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T12 Normalizer

Normalizes trailing 12-month operating statements for CRE acquisition underwriting. Removes one-time items, reprices below-market contracts, adjusts for tax reassessment, grosses up for vacancy, reclassifies capital items from opex, benchmarks against IREM/BOMA standards, and generates a questions-for-seller list that eliminates a full round-trip of due diligence clarification.

What this skill does

T12 Normalizer is an A.CRE Intelligence Hub skill that gives your AI agent the analyst-grade workflow a senior commercial real estate professional would run. Normalizes trailing 12-month operating statements for CRE acquisition underwriting. Removes one-time items, reprices below-market contracts, adjusts for tax reassessment, grosses up for vacancy, reclassifies capital items from opex, benchmarks against IREM/BOMA standards, and generates a questions-for-seller list that eliminates a full round-trip of due diligence clarification. Normalizes trailing 12-month operating statements for CRE acquisition underwriting. Removes one-time items, reprices below-market contracts, adjusts for tax reassessment, grosses up for vacancy, reclassifies capital items from opex, benchmarks against IREM/BOMA standards, and generates a questions-for-seller list that eliminates a full round-trip of due diligence clarification. Activate it inside ChatGPT, Claude, Manus, or OpenClaw by saying something like "Use the T12 Normalizer skill" — the Hub routes the request to this skill and the underlying primary-source CRE data feeds backends automatically.

How you'll use it

"Use the T12 Normalizer skill"

Say something like this to your AI agent in the Hub, Claude, or ChatGPT to activate this skill.

Skill activation rules

Detailed routing logic, prompts the skill responds to, and operational guardrails as documented by the author.

Normalizes trailing 12-month operating statements for CRE acquisition underwriting. Removes one-time items, reprices below-market contracts, adjusts for tax reassessment, grosses up for vacancy, reclassifies capital items from opex, benchmarks against IREM/BOMA standards, and generates a questions-for-seller list that eliminates a full round-trip of due diligence clarification.

What's inside

T-12 Operating Statement Normalizer

You are a CRE acquisition underwriting engine specializing in operating statement normalization. Given a seller's trailing 12-month operating statement, you restate it to reflect a buyer's go-forward economics: removing one-time items,…

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T-12 Operating Statement Normalizer

You are a CRE acquisition underwriting engine specializing in operating statement normalization. Given a seller's trailing 12-month operating statement, you restate it to reflect a buyer's go-forward economics: removing one-time items, repricing below-market contracts, adjusting for tax reassessment, grossing up for vacancy, and reclassifying capital items. Every adjustment is documented, every assumption explicit, and every anomaly generates a specific question for the seller. A $10/unit error in opex at a 5.5% cap rate moves value by $2,182/unit -- precision matters.

When to Activate

Trigger on any of these signals:

Full instructions and any reference files ship in the .skill bundle.

Bundle structure
t12-normalizer/
├── SKILL.md                    # Required: instructions + metadata
└── references/                 # Optional: documentation
    └── normalization-rules.md  # 14.7 KB

Who built it

With contributions from Avi Hacker.

How to run it

Not in the Hub? Download the .skill bundle above and follow the A.CRE skills install guide → to load it into Claude Desktop, ChatGPT, Cursor, or any other agent that supports skills.

Already in the Hub. If you're an AI.Edge Pro or A.CRE Accelerator member, this skill is bundled into the A.CRE Intelligence Hub direct connector (MCP server) — just ask your agent.

About this skill

Third-Party skill. This skill is redistributed under the Apache-2.0 license and is not authored or endorsed by Adventures in CRE. Use is governed by the upstream license and the original creator's terms. Original attribution: Mario Urquia (link) and contributors (Avi Hacker).