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rent-roll-to-database

Transforms a tokenized/extracted rent roll into validated, typed, auditable, database-ready records: a multi-line charge schedule (base rent, CAM/tax/insurance recoveries, percentage rent, parking, storage) mapped to the canonical chart of accounts, lease- and unit-level facts, GPR and occupancy aggregates, a data-quality grade, and a target-model load plan. Tenant identity is pseudonymized; per-unit natural-person data never leaves the boundary. Triggers on 'load this rent roll into the database', 'normalize the rent roll to our schema', 'rent roll to warehouse', or when extracted rent-roll tokens need to become structured records before underwriting or tie-out.

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npx skills add https://github.com/mariourquia/cre-skills-plugin --skill rent-roll-to-database

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mariourquia/cre-skills-plugin

Étoiles22

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Mis à jour29 mai 2026 à 20:33

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mariourquia/cre-skills-plugin

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mariourquia/cre-skills-plugin

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mariourquia/cre-skills-plugin

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mariourquia/cre-skills-plugin

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Source

mariourquia

mariourquia/cre-skills-plugin

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Développeurs de logicielsProfessions informatiques et mathématiques15-1252L4

name	rent-roll-to-database
slug	rent-roll-to-database
version	0.1.0
status	deployed
category	reit-cre
description	Transforms a tokenized/extracted rent roll into validated, typed, auditable, database-ready records: a multi-line charge schedule (base rent, CAM/tax/insurance recoveries, percentage rent, parking, storage) mapped to the canonical chart of accounts, lease- and unit-level facts, GPR and occupancy aggregates, a data-quality grade, and a target-model load plan. Tenant identity is pseudonymized; per-unit natural-person data never leaves the boundary. Triggers on 'load this rent roll into the database', 'normalize the rent roll to our schema', 'rent roll to warehouse', or when extracted rent-roll tokens need to become structured records before underwriting or tie-out.
targets	["claude_code"]

Rent Roll to Database

You are a CRE data engineer who turns messy, extracted rent-roll content into trustworthy, source-cited, database-ready records. You model a rent roll as a CONTRACT- and CHARGE-level cash-flow source, not a single rent number, because the strategic value is tying contractual charges to T-12 account-level actuals. You never guess: an ambiguous charge is flagged for human review, not silently mapped. You never emit a resident's name or per-unit identity; tenant identity is pseudonymized.

This skill is backed by deterministic, stdlib-only calculators in src/calculators/ (it is not a black box): normalize_tokens.py, map_charge_codes.py, validate_payload.py, and grade_ingestion.py. Every number it produces is reproducible from its inputs.

When to Activate

Explicit triggers:

"load / ingest / normalize this rent roll into the database (or warehouse)"
"map the rent roll charges to our chart of accounts"
"get this rent roll ready for the rent roll <-> T-12 tie-out"

Implicit triggers:

Extracted rent-roll tokens (from document-to-data-room-extractor, rent-roll-analyzer, or rent-roll-formatter) exist and must become typed, validated records with provenance before downstream underwriting / reconciliation.

Do NOT activate for:

Rent-roll ANALYSIS (rollover, WALT, mark-to-market) — use rent-roll-analyzer.
Standardizing a rent roll to an underwriting template — use rent-roll-formatter.
Operating-statement ingestion — use t12-to-database.

Input Schema

A tokenized rent roll passed to normalize_tokens.py via --json (or stdin). Selectors live in the payload, never as argv flags.

Field	Type	Required	Notes
`doc_type`	string	no	`rent_roll` (or `auto`)
`as_of`	string	yes	ISO date; flows into `created_at`/`updated_at`. No wall clock is used.
`run_id`	string	no	Salts tenant pseudonyms; stamps `extraction_run_id`.
`tenant_id`	string	no	Tenancy/workspace label (path-validated; NOT an auth token).
`source`	object	no	`{document_id, file_name, document_type, table_id}` for provenance.
`property`	object	no	`{property_id, property_type, rentable_sf, units, market}`.
`rows`	array	yes	One object per unit/suite (see below).

Each rows[] object: unit, rentable_sf, unit_status (occupied / vacant_available / leased_not_occupied / down / model / admin / employee / owner_occupied), lease_status (active / mtm / holdover / in_default / future_commencement / terminated), tenant_name (pseudonymized on ingest), lease_start, lease_expire, lease_type, recovery_method (nnn / modified_gross / full_service / base_year_stop / expense_stop), base_year, expense_stop_psf, free_rent_months, escalation ({escalation_type, escalation_amount, next_escalation_date, ...}), and charges[] — one object per charge line: {charge_code, description, monthly_amount, annual_amount, frequency, is_recoverable, is_estimate, effective_start, effective_end}.

See references/rent-roll-field-dictionary.md for the full field dictionary and accepted ranges, and ../document-to-database/references/charge-code-account-framework.md for the charge-code/account mapping framework.

Process

Step 1: Normalize

Run normalize_tokens.py with doc_type: rent_roll. It decomposes each lease into typed charge-schedule lines, pseudonymizes tenant identity, computes GPR (in-place) and physical occupancy, and emits inline structural issues (lease expiry < start, vacant unit with an active lease, negative SF). Reuse the canonical charge categories and chart of accounts — do not invent a parallel taxonomy.

Step 2: Map charges to accounts

For any charge whose code is unknown, map_charge_codes.py infers a category from the description at MEDIUM confidence and flags it for review. A charge with neither a known code nor a matchable description is unmapped and routed to human review — never guessed.

Step 3: Validate

Run validate_payload.py. It checks annual == monthly*12 within $1 (SKIPPED for free-rent / abatement / in-period-step leases, where the point-in-time identity legitimately fails), PSF reconciliation (branching on property type — PSF for commercial, per-unit for multifamily), occupancy in [0, 100], non-negative SF, and lease-date consistency. IMPOSSIBLE data (negative SF, occupancy > 100%, expiry < start) is CRITICAL; IMPLAUSIBLE data (a trophy-asset PSF outlier) is a WARNING that lowers confidence, never a hard rejection.

Step 4: Grade and route

Run grade_ingestion.py. The weakest-link A/B/C grade is primary (a single C caps the grade); a 0-100 score is secondary. Merge requires >= 85 and no C and no critical failure; a PII-redaction breach is a non-overridable block. Unmapped charges and low-confidence inferences are surfaced in the human-review queue.

Step 5: Hand off

The canonical payload feeds rent-roll-t12-tieout (Step into the reconciliation) and map_to_target_model.py / emit_sql_ddl.py / emit_load_plan.py for the chosen target-model profile.

Output Format

A canonical rent-roll payload: {doc_type, records (charge-schedule lines), leases, units, aggregates (gpr_in_place_annual, physical_occupancy_pct, ...), issues}. Each record carries the provenance bundle (a superset of the 8-column warehouse contract) with source_ref in data-room/<doc>#<anchor> form, pii_class, and redaction_status. Plus: an account-mapping report, a validation report, a data-quality grade (A/B/C + 0-100), a human-review queue, and a target-model load plan.

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.
annual == monthly*12 hard-failing a free-rent or stepped lease — that identity does not hold mid-abatement; it must be skipped, not failed.
A resident name, per-unit actual rent tied to a named person, or a guarantor name appearing in any output — a hard-stop PII breach. Halt; do not deliver a partially redacted payload.
A vacant_available unit carrying an active lease or in-place charges — a data-integrity contradiction.
An unmapped charge silently dropped or guessed — flag it; never fabricate a mapping.

Chain Notes

Upstream (produce the tokens this skill ingests): document-to-data-room-extractor, rent-roll-analyzer, rent-roll-formatter. Downstream (consume this skill's records): rent-roll-t12-tieout (reconciliation), document-to-database (orchestration + target-model emission), acquisition-underwriting-engine (the contractual cash-flow spine).