Carta Market Benchmarks

---
name: carta-market-benchmarks
description: >-
  Computed statistics across portfolio companies — median, average, typical, range — used as market benchmarks. Returns aggregate numbers and percentiles, not raw per-company listings.
when_to_use: >-
  Use when asked what's typical for a metric, what's normal at a given
  stage, what the median value is, what the average comes out to, how
  something compares to the market, what range a metric falls in across
  the portfolio, or how a specific deal's terms stack up against portfolio
  norms. Covers option pool sizing, SAFE valuation caps, SAFE discount
  rates, round sizes by stage, and similar structural metrics. For raw
  cross-company data tables without computed statistics, prefer a
  multi-company raw-data skill. For time-based risk detection across
  companies, prefer a portfolio-alerts 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>"}`
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`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 -->

# Market Benchmarks

Compute portfolio-wide benchmarks from your own Carta data: option pool sizes, SAFE valuation caps, and round sizes. Useful for sanity-checking a new deal's terms against your existing portfolio.

> **Note:** This reflects your firm's portfolio, not Carta-wide market data. Present results as "portfolio benchmarks" not "market data."

## Prerequisites

No inputs required — this skill loops the full portfolio automatically.

## Data Retrieval

### Portfolio Enumeration

Call `list_accounts`. Filter to `corporation_pk:` accounts. Extract up to 20 numeric corporation IDs. If more than 20 companies exist, ask the user to narrow scope.

### Per-Company Commands

For each company, the relevant commands are:

- `call_tool({"name": "cap_table__get__cap_table_by_share_class", "arguments": {"corporation_id": corporation_id}})` -- option pool data
- `call_tool({"name": "cap_table__get__convertible_notes", "arguments": {"corporation_id": corporation_id}})` -- SAFE/note terms (summary includes median/min/max price_cap, avg_discount, by_type)
- `call_tool({"name": "cap_table__get__financing_history", "arguments": {"corporation_id": corporation_id}})` -- round sizes (summary includes per-round cash_raised and latest_date)

The gateway defaults to `detail=summary` for all three commands. The enriched summaries include all fields needed for portfolio benchmarks — no individual records required.

> **Parallel execution**: The `fetch` tool has `readOnlyHint=true`, so Claude Code executes parallel fetch calls concurrently. Issue ALL fetch calls for ALL companies in a single response — do NOT loop company-by-company. See Workflow Step 2.

## Key Fields

From cap table (option pool):
- `option_plans[].authorized_shares`: shares authorized per plan
- `totals.total_fully_diluted`: total fully diluted share count

From convertible notes (summary):
- `median_price_cap`, `min_price_cap`, `max_price_cap`: valuation cap statistics
- `avg_discount`: average discount rate
- `by_type`: count of SAFEs vs Convertible Notes
- `total_dollar_amount`: total invested across all instruments

From financing history (summary):
- `by_round`: per-round `{count, cash_raised, latest_date}`
- `total_cash_raised`: aggregate across all rounds

## Workflow

### Step 1 — Get Portfolio

Call `list_accounts`. Filter to `corporation_pk:` accounts. Extract up to 20 numeric corporation IDs.

### Step 2 — Collect Data for All Companies (parallel)

Issue ALL fetch calls for ALL companies **in a single response** — do NOT loop company-by-company. Each fetch call is independent and will execute concurrently.

For example, with 5 companies and all 3 data types, issue all 15 fetch calls at once:

```
call_tool({"name": "cap_table__get__cap_table_by_share_class", "arguments": {"corporation_id": 1}})
call_tool({"name": "cap_table__get__convertible_notes", "arguments": {"corporation_id": 1}})
call_tool({"name": "cap_table__get__financing_history", "arguments": {"corporation_id": 1}})
call_tool({"name": "cap_table__get__cap_table_by_share_class", "arguments": {"corporation_id": 2}})
call_tool({"name": "cap_table__get__convertible_notes", "arguments": {"corporation_id": 2}})
call_tool({"name": "cap_table__get__financing_history", "arguments": {"corporation_id": 2}})
... (all companies)
```

Then from the results:

**Cap table by share class** (for option pool %):
- From `option_plans[]`: sum `authorized_shares` across all plans
- From `totals.total_fully_diluted`: compute option pool % = option_pool_authorized / total_fully_diluted

**SAFE / convertible note terms** (summary):
- Use `median_price_cap`, `min_price_cap`, `max_price_cap` directly for SAFE cap benchmarks
- Use `avg_discount` for discount benchmarks
- Use `by_type` to count SAFEs vs notes per company

**Financing history** (summary):
- Use `by_round` to identify rounds and their `cash_raised`
- Use `total_cash_raised` for aggregate amounts
- Most recent round = round with latest `latest_date`

### Step 3 — Compute Summary Statistics

For each metric, compute across companies that have data:
- **Median**, **min**, **max**
- Skip companies with no data for a given metric (don't count as zero)

Metrics:
- Option pool % (fully diluted)
- SAFE valuation cap
- Last priced round size

### Step 4 — Present Results

See Presentation section.

If the user asks about a specific company ("how does Acme's option pool compare?"), show that company's value alongside the portfolio median.

## Gates

**Required inputs**: None — portfolio enumeration is automatic.

**AI computation**: Yes — portfolio benchmark statistics (median, min, max for option pool sizes, SAFE caps, round sizes) are AI-derived from aggregated cap table data.
Trigger the AI computation gate (see carta-interaction-reference §6.2) before outputting any benchmark statistics or portfolio comparisons.

**Subagent prohibition**: Not applicable.

## Presentation

**Format**: Benchmark tables grouped by metric

**BLUF lead**: Lead with the number of companies analyzed and the most notable finding (e.g., "median option pool is 12.5% across 14 companies").

**Sort order**: By metric name (Option Pool, SAFE Caps, Round Sizes).

**Portfolio Benchmarks (N companies)**

**Option Pool Size (% Fully Diluted)**
| Metric | Value |
|--------|-------|
| Median | 12.5% |
| Range  | 8% – 20% |
| Companies with data | 14 |

**SAFE Valuation Caps**
| Metric | Value |
|--------|-------|
| Median | $8,000,000 |
| Range  | $3M – $25M |
| SAFEs analyzed | 28 |

**Last Priced Round Size**
| Metric | Value |
|--------|-------|
| Median | $5,000,000 |
| Range  | $500K – $30M |
| Companies with priced rounds | 10 |

## Caveats

- Portfolio data reflects point-in-time API calls, not a single atomic snapshot
- Companies with restricted permissions may have incomplete data
- Rate limit: maximum 20 companies per invocation
- This reflects your firm's portfolio, not Carta-wide market data — present results as "portfolio benchmarks" not "market data"