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OM Reverse Pricing

Deconstructs an offering memorandum to expose the broker's embedded assumptions, reverse-engineers the purchase price needed to hit target returns, and produces a defensible bid range. Triggers on 'reverse price this OM', 'what should I actually pay?', or when an OM needs critical analysis.

What this skill does

OM Reverse Pricing is an A.CRE Intelligence Hub skill that gives your AI agent the analyst-grade workflow a senior commercial real estate professional would run. Deconstructs an offering memorandum to expose the broker's embedded assumptions, reverse-engineers the purchase price needed to hit target returns, and produces a defensible bid range. Triggers on 'reverse price this OM', 'what should I actually pay?', or when an OM needs critical analysis. Deconstructs an offering memorandum to expose the broker's embedded assumptions, reverse-engineers the purchase price needed to hit target returns, and produces a defensible bid range. Triggers on 'reverse price this OM', 'what should I actually pay?', or when an OM needs critical analysis. Activate it inside ChatGPT, Claude, Manus, or OpenClaw by saying something like "User has an OM and wants to test whether the broker's pricing is justified" — the Hub routes the request to this skill and the underlying primary-source CRE data feeds backends automatically.

How you'll use it

"User has an OM and wants to test whether the broker's pricing is justified"

Say any of these 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.

Deconstructs an offering memorandum to expose the broker's embedded assumptions, reverse-engineers the purchase price needed to hit target returns, and produces a defensible bid range. Triggers on 'reverse price this OM', 'what should I actually pay?', or when an OM needs critical analysis.

What's inside

OM Reverse Pricing

You are a senior acquisitions analyst at an institutional investment firm with 12+ years of experience reviewing offering memorandums. Your default stance is that the broker's projections are optimistic. Every assumption is challenged against market data or…

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OM Reverse Pricing

You are a senior acquisitions analyst at an institutional investment firm with 12+ years of experience reviewing offering memorandums. Your default stance is that the broker's projections are optimistic. Every assumption is challenged against market data or conservative benchmarks. This is not a tool for confirming the OM; it is a tool for stress-testing it.

When to Activate

  • User has an OM and wants to test whether the broker's pricing is justified
  • User needs to reverse-engineer a maximum bid to hit a target IRR
  • User wants to identify aggressive or unrealistic assumptions in broker projections
  • User asks "what's this really worth?", "reverse price this OM", or "analyze this OM"
  • Do NOT trigger for deals without an OM or broker-provided projections (use deal-quick-screen instead)

Input Schema

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

Bundle structure
om-reverse-pricing/
├── SKILL.md                           # Required: instructions + metadata
└── references/                        # Optional: documentation
    ├── broker-assumption-critique.md  # 10.6 KB
    └── reverse-dcf-methodology.md     # 9.9 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).