CI/CD Mental Model and Pipeline Architecture Continuous Integration answers: can this change safely join the main branch? Continuous Delivery answers: do we have a verified artifact that could be deployed? Continuous Deployment goes one step further and actually promotes it automatically. TaskFlow separates these responsibilities instead of hiding them in one giant workflow.
Immutable Environment Commit SHA → Verified source → image → promotion
What this changes in TaskFlow • Source commit is the identity of a release
• Build once; promote the same artifact
• Failures stop at the earliest honest boundary
• Production deployment is Phase 34
CI/CD terminology here is an engineering model used by TaskFlow; deployment implementation intentionally remains Phase 34.
CI turns repository rules into automated gates. A useful pipeline must be understandable, reproducible locally, secure, and able to fail for the right reasons.
Restate the goal of “CI/CD Mental Model and Pipeline Architecture” as one observable behavior. Write the success signal before touching code.
Trace the boundary map: git event → runner → install → quality gates → artifact/deploy decision. Mark the one boundary this lesson changes.
Inspect the current repository state and record the exact files, route, table, or runtime involved. Avoid creating an abstraction before you can name the existing owner.
Implement the smallest vertical slice. Add one coherent change, run the narrowest verification command, and read the output before continuing.
Connect the slice to TaskFlow’s phase artifact: .github/workflows/ci.yml plus release/deploy workflows that build once, tag by commit SHA, and gate promotion.. Verify behavior from the outside as a user or API client would.
Create one controlled failure that is safe to reproduce. Predict the symptom first, capture evidence, locate the earliest broken boundary, then fix only that cause.
Close the reference and rebuild or explain the core change from memory. Record what you would monitor or test before trusting the same change in production.
name: CI
on: [pull_request]
jobs:
verify:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- run: corepack enable
- run: pnpm install --frozen-lockfile
- run: pnpm lint && pnpm typecheck && pnpm test && pnpm buildDo not copy this as a finished answer. Predict what each boundary does, type the smallest useful slice, run it, then compare the result with your prediction.
Compare Node/pnpm versions, lockfile state, environment variables, OS assumptions, and generated artifacts.
Set least-privilege permissions at workflow/job level and isolate untrusted pull-request code from secrets.
Without copying the final source, create a second small scenario that applies “CI/CD Mental Model and Pipeline Architecture” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Create CI triggered by pull_request with read-only contents permission and explicit concurrency cancellation.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Using pull_request_target with untrusted checkout/secrets
Predict the symptom before causing the failure. Then locate the earliest boundary where reality diverges from expectation.Why does this responsibility live here, what input does it trust, what output proves success, and what would go wrong if another layer owned it?
If your explanation is vague, repeat the boundary trace before moving on.Close the reference. Reproduce the core idea from memory or explain the exact request/data flow aloud. Then check your diff or notes for accidental shortcuts that bypass validation, authorization, error handling, or verification earned in earlier phases.