/**
 * notquality DOGFOOD — runnable example.
 *
 * P5: qulib scoring a real app we own. This is the confidence-layer thesis
 * proven on a live delivery pipeline: qulib ingests notquality's CI results,
 * PR metadata, and automation maturity, then emits a real Release Confidence
 * score + verdict.
 *
 * Run with:
 *   npx tsx packages/core/src/examples/notquality-dogfood/run.ts
 *
 * What it does:
 *   1. Loads DATED real-sample fixture (provenance: gh CLI, 2026-06-04).
 *   2. Maps CI run → ciResultsToEvidence (EvidenceItem, source='ci-results').
 *   3. Maps PR metadata → prMetadataToEvidence (EvidenceItem, source='deploy-metadata').
 *   4. Builds a test-automation EvidenceItem from the pre-scored maturity facts.
 *   5. Calls computeReleaseConfidence → fused score + verdict.
 *   6. Prints a structured JSON report to stdout.
 *
 * HELD signals (operator-gated, not run here):
 *   - qulib_score_automation live scan (requires local notquality checkout + qulib CLI).
 *     Instead this example uses the statically-derived NOTQUALITY_AUTOMATION_MATURITY
 *     from the fixture (documented, conservative, clearly labelled as pre-scored).
 *   - analyze_app deployed crawl (external HTTP + optional LLM cost).
 *     The live-app-quality and accessibility EvidenceItems are omitted here; the
 *     pipeline runs correctly with a subset of evidence (partial-evidence honesty).
 *
 * Output format: NDJSON line appended to stdout (one JSON object per run).
 * The rubricVersion field versions the roll-up formula so it can evolve without
 * silently changing meaning (P5 stopgap formula; retires when qulib's own aggregator
 * ships in P3/P4 roadmap).
 */
export {};
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