GitHub Actions Integration
Track pipeline metrics, correlate deployments with production incidents, and compute DORA metrics automatically. Know within seconds whether a new deploy caused a regression.
How It Works
Add the TigerOps Step
Add the tigerops/deploy-marker action as a step in your GitHub Actions workflow. It reports build metadata, deploy timestamps, and artifact versions to TigerOps automatically.
Configure Your API Key
Add TIGEROPS_API_KEY as a GitHub Actions secret in your repository settings. The action reads this secret and authenticates with TigerOps — no other configuration needed.
Deployments Appear as Events
Every successful deployment creates an event marker on your TigerOps metric charts. You can immediately see what changed in your system at any deployment boundary.
AI Correlates Deploys with Incidents
When an incident fires, TigerOps AI checks whether a recent deployment correlates with the anomaly. If so, it surfaces the deployment diff link and suggests an automated rollback.
What You Get Out of the Box
Deploy Event Markers
Every successful deployment appears as a vertical marker on all your metric charts. Immediately see whether a latency spike, error rate increase, or SLO breach coincides with a deploy.
Pipeline Duration Metrics
Track build duration, queue wait time, and job-level breakdown for all your GitHub Actions workflows. Identify which jobs are slowing down your deployment pipeline.
Failure Rate Analysis
Workflow failure rates by branch, repository, and job type. TigerOps alerts on persistent pipeline failures and identifies flaky test patterns using historical success rate data.
Deployment Frequency Tracking
Track DORA deployment frequency metrics automatically. See how often each service deploys to production, staging, and preview environments over time.
Change Failure Rate (CFR)
TigerOps automatically correlates deployment events with incident creation to compute change failure rate — a key DORA metric. No manual tracking required.
Mean Time to Restore (MTTR)
From deployment to incident resolution, TigerOps tracks the full incident lifecycle across deployments and computes MTTR per service and per team.
GitHub Actions Workflow Step
Add the TigerOps deploy marker step to your existing workflow in under a minute.
# .github/workflows/deploy.yml
name: Deploy to Production
on:
push:
branches: [main]
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Build application
run: npm ci && npm run build
- name: Run tests
run: npm test
- name: Deploy to production
run: ./scripts/deploy.sh
env:
DEPLOY_ENV: production
# Add TigerOps deploy marker AFTER your deploy step
- name: Mark deployment in TigerOps
uses: tigerops/deploy-marker@v1
with:
api_key: ${{ secrets.TIGEROPS_API_KEY }}
service: "order-service"
environment: "production"
version: ${{ github.sha }}
# Optional: link to rollback command
rollback_command: "kubectl rollout undo deployment/order-service"
# Track pipeline metrics (add to any workflow)
- name: Report pipeline metrics
if: always() # Run even on failure
uses: tigerops/pipeline-metrics@v1
with:
api_key: ${{ secrets.TIGEROPS_API_KEY }}
# Automatically records: duration, status, runner, branchCommon Questions
Does TigerOps require access to my GitHub repository?
No. The tigerops/deploy-marker action sends deployment metadata to TigerOps via an API call from within your workflow. TigerOps does not need OAuth access to your GitHub organization or repository — only the API key is required.
Can TigerOps track deployments across multiple environments (staging, production)?
Yes. Pass the environment parameter to the action (e.g., environment: production or environment: staging). TigerOps creates separate deployment timelines per environment and lets you compare metrics across deployment boundaries.
How does TigerOps correlate a deployment with an incident?
TigerOps uses a configurable lookback window (default: 30 minutes). When an incident fires, it checks whether a deployment event occurred in that window. If so, it adds the deployment as a contributing context event with the commit SHA, branch, and author.
Can I see which commit introduced a regression?
Yes. Each deploy marker in TigerOps includes the commit SHA, commit message, author, and a direct link to the GitHub commit diff. When AI identifies a deployment as correlated with an incident, it links directly to the diff for quick investigation.
Does TigerOps support GitHub Enterprise Server (GHES)?
Yes. Configure the action with the tigerops_github_api_url parameter pointing at your GHES instance. The action uses the GitHub REST API to fetch workflow run metadata, which is available on GHES 3.4+.
Know Whether Your Last Deploy Caused the Incident
Deploy markers, DORA metrics, and AI deploy-incident correlation. One workflow step to add.