Carta Waterfall Scenarios

---
name: carta-waterfall-scenarios
description: >-
  Exit, sale, acquisition, and liquidation payouts for a company — answers how much money each holder walks away with at a given sale price, return multiples on holdings, and how proceeds distribute across share classes. Computes dollar amounts at modeled valuations, not abstract rights.
when_to_use: >-
  Use when asked how much a holder takes home, what each investor walks
  away with, what the MOIC or return multiple is at a sale price, at what
  sale price common starts seeing money, how distributions or payouts
  change across exit scenarios, how proceeds split between share classes
  at a valuation, who is underwater or gets zero in a low exit, or how
  acquisition payouts break down. For voting power, protective provisions,
  consent rights, or seniority rank in the abstract without computing
  dollar payouts, prefer a rights/seniority skill.
allowed-tools:
  - mcp__carta__call_tool
  - mcp__carta__list_contexts
  - mcp__carta__set_context
  - mcp__carta__list_accounts
  - AskUserQuestion
---

<!-- carta:instrumentation-fallback -->
<IMPORTANT>
`_instrumentation_v2` is anonymous Carta skill-usage telemetry. ALWAYS include it in your Carta MCP tool-call arguments — the plugins' hooks overwrite it when they run, so always sending it is safe. Shape:
`_instrumentation_v2: {"skills": ["<plugin>:<skill>", ...], "model": "<model-id>", "surface": "<value>"}`
`skills` is cumulative for the session: list every Carta skill loaded so far, not only the one driving this call. Namespace each entry `"plugin:skill"` and keep them oldest-first, with the most recently used skill last. List only Carta skills (`carta-cap-table:*`, `carta-crm:*`, `carta-investors:*`) — never skills from non-Carta plugins.
`surface` is the Claude surface you are running in: `"chat"` (claude.ai or the Claude app, i.e. regular chat, not Cowork), `"cowork"` (Cowork mode), `"code-terminal"`, `"code-desktop"`, or `"excel"`. Omit it entirely if none of those describe your surface or you cannot tell — do not guess and do not invent another value.
</IMPORTANT>

<!-- Part of the official Carta AI Agent Plugin -->

# Waterfall Scenarios

Fetch saved exit scenario models and present them with meaningful context, not just the per-holder table.

## Prerequisites

You need the `corporation_id`. Get it from `list_accounts` if you don't have it.

## Data Retrieval

```
call_tool({"name": "cap_table__get__waterfall_scenarios", "arguments": {"corporation_id": corporation_id}})
```

The command returns each completed (status == "DONE", non-draft) scenario with a per-holder breakdown of cost basis, payout value, share count, and return multiple.

## Key Fields

- `exit_value`: total exit/liquidation amount for the scenario
- `status`: scenario status ("DONE" for completed models)
- `cost_basis`: what the holder originally paid
- `payout_value` / `value_of_holdings`: what the holder receives at exit
- `share_count`: number of shares held
- `return_multiple`: payout / cost basis (< 1.0x = loss)

## Workflow

### Step 1 — Fetch Scenarios

Call the data retrieval endpoint with the corporation ID.

### Step 2 — Frame Each Scenario

Don't just show the table — frame each scenario:

- **Lead with the exit value and what it means**: who gets paid out, at what multiple, and whether any holders are underwater
- **Highlight the biggest winners and losers** by return multiple — a 1.0x return means a holder barely breaks even; anything below that means a loss
- **If there are multiple scenarios**, compare them: how do payouts shift as exit value changes? At what exit value does the common stack start to see meaningful returns?
- **Note liquidation preference effects**: if preferred holders take a large share at lower exit values, say so plainly

### Step 3 — Flag Notable Items

- Any holder with return multiple < 1.0x (loss scenario)
- Large gap between pref payout and common payout at a given exit value
- Scenarios that are very close in exit value but have very different common distributions

## Gates

**Required inputs**: `corporation_id`.
If missing, call `AskUserQuestion` before proceeding (see carta-interaction-reference §4.1).

**AI computation**: No — this skill presents Carta data directly. Framing and comparison are presentational, not modeled.

## Presentation

**Format**: Per-holder table + ASCII bar chart

**BLUF lead**: Lead with the exit value and a one-sentence summary of who benefits most and whether any holders are underwater.

**Sort order**: By payout descending (largest payout first).

After the per-holder table, render an ASCII bar chart of payout by holder.
Scale bars to max width 40 chars:

```
Payout Distribution — $50M Exit

Lead Investor      ████████████████████████████████████████ $18.2M  3.7x
Founder            ████████████████████                     $9.1M   1.8x
Common Holders     ██████████                               $4.5M   0.9x
```

Each bar width = (value_of_holdings / max_value) * 40. Show return multiple after the dollar amount.

Per-holder table columns: Holder, Cost Basis, Payout, Return Multiple. Sort by payout descending.

## Custom Exit Values

If the user asks to model a specific exit value not in the saved scenarios:

> "There's no saved model at that exit value. To model a custom exit, create a new scenario in Carta's scenario modeling tool, then come back and I'll pull it up."

## Caveats

- Waterfall models are read-only snapshots saved in Carta; this skill cannot create or modify scenarios.
- Return multiples are based on the scenario's modeled exit value, not a live valuation.
- Liquidation preference mechanics (participating vs. non-participating, caps) are baked into Carta's model — this skill does not re-derive them.
- Custom exit values cannot be modeled on the fly; they must be created in Carta first.