AI-Native Maturity Scorecard
A 0–100 score across nine industry-standard competencies. Auto-updated as your team uses fastpace — local reads are real-time, fleet rollups every 5 min via F3.11 audit replication, webhook-driven events fire instantly. Per-customer scores are consent-gated; the org-wide aggregate may appear here when a buyer opts in.
F4.1 Project Setup 10 AI-readiness checklist (agents, hooks, the assistant context file — CLAUDE.md / AGENTS.md / GEMINI.md — decisions, patterns, glossary). F4.2 Spec-Driven Development 12 % of PRs with PRD/ERD/exec-plan provenance trailers. F4.3 Context Management 12 Six-dimension context score over time, plus 30-day trend. F4.4 Testing & Validation 14 % of AI-edited files with same-PR test changes (roadmap). F4.5 Harness Engineering 10 Guardrail strictness — # of agents × cap coverage × path tightness. F4.6 Architectural Guardrails 12 AI commits touching ADR-referenced files without updating the ADR (roadmap). F4.7 Review Maturity 12 % of AI-attributed PRs reviewed; avg substantive review comments per PR (roadmap). F4.8 AI SDLC Metrics 10 DORA split by attribution: lead time, deploy freq, MTTR, change-fail (roadmap). F4.9 Prompt Engineering 8 Per-developer reliability weighted by prompt-specificity proxy (roadmap). How it stays fresh
Every fastpace hook fire writes a signed entry to the local hash-chained audit log; every CLI command and the local UI compute sub-scores as derived views over those logs. Audit shards push to your S3/GCS bucket every 5 min (configurable per metric in F4.10) and the org dashboard ingests + recomputes on each ingest. There is no separate metrics pipeline — the data plane is the audit plane.
Spec:
packages/fp/assets/docs/ai-native-maturity.md — public,
versioned, in the open-source repo.