Identify and visualize GitHub Actions queue wait hotspots from run metadata to resolve CI bottlenecks before they stall development merges.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install github-actions-queue-latency-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-queue-latency-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 Queue Latency Audit?
The GitHub Actions Queue Latency Audit skill is a powerful diagnostic utility designed for DevOps engineers and developers who need to quantify CI/CD overhead. By analyzing exported workflow run data, it calculates the precise time jobs spend waiting in a queue versus their actual execution time. This visibility is crucial for identifying runner shortages, poorly scheduled jobs, or infrastructure scaling issues.
As part of the Openclaw Skills ecosystem, this tool enables teams to move beyond anecdotal evidence of CI slowness to data-driven performance tuning. It provides clear metrics on queue wait averages and peaks, allowing you to optimize your runner strategy and improve the developer experience by reducing the time developers wait for feedback on their pull requests.
GitHub Actions Queue Latency Audit Use Cases
- Detecting runner capacity shortages that cause jobs to wait in queue for extended periods.
- Auditing performance across large monorepos or complex multi-repository workflow families.
- Validating the impact of runner upgrades or configuration changes on overall CI throughput.
- Implementing automated CI checks that fail when queue latency exceeds pre-defined service level objectives.
How GitHub Actions Queue Latency Audit Works
- Run metadata is collected from GitHub using the gh CLI tool and stored as JSON files.
- The skill scans a specified directory for these JSON artifacts using glob patterns.
- It extracts critical timestamps including creation, start, and completion times for every job instance.
- Mathematical processing calculates the queue wait (startedAt - createdAt) and execution duration (completedAt - startedAt).
- Data is aggregated and grouped by repository, workflow, and job name to identify recurring hotspots.
- The tool generates a ranked report, highlighting jobs that exceed warning or critical thresholds.
GitHub Actions Queue Latency Audit Setup
To use this skill, ensure you have bash and python3 installed. First, collect your workflow run data:
gh run view <run-id> --json databaseId,workflowName,headBranch,headSha,url,repository,jobs > artifacts/github-actions/run-<run-id>.json
Then, execute the audit script with your desired configuration:
RUN_GLOB='artifacts/github-actions/*.json' \
TOP_N=20 \
QUEUE_WARN_SECONDS=120 \
bash skills/github-actions-queue-latency-audit/scripts/queue-latency-audit.sh
GitHub Actions Queue Latency Audit Data Schema & Taxonomy
The skill processes GitHub Actions run metadata and produces organized analysis reports.
| Data Point | Description |
|---|---|
| Queue Wait | Calculated as startedAt - createdAt |
| Runtime | Calculated as completedAt - startedAt |
| Grouping | Aggregated by Repository + Workflow + Job Name |
| Thresholds | Configurable via environment variables (Warn/Critical) |
When outputting in JSON format, the skill provides a root summary object, a list of grouped hotspots, and a list of specific job instances that triggered critical warnings.
GitHub Actions Queue Latency Audit Advanced Features
- Regex-based filtering for repositories, workflows, and jobs to isolate specific teams or projects.
- FAIL_ON_CRITICAL mode which exits with a non-zero status code if any job exceeds latency limits.
- Machine-readable JSON output mode for easy integration into custom dashboards or monitoring stacks.
- Multi-run analysis capabilities that allow for globbing hundreds of files to find long-term trends.
- Customizable ranking logic to focus on either the absolute worst outliers or the highest average wait times.
SKILL.md
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