document-to-database

Name: document-to-database
Author: mariourquia

36stars12forks

How to install document-to-database?

mariourquia/cre-skills-plugin/document-to-database

$npx -y skills add mariourquia/cre-skills-plugin --skill document-to-database

Installs into the current project.

Summary

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 type

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SKILL.md

1	# Document to Database
2
3	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.
4
5	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.
6
7	## When to Activate
8
9	Explicit triggers:
10	- "turn these documents into a database" / "ingest this data room into our schema"
11	- "document to warehouse" / "load this deal package into the model"
12	- "stand up the database-ready payload for these extracted rent rolls and T-12s"
13
14	Implicit triggers:
15	- 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.
16	- 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.
17
18	Do NOT activate for:
19	- A single rent roll where the specialized reader is the right entry point — use `rent-roll-to-database`.
20	- A single T-12 or operating statement — use `t12-to-database` / `operating-statement-to-database`.
21	- Reconciling an already-normalized rent roll against an already-normalized T-12 — use `rent-roll-t12-tieout`.
22	- Pure extraction of tokens FROM a source document — that is upstream, `document-to-data-room-extractor`.
23	- Rent-roll ANALYSIS (rollover, WALT, mark-to-market) — use `rent-roll-analyzer`.
24
25	## Input Schema
26
27	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.
28
29	\| Field \| Type \| Required \| Notes \|
30	\|---\|---\|---\|---\|
31	\| `doc_type` \| string \| no \| `rent_roll`, `t12`, `operating_statement`, or `auto` (classify from shape) \|
32	\| `as_of` \| string \| yes \| ISO date; flows unchanged into `created_at`/`updated_at`/`extracted_at`. No wall clock is used. \|
33	\| `run_id` \| string \| no \| Salts tenant pseudonyms; stamps `extraction_run_id`. \|
34	\| `tenant_id` \| string \| no \| Tenancy/worksp

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Type

document-to-database

36stars12forks

View on GitHub

How to install document-to-database?

mariourquia/cre-skills-plugin/document-to-database

$npx -y skills add mariourquia/cre-skills-plugin --skill document-to-database

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Document to Database
2
3	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.
4
5	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.
6
7	## When to Activate
8
9	Explicit triggers:
10	- "turn these documents into a database" / "ingest this data room into our schema"
11	- "document to warehouse" / "load this deal package into the model"
12	- "stand up the database-ready payload for these extracted rent rolls and T-12s"
13
14	Implicit triggers:
15	- 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.
16	- 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.
17
18	Do NOT activate for:
19	- A single rent roll where the specialized reader is the right entry point — use `rent-roll-to-database`.
20	- A single T-12 or operating statement — use `t12-to-database` / `operating-statement-to-database`.
21	- Reconciling an already-normalized rent roll against an already-normalized T-12 — use `rent-roll-t12-tieout`.
22	- Pure extraction of tokens FROM a source document — that is upstream, `document-to-data-room-extractor`.
23	- Rent-roll ANALYSIS (rollover, WALT, mark-to-market) — use `rent-roll-analyzer`.
24
25	## Input Schema
26
27	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.
28
29	\| Field \| Type \| Required \| Notes \|
30	\|---\|---\|---\|---\|
31	\| `doc_type` \| string \| no \| `rent_roll`, `t12`, `operating_statement`, or `auto` (classify from shape) \|
32	\| `as_of` \| string \| yes \| ISO date; flows unchanged into `created_at`/`updated_at`/`extracted_at`. No wall clock is used. \|
33	\| `run_id` \| string \| no \| Salts tenant pseudonyms; stamps `extraction_run_id`. \|
34	\| `tenant_id` \| string \| no \| Tenancy/worksp

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6 picks

Type