github-actions-trigger-health-audit

What This Skill Does

The github-actions-trigger-health-audit skill is a sophisticated diagnostic tool designed to improve CI/CD reliability within software engineering teams. By analyzing GitHub Actions run history, this agent identifies systemic patterns in build failures, cancellations, and timeouts. Instead of viewing failures as isolated incidents, this skill groups historical run data by repository, workflow, and trigger event. It aggregates key metrics—such as failure rates and average runtime duration—to pinpoint 'noisy' workflows that consume excessive engineering resources and hinder developer velocity.

The skill supports powerful filtering via regex, allowing teams to exclude irrelevant noise (like dependency updates or minor documentation changes) while focusing on production-critical deployment pipelines. By setting custom thresholds, you can receive warnings for flaky processes and trigger automated failures in your CI pipeline if specific thresholds are breached, acting as a quality gate for your infrastructure.

Installation

To integrate this skill into your OpenClaw environment, execute the following command in your terminal:

clawhub install openclaw/skills/skills/daniellummis/github-actions-trigger-health-audit

Ensure that you have collected relevant GitHub Action run data in JSON format within your workspace, typically organized in an artifacts folder, before running the audit script.

Use Cases

1. Pipeline Optimization: Identify which specific event triggers (e.g., 'pull_request' vs 'schedule') result in the highest failure rate to optimize test suites.
2. Quality Gates: Automatically block releases or deployments if the health audit detects that critical workflows have crossed a 40% failure threshold.
3. Flaky Test Reduction: Identify workflows that consistently time out or fail intermittently, signaling a need for test parallelization or environment stability improvements.
4. Resource Management: Monitor the average runtime of workflows to flag performance regressions that contribute to increased infrastructure costs.

Example Prompts

1. "OpenClaw, audit my GitHub Actions runs in artifacts/ and tell me which workflows are failing more than 30% of the time."
2. "Run a health check on all repository workflows, filter out the 'dependabot' event, and report the results as a JSON object."
3. "Execute a critical failure gate check for the last 50 runs. If any workflow has a critical failure rate, exit with a status of 1 to halt my deployment script."

Tips & Limitations

• Data Consistency: Ensure that all JSON run exports utilize the schema provided by gh run view. Missing fields may result in inaccurate calculations.
• Sample Size: Use the MIN_RUNS parameter effectively; very small sample sizes can lead to misleading failure percentages due to outliers.
• Regex Power: Leverage the WORKFLOW_MATCH and REPO_MATCH fields to narrow the scope of analysis when working in large monorepos with hundreds of disparate workflow files.
• Security: The skill reads local files generated from your GitHub environment. Ensure that your exported JSON files do not contain sensitive environment variables or secrets accidentally included in the GitHub Action run logs.