Project 11: Submission Dashboard Workflow Lab
Build a deterministic release workflow that converts app quality evidence into fast, predictable review outcomes.
Quick Reference
| Attribute | Value |
|---|---|
| Difficulty | Intermediate |
| Time Estimate | 1 week |
| Main Programming Language | N/A (process automation) |
| Alternative Programming Languages | TypeScript, Python |
| Coolness Level | Level 3 |
| Business Potential | Critical launch leverage |
| Prerequisites | Project 9 or equivalent release discipline |
| Key Topics | Submission states, artifact manifests, review queue control |
1. Learning Objectives
- Model app submission as a state machine with explicit gates.
- Build a complete evidence pack before dashboard submission.
- Enforce one-version-in-review workflow safely.
- Create a repeatable rejection-to-resubmission loop.
2. All Theory Needed (Per-Concept Breakdown)
Submission Lifecycle Control
Fundamentals Submission is a constrained release process. OpenAI guidance requires verified profile/domain/legal links and uses an explicit review lifecycle. Treat submission as an engineering pipeline, not a human checklist.
Deep Dive into the concept A reliable submission lifecycle has four domains: prerequisite validation, candidate assembly, review-state tracking, and remediation iteration. Prerequisites ensure you can submit at all (role, profile, domain, legal URLs). Candidate assembly ensures every release artifact is versioned and reviewable. Review-state tracking ensures feedback is tied to one candidate version. Remediation iteration ensures every rejection finding maps to a precise fix and proof.
The one-version-in-review rule drives queue design. Without strict candidate locking, teams unintentionally overlap changes and lose traceability. Use a release manifest and freeze window while in review. If a rejection occurs, reopen the candidate branch, patch only the required findings, and regenerate evidence. This keeps reviewer context stable and shortens approval loops.
Minimal concrete example
candidate_manifest_v1.2.0:
- metadata.json
- legal-links.json
- auth-recovery-trace.md
- policy-check-report.md
- ux-recovery-transcripts.md
3. Project Specification
3.1 What You Will Build
A submission pipeline that validates prerequisites, assembles evidence artifacts, and produces a dashboard-ready candidate package.
3.2 Functional Requirements
- Validate role/profile/domain/legal URLs.
- Block submission if another version is in review.
- Export evidence manifest with traceable artifact IDs.
- Track feedback and re-submission deltas.
3.3 Real World Outcome
$ npm run submission:dry-run
[ok] profile/domain/legal prerequisites
[ok] no active in-review candidate
[ok] metadata + screenshots bundle
[ok] auth/policy/ux evidence linked
ready_for_submit=true
candidate=v1.2.0
4. Solution Architecture
Quality Gates -> Candidate Manifest -> Dashboard Submit -> Review Feedback -> Patch Loop
5. Implementation Guide
5.1 The Core Question You’re Answering
“How do we guarantee every submission is complete, auditable, and review-friendly?”
5.2 Concepts You Must Understand First
- Release gates and blocking checks.
- Review lifecycle ownership.
- Evidence traceability and change control.
5.3 Questions to Guide Your Design
- Which checks must block release?
- How do you tie reviewer comments to specific evidence?
- How do you prevent mixed-version artifacts?
5.4 Thinking Exercise
Design a state machine with states draft, ready, submitted, rejected, approved, and define owner/action per transition.
5.5 The Interview Questions They’ll Ask
- How do you handle one-version-in-review constraints?
- What goes in a submission evidence bundle?
- How do you shorten rejection cycles?
- Which checks are most likely to catch blockers early?
- How do you audit release decisions?
5.6 Hints in Layers
- Hint 1: Start with a strict prerequisite checker.
- Hint 2: Build one machine-readable manifest file.
- Hint 3: Lock candidate version once submitted.
- Hint 4: Require remediation evidence for each rejection finding.
5.7 Books That Will Help
| Topic | Book | Chapter |
|---|---|---|
| Release discipline | “Accelerate” | Measurement chapters |
| Automation | “The Pragmatic Programmer” | Feedback and automation |
| Interface governance | “API Design Patterns” | Evolution patterns |
6. Testing Strategy
- Dry-run submission checks on every release branch.
- Simulated rejection with required remediation mapping.
- Evidence manifest integrity validation.
7. Common Pitfalls & Debugging
| Pitfall | Symptom | Solution |
|---|---|---|
| Mixed candidate artifacts | Reviewer confusion | Lock one candidate version at a time |
| Missing legal links | Immediate block | Add URL health checks to release gates |
| Vague metadata | Rejection for clarity | Rewrite around explicit user jobs |
8. Extensions & Challenges
- Add auto-generated reviewer response templates.
- Add SLA dashboard for rejection turnaround time.
- Add historical scoring for submission quality trends.
9. Real-World Connections
- App marketplace launch operations
- AI feature governance workflows
- DevRel and release management collaboration
10. Resources
- OpenAI Apps SDK: Submit your app
- OpenAI Apps SDK: App submission guidelines
- OpenAI Help: Submitting apps to the directory
11. Self-Assessment Checklist
- I can describe every submission state transition.
- I can produce a complete evidence manifest automatically.
- I can run a clean rejection-to-resubmission cycle.
12. Submission / Completion Criteria
Minimum Viable Completion
- Automated prerequisite checks and one candidate manifest.
Full Completion
- Includes review lifecycle tracking, remediation evidence links, and measurable cycle-time metrics.