Analyze and compare GitHub Actions runtime metrics to detect performance regressions and optimize CI costs.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install github-actions-runtime-regression-audit
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install github-actions-runtime-regression-audit using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is GitHub Actions Runtime Regression Audit?
The GitHub Actions Runtime Regression Audit is a specialized tool designed to prevent CI/CD bloat by comparing current workflow performance against established baselines. By leveraging this entry in the Openclaw Skills collection, developers can automatically identify jobs that have slowed down, ensuring that CI costs and lead times remain within acceptable limits. This skill processes raw GitHub CLI data to provide actionable insights into your automation efficiency, helping teams maintain a lean and fast development cycle.
GitHub Actions Runtime Regression Audit Use Cases
- Detecting slow-downs in CI/CD pipelines after merging large feature sets.
- Auditing GitHub Actions costs by identifying inefficient jobs that consume excessive runner minutes.
- Benchmarking workflow improvements over several weeks of historical data.
- Implementing CI gates that fail when runtime regressions exceed defined critical thresholds.
How GitHub Actions Runtime Regression Audit Works
- Export baseline run data and current run data into JSON format using the GitHub CLI (gh run view).
- Provide the file paths to the skill using the BASELINE_GLOB and CURRENT_GLOB parameters.
- The skill parses the JSON files, calculating average and p95 runtime metrics for every repository, workflow, and job.
- It performs a delta analysis, comparing the current performance against the baseline metrics.
- The tool ranks the largest regressions and flags them based on user-defined warning and critical thresholds.
- The skill generates a text or JSON report and optionally exits with a failure code if critical regressions are detected.
GitHub Actions Runtime Regression Audit Setup
To use this skill, ensure you have bash and python3 installed. You will first need to collect run data from your repositories:
gh run view <run-id> --json databaseId,workflowName,headBranch,headSha,url,repository,jobs > artifacts/github-actions/run-<run-id>.json
Once data is collected, run the audit script:
BASELINE_GLOB='artifacts/github-actions/baseline/*.json' \
CURRENT_GLOB='artifacts/github-actions/current/*.json' \
bash skills/github-actions-runtime-regression-audit/scripts/runtime-regression-audit.sh
GitHub Actions Runtime Regression Audit Data Schema & Taxonomy
The skill organizes data by calculating deltas between two sets of JSON files. It identifies jobs using a composite key of Repository + Workflow + Job Name.
| Input Parameter | Description | Default |
|---|---|---|
| BASELINE_GLOB | Glob pattern for historical run JSON files | Required |
| CURRENT_GLOB | Glob pattern for current run JSON files | Required |
| WARN_DELTA_SECONDS | Seconds over baseline to trigger a warning | 30 |
| CRITICAL_DELTA_SECONDS | Seconds over baseline to trigger a failure | 90 |
| OUTPUT_FORMAT | The reporting format (text or json) | text |
| FAIL_ON_CRITICAL | Exit with error code if critical regression found | 0 |
GitHub Actions Runtime Regression Audit Advanced Features
- Granular filtering using regex for specific repositories, workflows, and jobs to exclude noise.
- Support for p95 runtime analysis to account for CI jitter and infrastructure outliers.
- Automated CI gating via the FAIL_ON_CRITICAL flag to prevent performance-degrading code from merging.
- Flexible output modes, making it compatible with other Openclaw Skills for automated reporting or dashboarding.
- Detection of newly introduced jobs that lack baseline data for immediate performance tracking.
SKILL.md
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