Quickstart
Connect your first repository and run an AI-selected test pipeline in under 5 minutes.
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Create your account
Go to superplane.com and sign up with GitHub OAuth or work email. GitHub OAuth is faster — it pre-authorizes repository access for the next step. Developer plan is free, no card required.
Note — plan selection# Developer plan: free, no card required # Builder plan: $89/mo — unlocks env provisioning + canary rollout # Platform plan: $299/mo — adds SSO, audit log, RBAC, 99.9% SLAConnect a repository
In the SuperPlane dashboard, click Add repository. Select the repo you want to connect. SuperPlane requests minimum required scopes only.
GitHub OAuth permission requestRequested scopes: repo:read # read commit metadata + file tree checks:write # post check run status statuses:write # update commit statusCreate policy.yml
Add a
.superplane/policy.ymlfile to your repository root. This is the control plane for SuperPlane's behavior on each run..superplane/policy.ymlversion: "1" test_selection: enabled: true confidence_threshold: 0.85 env_provisioning: enabled: true provider: "aws" canary: enabled: falseAdd SuperPlane to your CI config
Add a single step to your existing CI workflow. SuperPlane wraps your existing test runner — it doesn't replace it.
.github/workflows/ci.ymlsteps: # ... your existing checkout and setup steps - name: SuperPlane — AI test selection uses: superplane-io/action@v1 with: api_token: ${{ secrets.SUPERPLANE_TOKEN }} policy: ".superplane/policy.yml" - name: Run selected tests run: ${{ steps.superplane.outputs.test_command }}Push and review your first run
Push a commit. SuperPlane intercepts the pipeline, runs AI selection, and posts the run log back to GitHub. You'll see which tests were selected, which were skipped, and the confidence score for each decision.
SuperPlane run log — first run✓ superplane v0.9.2 ──────────────────────────────────────────── commit a7f3c21 feat: add export to CSV files_changed 3 tests_total 847 tests_selected 112 # 735 skipped at confidence >0.85 ──────────────────────────────────────────── api/export_test.py SELECTED 0.97 api/auth_test.py SELECTED 0.91 ui/dashboard_test.py skipped 0.23 tests/regression/suite_4 skipped 0.11Next steps
The model starts with zero history — it gets smarter with every run. After ~20 runs you'll see selection accuracy climb significantly.