Cedar Ridge Intake

Cedar Ridge Intake Coordination Portal tasks

These tasks all follow one shape: a prompt names an entity to audit (a roster, a referral batch, a transfer batch, or a program-candidate panel) at the Cedar Ridge Intake Coordination Portal, and ships an input/payloads/answer_template.json that defines the exact JSON output shape and the controlled vocabulary (enums, reason codes, ordering). Your job is to read the live portal, apply the deterministic intake rules, and return one JSON object that conforms to the template — JSON only, no prose, unless the prompt says otherwise.

The scoring is exact-match against controlled values, so precision on enums, list ordering, count keys, and echoed constants matters more than anything.

Workflow

1. Read the task fully. Read input/prompt.txt and, byte for byte, the input/payloads/answer_template.json (and any other payload, e.g. target_roster.json). The template is the source of truth for: required top-level keys, per-item keys, every allowed enum / reason-code / action-code value, ordering rules, and which constants (task_id, batch_id, roster_id, program_code, service_line) to echo verbatim. Never invent a value outside the template's allowed set.

2. Reach the environment. Get the base URL from environment_access.md (GDPEVO_ENV_BASE_URL); prompts use the placeholder <TASK_ENV_BASE_URL>. Confirm liveness, then pull data with the read-only SQL endpoint (POST /query, body {"sql":"SELECT ..."}) — it is far faster than the REST endpoints for whole batches. See references/portal_api.md for endpoints, the full table schema, and stable reference constants (ICD chapter map, dialysis facilities, artifact types).

3. Identify the archetype from the template's keys and open the matching rule file:

   Signal in the template / prompt	Archetype	Rules
   roster_id, per-patient insurance/prescription/pharmacy/lifestyle/overall risk + registration	A. Access verification	references/access_verification.md
   referral batch_id, readiness_status, ICD/coding discrepancies, duplicates, blockers, priority	B. Referral readiness / chart activation	references/referral_readiness.md
   transfer batch_id, packet completeness/staleness, chair capacity, intake decision	C. Dialysis transfer review	references/transfer_review.md
   program_code, per-candidate eligibility/enrollment/monitoring package	D. Enrollment panel	references/enrollment_panel.md

   If a task doesn't match any archetype, treat the rule files as the pattern: pull the batch and joined rows, and reverse-engineer the mapping from the template's controlled values and the portal data (see "Deriving rules for a new variant" below).

4. Apply the rules in the archetype file to every row. Compute each per-item field, then the cohort/summary block from those items. Sort every list exactly as the template specifies.

5. Validate before returning. Re-check against the template: all required keys present; every enum/code value is in the allowed set; every list ordered as required; count objects include all keys, zero-filled, and the counts equal the number of matching items; echoed constants (task_id, batch_id/roster_id/program_code, service_line, requested dates) are exactly right; the row count equals the number of entities in the batch/roster/candidate list. Output valid JSON with no surrounding text.

Output conventions (apply everywhere)

• Echo, don't guess, constants. task_id (often required to equal the task folder name), roster/batch/program ids, and service_line come from the template's required_value/constant or from the roster/referral/program row — copy them exactly, uppercase as stored.
• Unordered sets vs ordered lists. When the template calls a list an "unordered set" (reason/issue/blocker/action codes), emit it sorted (alphabetical) for stability. When it specifies an ordering (ascending by id, alphabetical by code, priority order), follow that literally.
• Count objects are exhaustive and zero-filled. Every enum key the template lists must appear with an integer, including zeros. Cross-check that per-category counts sum to the total.
• Empty is [], not omitted. A section with no members (e.g. ready_to_schedule, duplicate_groups) is an empty list, still present.
• Booleans/nulls as typed. first_checkin_days and similar are integer-or-null; priority_tier is null for ready referrals; keep JSON types exact.

Deriving rules for a new variant

The environment holds many more rosters/batches/programs than any one task, and the engine is deterministic. If you hit a service line, program, modality, or output field the rule files don't cover: pull that batch plus its joined rows, list the template's controlled values, and infer the condition→value mapping from the data (e.g. SELECT DISTINCT chapter FROM icd_codes WHERE service_family=... for a new referral line). Keep every rule monotonic and reproducible, and prefer the documented cross-archetype invariants (severity precedence, accepted-chapter checks, capacity sums, chart presence tests) which are stable across batches.

The rule files describe the portal's business logic (the reusable engine), calibrated against training batches. Where a branch was never exercised by training data, the file says so — treat those as principled defaults and double-check them against the current task's data.