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Fund Raise Negotiation Engine

Tracks LP-by-LP capital raise negotiations with persistent state, models fee concession impact in real-time including MFN cascade analysis, and maintains a live ledger of blended fund economics as commitments lock in. Scales from 10-LP seed funds to 300+ LP institutional raises.

What this skill does

Fund Raise Negotiation Engine is an A.CRE Intelligence Hub skill that gives your AI agent the analyst-grade workflow a senior commercial real estate professional would run. Tracks LP-by-LP capital raise negotiations with persistent state, models fee concession impact in real-time including MFN cascade analysis, and maintains a live ledger of blended fund economics as commitments lock in. Scales from 10-LP seed funds to 300+ LP institutional raises. Tracks LP-by-LP capital raise negotiations with persistent state, models fee concession impact in real-time including MFN cascade analysis, and maintains a live ledger of blended fund economics as commitments lock in. Scales from 10-LP seed funds to 300+ LP institutional raises. Activate it inside ChatGPT, Claude, Manus, or OpenClaw by saying something like "Use the Fund Raise Negotiation Engine 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 Fund Raise Negotiation Engine skill"

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

What you give it / What you get back

You give it

See the SKILL.md preview below.

You get back

  • Confirmation summary with a standard terms table
  • close schedule
  • MFN configuration. State file written to `~/.cre-skills/fund-raises/{fund-id}.json`

Skill activation rules

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

Tracks LP-by-LP capital raise negotiations with persistent state, models fee concession impact in real-time including MFN cascade analysis, and maintains a live ledger of blended fund economics as commitments lock in. Scales from 10-LP seed funds to 300+ LP institutional raises.

What's inside

Fund Raise Negotiation Engine

You are a senior IR strategist and fund formation economics analyst with deep experience managing LP-by-LP capital raise negotiations for institutional CRE private equity funds. You maintain a persistent, session-spanning ledger of every LP's…

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Fund Raise Negotiation Engine

You are a senior IR strategist and fund formation economics analyst with deep experience managing LP-by-LP capital raise negotiations for institutional CRE private equity funds. You maintain a persistent, session-spanning ledger of every LP's negotiation status, commitment amount, and negotiated terms. You model fee concession impact in real-time -- including MFN cascade analysis, blended fee calculations, GP promote sensitivity, and breakeven analysis -- so the IR team always has an accurate picture of their fund's blended economics as commitments lock in.

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

Bundle structure
fund-raise-negotiation-engine/
├── SKILL.md                         # Required: instructions + metadata
└── references/                      # Optional: documentation
    ├── blended-fee-benchmarks.yaml  # 21.8 KB
    ├── fee-negotiation-playbook.md  # 21.2 KB
    └── mfn-cascade-mechanics.md     # 15.8 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).