Artifacts carry evidence, status, and non-conclusions together.
ArchSig artifacts are JSON records for a bounded observation universe. Their role is to preserve source refs, measurement status, coverage gaps, theorem boundaries, and report projections across commands.
Primary artifacts
The main review flow starts with Sig0 and ends with a PR
comment summary. Each step keeps claim level and measurement
status visible.
Sig0 outputarchsig-sig0-v0: component list, dependency edges, signature axes, and metric status for one revision.
Validation reportcomponent-universe-validation-report-v0: duplicate, closure, external target, and policy status checks.
Snapshot and diffsignature-snapshot-store-v0 and signature-diff-report-v0: revision persistence and before / after changes.
AIR and review reportsaat-air-v0, theorem precondition check, Feature Extension Report, policy decision, and PR comment summary.
Surface artifacts
The artifact catalog should be read by surface. Core artifacts
describe a bounded repository observation; Review artifacts
project that observation into PR review; SFT artifacts project
a proposed change into a bounded forecast report; Operational
artifacts store calibration and feedback evidence.
Core artifactsSig0, validation reports, snapshots, diff reports, metric status, and unmeasured axis records.
Review artifactsAIR, AIR validation, theorem precondition check, Feature Extension Report, policy decision, PR comment summary, and baseline suppression.
SFT artifactsArtifactDescriptor, OperationSupportEstimate, ForecastConeSkeleton, ConsequenceEnvelope, ForecastCalibrationHook, and their validation reports.
Operational artifactsPR history datasets, feature datasets, outcome linkage, daily ledger, calibration, threshold, ownership, repair adoption, incident correlation, and hypothesis refresh records.
Policy and registry
Policy, registry, and schema artifacts let teams add local
thresholds and adapters without claiming that a policy pass is
architecture lawfulness.
Dataset artifacts are empirical records. They connect PR
metadata, reports, and outcome observations without turning
correlation into causality.
Empirical datasetsPR metadata, before / after signatures, feature reports, theorem checks, and outcome linkage.
Operational feedbackDaily ledger, calibration review, team threshold, ownership boundary, repair adoption, incident correlation, and hypothesis refresh artifacts.
SFT forecasting
Forecasting artifacts bound the input, operation support,
finite support, horizon, unknown remainder, and review
recommendations for a proposed change.
ArtifactDescriptorSource refs, action class candidates, scope, missing evidence, measurement boundary, and forecast non-conclusions.
OperationSupportEstimateCandidate operation families, policy constraints, known forbidden support, unknown remainder, and confidence boundary.
ForecastConeSkeleton and ConsequenceEnvelopeBounded path candidates, affected regions, comparable axes, obstruction candidates, missing boundary, and review / CI recommendations.