fetch-jira-issue` for any linked bugs.

Return ONLY a JSON object with these fields:
{
  "prow_url": "{prow_url}",
  "root_cause": "One-sentence explanation",
  "failure_type": "Infrastructure flake | Test regression | Install failure | Platform issue",
  "impact": "How this affects payload acceptance and which topologies",
  "suspect_prs": ["https://github.com/org/repo/pull/123"],
  "recommendation": "Specific next action"
}

When using subagents, launch all in parallel using multiple Agent tool calls in the same response. Collect their results and proceed to Step 5.

Subagent configuration: When spawning subagents, set a timeout of 5 minutes per agent. If a subagent times out or returns an error:

1. Record the failure in the analysis JSON with a descriptive error:

   {
     "prow_url": "{prow_url}",
     "root_cause": "Analysis timed out or failed: {error_message}",
     "failure_type": "Analysis error",
     "impact": "Unable to determine — manual investigation needed",
     "suspect_prs": [],
     "recommendation": "Run /ci:prow-job-analyze-test-failure {prow_url} manually for detailed analysis"
   }
   

2. Continue with results from other subagents — do NOT discard partial results.

3. Include a note in the final output indicating which jobs could not be analyzed.

Step 5: Write Analysis File

Collect the deep analysis results (from subagents or inline analysis) and write a small analysis-only JSON file keyed by prow_url. Use the actual report stem from Step 3 (e.g., reports/analysis-2026-03-25.json or reports/analysis-2026-03-25-143027.json):

{
  "by_prow_url": {
    "https://prow.ci.openshift.org/view/gs/.../123": {
      "root_cause": "One-sentence explanation of the root cause",
      "failure_type": "Infrastructure flake | Test regression | Install failure | Platform issue",
      "impact": "How this affects payload acceptance and which topologies",
      "suspect_prs": ["https://github.com/org/repo/pull/123"],
      "recommendation": "Specific next action (file bug, wait for fix, investigate PR, etc.)"
    }
  }
}

This file is intentionally small — it contains only the AI analysis results, not the full report data. This minimizes token usage.

Step 6: Patch Analysis into HTML

Patch the analysis directly into the existing HTML report. Use the actual report path from Step 3:

cd "$TOOL_DIR" && .venv/bin/python -m payload_monitor \
  --merge-analysis reports/<actual-analysis>.json \
  --output reports/<actual-report>.html

This finds each job's detail section by its prow URL and injects the "AI Root Cause Analysis" card directly into the HTML. No JSON round-trip needed.

If there were no blocking failures (no analysis file), skip this step entirely — the HTML report is already complete.

Step 6b: Clean Up Analysis File

After successfully patching the analysis into the HTML report, delete the intermediate analysis JSON file:

rm "$TOOL_DIR/reports/<actual-analysis>.json"

This file has served its purpose — the analysis is now embedded in the HTML report.

Step 7: Present Output

Do NOT duplicate the report data or findings summary — the HTML dashboard already contains all of that. Present only a brief confirmation:

## Edge OCP Payload Monitor Report Generated

Report: `<actual report path captured from Step 3>`

Analyzed {N} blocking job failure(s) with AI root cause analysis.
Open the HTML report for the full interactive dashboard with findings summary, suggested actions, and detailed analysis.

Use the actual report file path captured from the tool's Report: log line in Step 3 — do NOT hardcode a date-based path.

Offer follow-up actions the user can take from this session:

• Create JIRA bugs for untracked failures
• Set release blocker on a JIRA issue (ci:set-release-blocker)
• Triage a regression in Component Readiness (ci:triage-regression)
• Trigger payload job to test a fix (ci:trigger-payload-job)
• Investigate an informing job further (ci:prow-job-analyze-test-failure)

Important Notes

• The Python tool must be run from the $TOOL_DIR directory
• Dependencies are checked and installed automatically in Step 2
• JIRA features require a JIRA_TOKEN environment variable with read-only permissions — the tool only searches for existing bugs, never creates or modifies issues
• Prow artifact fetching requires gsutil (Google Cloud SDK)
• Do NOT modify the Python source code — this skill is an orchestration layer on top
• Do NOT duplicate report data in your output — the HTML dashboard is the primary output, keep your response brief
• Deep analysis runs automatically for blocking jobs only — informing jobs get a Claude suggestion instead
• Prioritize blocking failures over informing failures in all analysis
• The dashboard automatically detects recurring (2+ payloads) and persistent (3+ payloads) failures — highlight these in your summary
• Unstable jobs are informing jobs failing in 3+ consecutive payloads — these are consistently failing and need attention
• Cross-topology correlation ("Also in: SNO/TNA/TNF" hints) surfaces shared platform issues — when you see these, investigate the shared root cause rather than each topology independently
• Every finding in the dashboard has a next step (JIRA link, Claude command, triage URL, or create-bug button) — reference these in your follow-up suggestions
• For TNF/TNA failures, pay special attention to etcd, Pacemaker, and fencing-related errors
• For SNO failures, check for single-node-specific issues like workload partitioning, resource constraints
• When multiple edge topologies fail in the same payload, investigate whether it's a shared platform issue vs topology-specific