القائمة
Financial reporting rules: grain consistency, balance sheets, double-entry ledgers, fiscal year boundaries, period-over-period calculations.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Load when working with Xata Postgres branches: forking a branch, building or testing dbt models on a branch, wiring dbt to a branch's credentials, diffing two branches, or the pre-merge impact report. Covers create_xata_branch, delete_xata_branch, get_dbt_profile, xata_branch_diff, schema_diff_branches, and pgroll migrations.
Load at Step 1 before exploring the project. Covers output shape inference, incremental model handling, and what to trust in YML.
Use this skill before writing any SQL query. Covers: output shape inference (cardinality clues from the question), efficient schema exploration, iterative CTE-based query building, structured verification loop (row count, NULL audit, fan-out check, sample inspection), error recovery protocol, saving output to result.sql and result.csv, turn budget management, and common benchmark traps.
Populate the knowledge base from dbt project research. Proposes entries across all 6 categories at org, project, and connection scopes.
Load FIRST before any dbt project work. Covers the full 8-step dbt workflow: project scanning, skill loading, validation, macro discovery, research, technical spec, SQL writing, and verification. Also covers output shape inference, incremental model handling, and what to trust in YML.
Loaded at Step 2 for the full workflow. Covers column naming, type preservation, JOIN defaults, lookup joins, sibling models, materialization, packages, and filtering rules.
| name | domain-financial |
| description | Financial reporting rules: grain consistency, balance sheets, double-entry ledgers, fiscal year boundaries, period-over-period calculations. |
When aggregating transactions or metrics, drive FROM the fact/transaction table (journal_entries, invoices, payments), not the dimension table (accounts, customers). Accounts with zero transactions MUST NOT appear in transaction reports - they have no data to aggregate.
All metrics in a report MUST operate at the same grain - mixing grains produces silently wrong totals.
Use COUNT() on a line-item fact table, not COUNT(DISTINCT document_id) - the grain is the line item, not the document, so COUNT() is consistent with SUM(amount).
NEVER use COUNT(DISTINCT) just because a key represents a document or entity - a transaction fact table's grain is the transaction row.
Do NOT use a YML column description to choose between COUNT(*) and COUNT(DISTINCT) - descriptions explain semantics, not aggregation method. The source table's grain determines the correct aggregation.
A balance sheet has exactly three components:
Query the project's configuration or organization table for fiscal year end settings - these values vary per tenant and MUST be used to compute the boundary, not hard-coded.
P&L transactions before the fiscal year boundary = Retained Earnings. P&L from the boundary onward = Current Year Earnings.
If the YML description specifies this earnings split, implement it exactly as written.
Every transaction has offsetting debits and credits - when unioning ledger entries, preserve BOTH sides.
Running balances MUST be computed with a window function ordered by transaction_date AND a deterministic tiebreaker (transaction_id or a sequence index) - ordering by date alone is non-deterministic when multiple transactions share a timestamp.
See dbt-workflow "Incremental Models and Period-Over-Period Columns" for the full rule. On first build, period-over-period columns MUST be NULL. Do NOT substitute 0.
