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A first-screen quality standard before buyers judge the product.
A clearer logo direction before image prompts become random.
A reviewable workflow before AI builds around a messy process.
A build brief before developers or AI start guessing.
File changes with ownership, purpose, and recovery evidence.
An early code-quality check before the next feature piles on.
A defensive risk map before real users, money, or data depend on the app.
A realistic handoff path before calling a web app standalone.
Governed UI review when one good screen is no longer enough.
A practical stack decision before the build locks in cost and complexity.
System boundaries before features harden into accidental architecture.
Owner-readable progress from real project evidence.
A requirements authority before AI treats draft notes as truth.
A data model before screens hard-code the wrong records.
Implementation tied to approved requirements, not the latest chat drift.
Requirement-by-requirement evidence before release approval.
A production code-quality gate for risky or shared changes.
Session continuity when AI work spans tasks and blockers.
Reversible cleanup instead of deleting by instinct.
One final readiness gate across the whole project.
Skill lifecycle control before prompts become a messy library.
A project baseline before an AI agent starts coding blind.
A reusable rule baseline for future AI sessions.
Avoid turning your first AI sessions into the messy foundation you keep building on.
Stop the MVP from becoming a rebuild loop before AI builds too far on the wrong foundation.
Keep control before decisions, files, fixes, and rules scatter across AI-agent sessions.
