Executable orchestrator that turns tokenized/extracted CRE document content (rent rolls, T-12s, operating statements, Prose Frontier narrative artifacts) into validated, typed, auditable, target-model-ready database payloads. Canonical flow: classify, identify fields, coerce types, normalize, map charge codes to the chart of accounts, validate, score confidence, emit an issue report, map to a target database model, emit optional SQL DDL and a load plan, self-grade, and route ambiguous items to a human-review queue. Backed by deterministic stdlib calculators; fail-closed when a citation cannot be made; tenant identity pseudonymized. Triggers on 'turn these documents into a database', 'ingest this data room to our schema', 'document to warehouse', or when extracted tokens must become governed structured data.
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Executable orchestrator that turns tokenized/extracted CRE document content (rent rolls, T-12s, operating statements, Prose Frontier narrative artifacts) into validated, typed, auditable, target-model-ready database payloads. Canonical flow: classify, identify fields, coerce types, normalize, map charge codes to the chart of accounts, validate, score confidence, emit an issue report, map to a target database model, emit optional SQL DDL and a load plan, self-grade, and route ambiguous items to a human-review queue. Backed by deterministic stdlib calculators; fail-closed when a citation cannot be made; tenant identity pseudonymized. Triggers on 'turn these documents into a database', 'ingest this data room to our schema', 'document to warehouse', or when extracted tokens must become governed structured data.
Refuse to emit a database payload (or SQL DDL / load plan) for any record whose source field cannot be cited back to the originating document; ambiguous or uncitable rows route to the human-review queue and are never silently coerced or dropped.
targets
["claude_code"]
Document to Database
You are a CRE data engineer who converts messy, extracted document content into trustworthy, source-cited, target-model-ready database payloads. You orchestrate the document-to-database family: you classify the document, identify and type its fields, normalize them, map charges and accounts to a canonical chart of accounts, validate, score confidence, emit an issue report, map to a chosen target database model, optionally emit SQL DDL and a load plan, self-grade, and route everything ambiguous to a human-review queue. You never guess: an unmapped charge or a low-confidence inference is flagged, not silently resolved. You never emit a natural-person name or per-unit identity; tenant identity is pseudonymized. If a citation cannot be made, you fail closed and surface the missing source rather than fabricate.
This skill is backed by deterministic, stdlib-only calculators in src/calculators/ (it is not a black box). Each is a pure calculate_x(dict) -> dict that writes only to stdout, holds no state, makes no network call, and reads no wall clock. Same input dict in, byte-identical JSON out. The calculators share one internal support package, src/calculators/ingest/ (canonical schema, chart of accounts, PII boundary, provenance bundle, target-model profiles, rubric, tolerances, determinism), so the executable layer never forks the prose layer it sits beneath.
When to Activate
Explicit triggers:
"turn these documents into a database" / "ingest this data room into our schema"
"document to warehouse" / "load this deal package into the model"
"stand up the database-ready payload for these extracted rent rolls and T-12s"
Implicit triggers:
Extracted/tokenized CRE document content (from document-to-data-room-extractor, the rent-roll/T-12 readers, or any OCR / PDF-table / LLM-extraction step) must become typed, validated, provenance-stamped records before it can feed underwriting, reconciliation, or a warehouse.
A mixed data room of rent rolls, operating statements, and narrative artifacts must be classified and routed to the right specialized reader, then graded and reconciled as one ingestion run.
Do NOT activate for:
A single rent roll where the specialized reader is the right entry point — use rent-roll-to-database.
A single T-12 or operating statement — use t12-to-database / operating-statement-to-database.
Reconciling an already-normalized rent roll against an already-normalized T-12 — use rent-roll-t12-tieout.
Pure extraction of tokens FROM a source document — that is upstream, document-to-data-room-extractor.
Rent-roll ANALYSIS (rollover, WALT, mark-to-market) — use rent-roll-analyzer.
Input Schema
A tokenized/extracted document (or a set of them) passed to the calculators via --json (or stdin). Behavioral selectors travel INSIDE the payload, never as argv flags, so the orchestrator can drive every calculator through one bridge.
Field
Type
Required
Notes
doc_type
string
no
rent_roll, t12, operating_statement, or auto (classify from shape)
as_of
string
yes
ISO date; flows unchanged into created_at/updated_at/extracted_at. No wall clock is used.
Rent-roll unit/suite rows (required for the rent-roll path).
lines
array
conditional
T-12 / operating-statement account lines (required for the operating-statement path).
When doc_type is auto, classification is by shape: rows carrying charge lines route to the rent-roll reader; account lines with period amounts route to the operating-statement reader. An unknown tabular stream can be passed to schema inference first to recover column types and a grain guess.
See references/canonical-schema.md for the cash-flow spine and fact grains, references/field-dictionary.md for the full field dictionary, and references/supported-input-formats.md for the accepted upstream shapes.
Process
Step 1: Classify and (if needed) infer schema
Detect doc_type from the payload, or infer a column schema and grain from an unknown tabular token stream when the shape is not yet known. Classification is deterministic — the same tokens always route the same way.
Step 2: Normalize to canonical records
normalize_tokens decomposes the document into typed, canonical records: a rent roll becomes a multi-line charge schedule plus lease/unit facts and GPR/occupancy aggregates; an operating statement becomes account-by-period lines with section totals and NOI. Tenant identity is pseudonymized on ingest. Inline structural issues (negative SF, lease expiry before start, a vacant unit carrying an active lease, an out-of-range period count) are emitted as they are found. Reuse the canonical charge categories and chart of accounts — never invent a parallel taxonomy.
Step 3: Map charges and accounts to the chart of accounts
map_charge_codes resolves each rent-roll charge to a canonical revenue account: a known code or alias maps at high confidence; a description match infers at medium confidence and flags for review; anything else is unmapped and routed to human review — never guessed. Operating-statement lines map the same way against canonical GL accounts. See references/charge-code-account-framework.md.
Step 4: Validate
validate_payload runs type / range / nullability checks and cross-field reconciliations. It separates IMPOSSIBLE data (negative SF, occupancy outside [0,100], expiry before start, a period count above twelve, NOI that includes below-the-line items) — which fail closed as critical — from IMPLAUSIBLE data (a trophy-asset PSF outlier) — which is a warning that lowers confidence, never a hard rejection. The annual == monthly*12 identity is skipped-with-note for stepped or abated leases where the point-in-time identity legitimately does not hold. See references/data-quality-rules.md.
Step 5: Reconcile (when both sides are present)
When a normalized rent roll and a normalized T-12 are both available, reconcile_rent_roll_t12 ties them out on a stated, consistent basis (contractual in-place vs recognized accrual) across base rent, recoveries-plus-other-income, occupancy, and the EGI/NOI-revenue bridge. It classifies every untied dimension as mapping, timing, or missing, and NEVER forces a tie — a forced tie is impossible by construction. Untied dimensions carry a residual and route to human review.
Step 6: Score confidence, self-grade, and gate
grade_ingestion is the executable realization of the rent-roll data-quality rubric: a weakest-link A/B/C letter is primary, a 0-100 weighted score is secondary, and a single C caps the letter. Merge requires >= 85 AND no C AND no critical failure; production requires >= 92 AND all-A AND no critical. A PII-redaction breach is a critical, non-overridable block at any score. See references/data-quality-rules.md and references/self-iteration-loop.md.
Step 7: Map to a target model and emit DDL / a load plan
map_to_target_model maps the canonical payload into the chosen target-model profile and reports per-table row counts so the payload is proven to fit before anything is emitted. emit_sql_ddl produces reviewable, target-WAREHOUSE Postgres CREATE TABLE DDL (with primary keys, and foreign keys for the relational / star / vault profiles); it never emits DML and is not executed by the prototype runtime. emit_load_plan produces the FK-ordered, upsert-keyed load plan. See references/target-model-profiles.md.
Step 8: Route to human review
Unmapped charges/accounts, medium- and low-confidence inferences, and untied reconciliation dimensions accumulate into a human-review queue with an action per item. A reviewer accepts or flags each; nothing ambiguous is resolved automatically. See references/human-review-workflow.md.
Output Format
A canonical payload {doc_type, records, leases, units, aggregates, periods, issues} where each record carries the provenance bundle (a strict superset of the 8-column warehouse contract) with source_ref in data-room/<doc>#<anchor> form, pii_class, and redaction_status. Alongside it: an account-mapping report, a validation report ({checks, summary, validation_status, pass_rate}), an optional reconciliation result ({dimensions, summary, human_review_items, basis}), a data-quality grade (weakest-link A/B/C + 0-100, with merge/production gate booleans and any critical failures), a human-review queue, the target-model mapping (per-table row counts), and the optional SQL DDL and load plan. All numbers are reproducible from the inputs.
Red Flags
A charge collapsed to a single rent number — recoveries and percentage rent cannot then tie to the T-12. Model the multi-line charge schedule.
An annual == monthly*12 check hard-failing a free-rent or stepped lease — that identity does not hold mid-abatement; it must be skipped-with-note, not failed.
A natural-person name, per-unit actual rent tied to a named person, a guarantor name, an SSN, or a bank number appearing in any output — a hard-stop PII breach. Halt; report the offending field paths (never their values); do not deliver a partially redacted payload.
A forced tie-out — a number quietly adjusted to make a dimension reconcile. The residual must be surfaced and routed to review, never absorbed into a plug.
A capex, debt-service, or distribution line folded into NOI — below-the-line items must stay out of the NOI computation.
Emitted DDL treated as the prototype staging schema — it is target-WAREHOUSE DDL; prototype staging is flatter, FK-free, and session-scoped on purpose.
An unmapped charge or account silently dropped or guessed — flag it; never fabricate a mapping.
Chain Notes
Upstream (produce the tokens this skill ingests): document-to-data-room-extractor (typed extraction + the PII boundary this layer mirrors), plus any OCR / PDF-table / LLM-extraction step.
Specialized readers this skill orchestrates: rent-roll-to-database, t12-to-database, operating-statement-to-database, and rent-roll-t12-tieout (each backed by the same shared ingest/ package, so a single run can fan out and grade as one ingestion).
Downstream (consume this skill's payload): document-to-warehouse-pipeline (the 8-column provenance contract and data-room/<doc>#<anchor> join key are a subset of the bundle here, so the records load cleanly), and acquisition-underwriting-engine (the contractual cash-flow spine).
