GitHub CI Monitor — DORA Metrics & Workflow Analytics

What does GitHub CI Monitor do?

GitHub CI monitor collects completed GitHub Actions workflow run data across any number of repositories and computes four DORA metrics — Deployment Frequency, Change Failure Rate, Mean Time to Recovery (MTTR), and success rate — alongside timing statistics (average, min, and max run duration). This GitHub Actions tracker works on public repositories without a token, and supports private repositories with a GitHub Personal Access Token. Use it to power engineering dashboards, benchmark open-source project health, and automate CI/CD pipeline status reporting.

One structured record is produced per repository-workflow combination, covering a configurable lookback window of up to 90 days.

Why use GitHub CI Monitor?

• Engineering dashboards — aggregate CI/CD pipeline status across your entire GitHub organization into a single structured dataset; feed it into Grafana, Metabase, or any BI tool.
• DORA metrics reporting — compute Deployment Frequency, Change Failure Rate, and MTTR automatically from raw workflow history without manually exporting GitHub data.
• Flaky workflow detection — identify workflows with consistently high failure rates or extreme duration variance to prioritize pipeline reliability work.
• Open-source health monitoring — benchmark popular open-source repositories against their GitHub Actions tracker data to evaluate project maturity and CI discipline.
• Scheduled trend tracking — run weekly to build a time-series of CI health, surfacing degradations in success rate or rising MTTR before they become incidents.

How to use GitHub CI Monitor

1. Open the actor on Apify and click Try for free.
2. In the Input tab, add one or more GitHub repository slugs (e.g. ["microsoft/vscode", "facebook/react"]).
3. Optionally paste a GitHub Personal Access Token to unlock private repos and raise the rate limit from 60 to 5,000 requests/hour.
4. Set lookbackDays (default 30, max 90) and an optional workflowFilter substring to focus on specific workflows.
5. Click Start — each repository-workflow combination produces one output record.
6. Export as JSON or CSV, or stream results via the Apify API into your dashboard pipeline.

Input

Field	Type	Required	Default	Description
repositories	string[]	Yes	—	GitHub repo slugs to monitor (e.g. "microsoft/vscode")
githubToken	string	No	—	GitHub Personal Access Token — raises rate limit to 5,000 req/hr and enables private repos
lookbackDays	integer	No	30	Days of history to analyze (max 90)
workflowFilter	string	No	—	Case-insensitive name substring to filter workflows (e.g. "CI")

Example input:

{
  "repositories": ["microsoft/vscode", "facebook/react"],
  "lookbackDays": 30,
  "workflowFilter": "CI"
}

Output

One record per repository + workflow combination:

[
  {
    "repo": "microsoft/vscode",
    "workflow_id": 12345678,
    "workflow_name": "CI",
    "workflow_path": ".github/workflows/ci.yml",
    "period_days": 30,
    "total_runs": 142,
    "success_runs": 131,
    "failure_runs": 9,
    "cancelled_runs": 2,
    "success_rate_pct": 92.3,
    "change_failure_rate_pct": 6.3,
    "avg_duration_minutes": 8.42,
    "min_duration_minutes": 3.11,
    "max_duration_minutes": 24.7,
    "deployment_frequency_per_day": 4.37,
    "mean_time_to_recovery_hours": 1.25,
    "last_run_status": "success",
    "last_run_at": "2026-08-31T14:22:10Z"
  }
]

Data fields

Field	Type	Description
repo	string	Repository slug (owner/name)
workflow_id	integer	GitHub workflow ID
workflow_name	string	Workflow display name
workflow_path	string	Path to the workflow YAML file
period_days	integer	Lookback window in days
total_runs	integer	All completed runs in the period
success_runs	integer	Runs with success conclusion
failure_runs	integer	Runs with failure conclusion
cancelled_runs	integer	Runs with cancelled conclusion
success_rate_pct	float	Percentage of runs that succeeded
change_failure_rate_pct	float	DORA Change Failure Rate (% of failure runs)
avg_duration_minutes	float	Mean duration of all runs
min_duration_minutes	float	Fastest run duration
max_duration_minutes	float	Slowest run duration
deployment_frequency_per_day	float	DORA Deployment Frequency (successful runs/day)
mean_time_to_recovery_hours	float	DORA MTTR — avg hours from failure to next success
last_run_status	string	Conclusion of the most recent run
last_run_at	ISO8601	Timestamp of the most recent run

Cost estimation

Pricing is Pay-Per-Event (PPE) — charged per workflow record produced.

Workflow records	Cost
5 repos × 3 workflows = 15	$0.045
20 repos × 5 workflows = 100	$0.30
50 repos × 10 workflows = 500	$1.50

FAQ

Do I need a GitHub token? No — public repositories work without a token, though the unauthenticated rate limit is 60 requests/hour. For large organizations or private repositories, add a GitHub Personal Access Token with repo scope to raise the limit to 5,000 requests/hour.

What counts as a "completed" run? Only runs with status=completed are fetched. Queued and in-progress runs are excluded. Cancelled runs are counted in total_runs but not in success_runs or failure_runs.

How is MTTR calculated? For each failure run, the actor finds the chronologically next success run in the same workflow and measures the elapsed time. MTTR is the average across all such pairs in the lookback window.

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Disclaimer: This actor uses the public GitHub REST API. Data reflects completed workflow runs within the specified lookback window and depends on GitHub API availability. DORA metrics are computed from workflow run history and are approximate indicators, not exact operational measurements.

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Related actors

• GitHub Repo Stats — Stars, forks, contributor count for the same repos you're monitoring CI health on
• GitHub Trending Tracker — Rising repos to discover new projects whose CI practices you can benchmark against
• Docker Hub Image Tracker — Container image pull trends to pair with CI deployment frequency data

Feedback

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Keywords: GitHub Actions monitor, CI/CD analytics, build success rate, workflow tracker, DORA metrics, deployment frequency, GitHub CI analytics, lead time, GitHub Actions scraper, devops metrics, build failure patterns, GitHub automation, CI monitoring