Initial UI created with v0Quality gate: passWording: Kimi K2.6 · English
v0-assisted Anthropic token counter
A small AI-assisted utility that sends user text through a server route to Anthropic’s token-counting API—ideal for testing provider, input, logging, and secret-reference boundaries.
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
24
12 supported files parsed
Source facts connected
637
Imports, declarations, calls, routes, data operations, and other source observations
Functions analyzed
17
Static behavior analysis; not proof of runtime execution
Dependency candidates
363
Checked for advisory evidence; production reachability remains separate
Artifacts retained
38
Reports, diagrams, evidence, validation, and traceability records
WHAT WE DID
From repository to validated report
01
Pinned the v0-assisted repository and analyzed its client-to-route-to-provider source path.
02
Distinguished environment-variable references from hardcoded credential values.
Published the complete source assessment with runtime limitations attached and one dependency follow-up check.
WHAT THE RESULT ESTABLISHED
A useful review without invented certainty
Reconstructed the API request plus the partial user-input-to-Anthropic provider path.
Kept ANTHROPIC_API_KEY references as configuration evidence rather than claiming an exposed secret.
Exposed payload/logging and dependency follow-up without presenting unverified candidates as confirmed issues.
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.