Role Context Runtime Fixtures Handoff
Runtime-query-backed real-row fixtures now feed the AI Fluency loop.
The runtime query surface now feeds the AI Fluency loop with 50 real-row fixtures. Each selected fixture traces to JD-aware source job IDs, carries a role-context-query route decision, and produces five-layer AI Fluency capability maps through the existing headless loop.
A bridge now exists from role context to AI Fluency.
The new role_context_runtime_fixtures.py builder reads JD-aware E2E fixtures, enriches each row with role-context-query, and passes those enriched rows into run_ai_fluency_headless_loop. The output is a durable runtime fixture receipt plus a 50-row JSONL proof artifact.
The real run produced 50 query-backed fixtures.
| Category | Count |
|---|---|
frontline_retail | 8 |
healthcare_or_regulated | 8 |
legal_or_compliance | 8 |
hr_or_people_sensitive | 8 |
general_business | 7 |
product_workflow | 5 |
ambiguous_healthcare_or_social_services | 3 |
customer_support | 2 |
data_analytics | 1 |
The five-layer loop is populated.
The generated ai_fluency_headless_loop_v1 receipt now has 50 capability maps, 250 capability layer scores, 250 capability observations, 50 proof artifact refs, and 250 aggregate heatmap rows. Every row has source job refs and a next curriculum move.
The focused and full checks pass.
| Check | Result |
|---|---|
| Runtime fixture tests | 2 passed |
| Combined focused tests | 7 passed |
| Ruff | All checks passed |
| Runtime fixture receipt | pass |
| Full validation | pass |
No production boundary moved.
No live Gemini call was made. No production runtime, real-user data, external writes, or telemetry were introduced. Runtime embedding authority remains unpromoted, and workflow fingerprint creation remains false.
Scale this proof to top-band coverage reporting.
The next slice should expand from the 50-row proving set to top 500 coverage reporting, then add stricter route expectations for regulated, healthcare, HR, legal, frontline, and generic-neighbor mismatch cases before more embedding expansion.
Use the 50-row runtime fixture set as the first real proving ground for role-context-to-AI-Fluency personalization.