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tbm-data-reconciliation-mapper

Align GL data across actuals, budget, and forecast with COA mappings.

Updated Mar 19, 2026
Search Tags:#budget-variance#tbm#gl reconciliation#data alignment#coa mapping#phase 2
One-click install
npx skills add https://github.com/forgeonlabs/layerforge --skill tbm-data-reconciliation-mapper
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tbm-data-reconciliation-mapper
Source: https://github.com/forgeonlabs/layerforge/tree/main/skills/A0-data-reconciliation
Command: npx skills add https://github.com/forgeonlabs/layerforge --skill tbm-data-reconciliation-mapper

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, and includes scripts (resource) and references (resource) components.

What problem does it solve?

The TBM Data Reconciliation Mapper aligns GL structures across actuals, budget, and forecast, ensuring a single, comparable GL framework before TBM classification.

Core Features & Use Cases

  • Auto-aligns CC×GL keys, applies COA mappings, and flags gaps for fuzzy matching.
  • Produces a reconciled Tier 4 dataset ready for A1 and B2; outputs include reconciliation stats and unmatched pools.
  • Use cases include preparing data for GL-level TBM analysis when data comes from multiple source systems and COA changes.

Quick Start

Run the reconciliation with your actuals, budget, and optional forecast CSVs to generate the output JSON and Tier 4 CSV for A1.

Frequently Asked Questions about tbm-data-reconciliation-mapper

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I reconcile GL data across actuals, budget, and forecast for TBM readiness?▼

GL data reconciliation aligns cost center and GL keys across actuals, budget, and forecast datasets. By applying deterministic cleaning and optional COA mappings, it produces a comparable framework before TBM classification and variance analysis.

What happens to unmatched GL items during COA mapping and data alignment?▼

Unmatched GL items are flagged and pooled separately during data alignment. The reconciliation outputs these unmatched item pools alongside reconciliation stats to drive a Phase 2 fuzzy matching process for resolving gaps.

Do I need a COA mapping input file to prepare GL data for TBM analysis?▼

A COA mapping input is optional for preparing GL data for TBM analysis. The reconciliation applies deterministic data cleaning regardless, using the mapping when available to align structures before generating the Tier 4 dataset.

Can I use pandas to align GL structures from multiple source systems before TBM classification?▼

Yes, pandas is required to align GL structures from multiple source systems. The reconciliation auto-aligns CC and GL keys, applies COA mappings, and flags gaps to ensure a single comparable framework before TBM classification.

What is the best way to generate a reconciled Tier 4 dataset for budget variance analysis?▼

The best way to generate a reconciled Tier 4 dataset is to run reconciliation on actuals, budget, and optional forecast CSVs. This applies COA mappings and outputs a Tier 4 CSV ready for A1 and B2 analysis.

What are the limitations of deterministic data cleaning for GL reconciliation?▼

Deterministic data cleaning cannot resolve all GL mismatches from multiple source systems. It handles exact COA mapping alignments but leaves unmatched items in separate pools, requiring downstream fuzzy matching to complete reconciliation.

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