Built with LovableQuality gate: passWording: Kimi K2.6 · English
Lovable-generated Gantt planner
A larger AI-generated project planner with persistent CRUD, project membership, roles, task ordering, and a prompt history that documents how the app was assembled.
Measured analysis work, not a sample-based AI opinion
These are workload and retained-evidence counts, not defect counts. FlowPreflight inventories source first, then connects the observations into routes, functions, effects, controls, and journeys.
Files inventoried
135
103 supported files parsed
Source facts connected
3,538
Imports, declarations, calls, routes, data operations, and other source observations
Functions analyzed
140
Static behavior analysis; not proof of runtime execution
Dependency candidates
399
Checked for advisory evidence; production reachability remains separate
Artifacts retained
46
Reports, diagrams, evidence, validation, and traceability records
WHAT WE DID
From repository to validated report
01
Verified the repository’s documented Lovable origin and pinned the analyzed commit.
02
Mapped React/Redux client behavior and inventoried Supabase data effects, project/task operations, and role-related source evidence.
03
Kept the missing ordered path between those source areas visible instead of inventing a complete customer journey.
04
Validated the complete report bundle while keeping the unresolved customer-journey reconstruction outside the primary flow count.
WHAT THE RESULT ESTABLISHED
A useful review without invented certainty
Recorded the persistent task/link operations and project-scoped data structures visible in source.
Surfaced the difference between demo-only roles and deploy-time authorization evidence.
Turned data consistency, dependency, and code-ownership questions into three bounded checks, not false release blockers.
PUBLICATION CHECKS
Deterministic analysis first. Controlled AI wording second.
FlowPreflight selected the evidence, journeys, decisions, priorities, and tasks without an LLM. Kimi K2.6 received only bounded report sections plus the minimum canonical context needed to improve the English wording. It did not receive the repository, analysis databases, full dossier, or full canonical report, and it could not add or remove a finding.
Schema and reference validation
Canonical counts, IDs, and claim levels preserved
English-only output validation
Public-output and secret-safety checks
Customer Report and Prompt Pack quality gates passed
Report publishedNo confirmed release blocker was established from the available evidence. Follow-up work remains attached to the exact evidence boundaries.
Artifact quality checks passed
BEFORE YOUR NEXT DEPLOY
Get the same evidence trail for your codebase.
See the flows AI generated, the controls source evidence supports, and the exact checks that still need a human or runtime signal.