Carta Compensation Benchmarks

Benchmark Query

Look up Carta Total Compensation (CTC) market salary and equity benchmarks for a role at a specific corporation.

CRITICAL — Casing rule for ALL user-facing CTC values.

In every part of your response that the user reads — chat narration, status updates, table headers, table cells, chart titles, CSV column values, file summaries, follow-up suggestions — render CTC taxonomy values in Title Case display form, never the UPPER_SNAKE_CASE API enums. This matches the carta-compensation-rolematcher output convention so the plugin's voice is consistent.

Field	Use in user-facing text	Never
Job area	Engineering, Sales, Customer Success, Project Management, Human Resources	ENGINEER, SALES, CUSTOMER_SUCCESS, PROJECT_MANAGEMENT, HR
Focus	DevOps and Site Reliability, Account Executive, FP&A	devops and site reliability, account executive, fp&a
Level	Entry, Mid 1, Senior 1, Staff 2, VP 1, C-Level, CEO, Unknown	ENTRY, MID1, SENIOR1, STAFF2, VP1, C_LEVEL, UNKNOWN
Track	IC, Manager, Executive, Unknown	ic, manager, executive, UNKNOWN

The UPPER_SNAKE_CASE enums are only for machine handoff — i.e. the job, level, focus, is_leader parameters you pass to compensation:get:benchmark. Inside the JSON payload for the API call, keep the enum form. Outside the API call, switch to Title Case before any value reaches the user. Even in narration like "Engineering maps to ENGINEER", drop the API enum — say "Pulling Engineering benchmarks for corp 7" instead.

See carta-compensation-rolematcher → "Display → API enum tables" for the full mapping.

Use MCP, not CLI. Every API call in this skill goes through the carta MCP server's mcp__carta__call_tool tool, with compensation:* commands. Do NOT shell out to the carta CLI (carta compensation ..., carta web ..., etc.) — that bypasses the formatters, the 403 handler, and the attribution requirement. The Bash tool is allowed only for writing CSV/JSON files locally, never for calling Carta APIs.

Examples below use shorthand call_tool({"name": "compensation__get__plan", "arguments": {...}}) — read this as mcp__carta__call_tool({"name": "compensation__get__plan", "arguments": {...}}).

CRITICAL — Show only PERCENTILE columns (p25/p50/p75/p90) for all three rating types.

The compensation:get:benchmark response includes both low/mid/high bands AND p25/p50/p75/p90 percentiles. Surface only the percentiles — they are the raw market data. Skip the band fields entirely (they're a derived corp-specific target band that adds noise without adding information for benchmark queries).

Every output (chat reply, CSV, JSON) MUST include all three rating types: salary, equity, AND total cash. Don't stop at salary.

Cowork vs everywhere else — pick ONE chat surface, not both

Where the benchmark numbers actually appear depends on the client:

Client	Chat reply	Live artifact panel
Artifact callable	One-line acknowledgement + the data-source attribution line. NO markdown percentile tables.	✅ Renders the percentile tables
Claude Code, Claude Desktop, claude.ai	✅ Renders the markdown tables (the "Chat reply format" below) + the attribution line	Not available — skip the artifact path

Anti-patterns:

• ❌ In Cowork, rendering the markdown percentile tables AND the artifact panel — the data appears twice, the chat reply is noise.
• ❌ In Claude Code / Desktop / claude.ai, skipping the markdown tables on the assumption an artifact will pick up the slack — the artifact doesn't render there, so the user gets nothing.

The Excel / CSV export paths are unchanged — both clients can request a file export and it works the same way regardless.

Chat reply format (single role) — Claude Code / Desktop / claude.ai only

Skip this entire section when running in Cowork — the artifact panel renders the same percentile data and a markdown duplicate is noise. Use the one-line acknowledgement format from the "Live artifact" section below instead.

Three small tables, one per rating type. Each has 4 columns: P25, P50, P75, P90.

## Market Benchmark: [Role] at [Company]

**Salary**
| P25 | P50 | P75 | P90 |
|-----|-----|-----|-----|
| $145,000 | $164,000 | $186,000 | $210,000 |

**Total Cash Compensation (TCC)**
| P25 | P50 | P75 | P90 |
|-----|-----|-----|-----|
| $164,000 | $185,000 | $210,000 | $237,000 |

**Equity (4-Year Grant)**
| Metric | P25 | P50 | P75 | P90 |
|--------|-----|-----|-----|-----|
| FD % | 0.030% | 0.040% | 0.050% | 0.144% |
| Shares | 18,620 | 24,745 | 30,870 | 88,444 |
| Notional value | $100,000 | $133,000 | $165,000 | $474,000 |

(For peer groups ≥ $500M post money — `peer_group.notional_available: true` — put **Notional value** as the first row instead.)

**Geo Adjustment:** [location] (X.XX× salary, X.XX× equity)

---
*Data source: Companies with [peer_group_dimension_phrase] [peer_group_label]. Benchmarks released [Month YYYY].*

The [peer_group_dimension_phrase] varies by peer_group.dimension — see "Required attribution" below for the three exact phrasings. Do NOT hardcode post money valuations between.

CSV format (bulk)

One row per (job, ladder, level). Default column order (peer group < $500M post money):

job, ladder, level, currency,
salary_p25, salary_p50, salary_p75, salary_p90,
tcc_p25, tcc_p50, tcc_p75, tcc_p90,
equity_fd_pct_p25, equity_fd_pct_p50, equity_fd_pct_p75, equity_fd_pct_p90,
equity_shares_p25, equity_shares_p50, equity_shares_p75, equity_shares_p90,
equity_notional_p25, equity_notional_p50, equity_notional_p75, equity_notional_p90

For peer groups ≥ $500M post money (peer_group.notional_available: true), notional comes first:

..., equity_notional_p25..p90, equity_fd_pct_p25..p90, equity_shares_p25..p90

Field source map (from each benchmarks[i] entry):

• salary_p* → salary_benchmarks.percentiles.{p25,p50,p75,p90}
• tcc_p* → tcc_benchmarks.percentiles.{p25,p50,p75,p90}
• equity_shares_p* → equity_benchmarks.percentiles.{p25,p50,p75,p90}.as_shares
• equity_fd_pct_p* → equity_benchmarks.percentiles.{p25,p50,p75,p90}.as_fd_percentage
• equity_notional_p* → equity_benchmarks.percentiles.{p25,p50,p75,p90}.as_notional_value
• currency → salary_benchmarks.currency_code

Note: equity percentiles are nested objects (percentiles.p25.as_shares, etc.), not flat values like salary/tcc.

If a column's source field is missing for a particular row (e.g. some roles have no equity), leave that cell blank — do not invent zeros and do not drop the column.

Anti-patterns:

• ❌ Showing the user a low/mid/high table. Those are the corp's pay-band target, not market data — skip them.
• ❌ CSV with salary_low / salary_mid / salary_high columns instead of percentile columns.
• ❌ Salary-only output. The user asked for "benchmarks" — show all three rating types.
• ❌ Skipping TCC because "the user said sales benchmarks" — TCC IS a benchmark.

CRITICAL — Excel exports MUST use the branded export script

When the user asks for an Excel (.xlsx) file, you MUST call the export script below. Do not write openpyxl code yourself. Do not choose colors, fonts, or layout — the script applies Carta's official brand guidelines automatically (teal headers, alternating rows, "Powered by Carta" logo, attribution row). Any hand-rolled Excel output will have incorrect branding.

Anti-patterns:

• ❌ Writing from openpyxl import Workbook and styling cells yourself
• ❌ Choosing your own header colors (navy, blue, or anything else)
• ❌ Skipping the logo — the script embeds it automatically from the plugin's assets
• ❌ Omitting --notional-first when peer_group.notional_available is true

Excel export — exact steps

Step 1 — Build a JSON array of row objects (one dict per (job, ladder, level)). Use null for missing values, never 0 or empty string.

{
  "job": "ENGINEER", "ladder": "IC", "level": "SENIOR1", "currency": "USD",
  "salary_p25": 145000, "salary_p50": 164000, "salary_p75": 186000, "salary_p90": 210000,
  "tcc_p25": 164000, "tcc_p50": 185000, "tcc_p75": 210000, "tcc_p90": 237000,
  "equity_fd_pct_p25": 0.0003, "equity_fd_pct_p50": 0.0004, "equity_fd_pct_p75": 0.0005, "equity_fd_pct_p90": 0.00144,
  "equity_shares_p25": 18620, "equity_shares_p50": 24745, "equity_shares_p75": 30870, "equity_shares_p90": 88444,
  "equity_notional_p25": 100000, "equity_notional_p50": 133000, "equity_notional_p75": 165000, "equity_notional_p90": 474000
}

Step 2 — Write the rows to a temp JSON file (avoids shell argument length limits):

Write /tmp/benchmarks_export.json  ← the JSON array

Step 3 — Run the export script:

uv run ${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-benchmarks/scripts/export_benchmarks.py \
  --data @/tmp/benchmarks_export.json \
  --output <output_path>.xlsx \
  --attribution "<full attribution string>" \
  [--notional-first]   # required when peer_group.notional_available is true

The script handles all branding automatically — do not modify its output styling. The attribution string must also appear in the chat reply per the attribution rules above.

Live artifact — exact steps

The live artifact panel is an enhancement, not a replacement for the chat experience. Every surface gets the same data and the same Excel export path. The artifact only changes HOW that data is presented:

• Where Artifact is callable — publish the panel for every benchmark query, no opt-in trigger required. Words like "interactive", "visualize", "explore" are no longer needed. The chat reply that accompanies the panel MUST be a one-line acknowledgement (e.g. "Published the benchmark panel for at — .") plus the standard data-source attribution line (see "Required attribution" below) — and NOTHING ELSE. No markdown percentile tables, no per-rating-type sub-tables, no salary/TCC/equity numbers in chat. The artifact panel owns those numbers; duplicating them in chat is noise. The attribution stays in chat because the panel doesn't render it.
• Where it is not — present the same data inline using the "Chat reply format (single role)" or CSV/Excel export paths. Skip the artifact steps below.

Why no preview_start path? The panel's interactive controls (corp search, refetch, Download Excel) call Carta through the published artifact's mcp capability, which only a published artifact has. In preview_start, the panel would render but the buttons would silently fail. Publishing is the only surface where the panel functions fully; the chat-only path covers everywhere else equivalently.

Step 0 — Pick the rendering path

• Artifact is callable → use the artifact panel path below (Steps 1, 2, 4). Default for every benchmark query — no trigger phrase needed.
• Anywhere else → skip the artifact steps; present the data inline per "Chat reply format (single role)" + offer the Excel export when appropriate.

Step 1 — Build the benchmark payload JSON

Serialize the fetched benchmark results into this shape:

{
  "company": { "id": 7, "name": "Acme Corp" },
  "results": [ /* array of row objects — see the per-row shape below */ ],
  "version": "v3.1",
  "benchmark_version_id": 51,
  "peer_group": { "dimension": "post_money", "code": "ONE_HUNDRED_MILLION", "label": "$100M-$250M" },
  "fetchedAt": "2026-05-27T10:00:00Z"
}

benchmark_version_id is REQUIRED in the payload (the numeric benchmark_version.id from compensation:get:plan / the benchmark response — NOT the "v21.0" display string). The artifact's interactive controls (changing level, location, adding a row) re-fetch via compensation:get:benchmark and must pin the same benchmark version the pre-seed used; omitting it causes those re-fetches to fail. The top-level version string is display-only.

peer_group is REQUIRED in the payload — the same {dimension, code, label} you captured from compensation:get:plan in Step 3b. The artifact's interactive re-fetches pass <dimension>_bucket: <code> on every call so the panel's numbers match the corp's plan-configured peer group (and the CTC product UI). Omitting it makes those re-fetches fall back to a default comparable set whose values diverge from the FE — the exact mismatch users report. code is the bucket enum (e.g. ONE_HUNDRED_MILLION), NOT the label display string.

The artifact's results[] row shape is NESTED — NOT the flat CSV shape. The engine's renderTable reads r.salary.p25, r.equity.p50.notional, etc. Pre-seeding flat rows (e.g. r.salary_p25) makes every cell render as —. Use this shape per row:

{
  "job": "ENGINEER", "level": "SENIOR1",
  "ladder": "IC",
  "currency": "USD",
  "location": "San Francisco,CA,USA",
  "geo": "San Francisco-Oakland-Hayward, CA",
  "version": "v24.9",
  "error": null,
  "salary": { "p25": 145000, "p50": 164000, "p75": 186000, "p90": 210000 },
  "tcc":    { "p25": 164000, "p50": 185000, "p75": 210000, "p90": 237000 },
  "equity": {
    "p25": { "notional": 100000, "shares": 18620, "fdpct": 0.0003 },
    "p50": { "notional": 133000, "shares": 24745, "fdpct": 0.0004 },
    "p75": { "notional": 165000, "shares": 30870, "fdpct": 0.0005 },
    "p90": { "notional": 474000, "shares": 88444, "fdpct": 0.00144 }
  }
}

Field mapping from the compensation:get:benchmark response:

• salary.p* ← salary_benchmarks.percentiles.p* (numeric)
• tcc.p* ← tcc_benchmarks.percentiles.p* (numeric)
• equity.p*.notional ← equity_benchmarks.percentiles.p*.as_notional_value
• equity.p*.shares ← equity_benchmarks.percentiles.p*.as_shares
• equity.p*.fdpct ← equity_benchmarks.percentiles.p*.as_fd_percentage
• ladder ← benchmarks[i].ladder ("IC" or "LEADER"). The artifact derives the row's track from this + the level: IC → IC track; LEADER with level rank ≤8 → Manager track; LEADER with level rank ≥9 (VP1+) → Executive track. This is what makes the displayed track and per-track level name (e.g. VP1 shows as "Distinguished" on IC but "Vice President" on Executive) match the CTC product UI — pass ladder through verbatim.
• currency ← salary_benchmarks.currency_code (fall back to tcc_benchmarks.currency_code; surface null if neither is present — do NOT default to "USD")
• location ← the API location string you passed as the location param to compensation:get:benchmark (the "City,ST,USA" form, e.g. "San Francisco,CA,USA"; ",,US" for national). This is what pre-seeds the artifact's per-row location dropdown — it must be the API value, NOT the display label. Omit or set null when you queried without a location (the dropdown then seeds to "Any").
• geo ← geo_adjustment.label (the MSA display label, e.g. "San Francisco-Oakland-Hayward, CA"). Display-only — drives the read-only "Location:" line, NOT the dropdown selection. Keep it distinct from location: the label is not a valid API value and must never be sent back as the location param.
• version ← benchmark_version.version_major and version_minor concatenated as "v<major>.<minor>"
• error ← null for successful rows; populate with a short string when a per-job/level fetch failed so the table can render an explicit error cell instead of fabricating zeros

Use null for any percentile value the API didn't return — never 0 or "".

⚠ Do not confuse this with the Excel export's row shape. The Excel export script (export_benchmarks.py) consumes a flat row shape (salary_p25, equity_shares_p50, etc., documented in the Excel section above). The artifact engine consumes the nested shape documented here. Keep them separate — they are two independent contracts with different consumers.

Write the payload to /tmp/benchmark_payload_<corp_id>.json.

Step 2 — Publish the artifact panel

Read the engine HTML, replace {{CARTA_MCP_SERVER}} with the Carta connector's display name, and inject the payload as a <script> block before the engine's own JavaScript runs:

Read ${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-benchmarks/assets/artifact_engine.html

Write <script>window._BENCHMARK_PAYLOAD = <payload JSON>;</script> + engine HTML to comp-benchmarks-<company-slug>.html. The script tag MUST appear in the document before the engine's main <script> block; injecting after the engine's own boot IIFE runs is too late — the engine will treat the panel as interactive-only and skip the pre-seed.

Then check for an existing one with Artifact({action: "list", scope: "mine"}) and publish:

Artifact({
  file_path: "comp-benchmarks-<company-slug>.html",
  url: "<url of the existing panel for this company — omit on a first publish>",
  title: "Compensation Benchmarks — <Company Name>",
  description: "Compensation benchmarks for <Company Name>",
  favicon: "💰",
  capabilities: {
    mcp: { servers: [{ server: "<CARTA_MCP_SERVER>", tools: ["list_accounts", "fetch"] }] }
  }
})

The panel's own controls (corp search, refetch, Download Excel) call Carta at runtime, so both tools must be in the grant or those controls fail with not_in_manifest. Re-renders for the same company reuse the same url, so redeploying in place is the common case after the first publish.

CRITICAL — Required attribution on every benchmark response

Whenever you surface ANY Carta Total Compensation benchmark data (single lookup, bulk table, comparison, follow-up answer, CSV, Markdown, JSON export — anything that contains target $, percentile, compa-ratio, score, or per-role/level numbers), you MUST include the attribution string in EVERY output channel — chat reply AND every file you generate.

The exact string

Data source: Companies with <peer_group_dimension_phrase> <peer_group_label>. Benchmarks released <Month> <YYYY>.

Three placeholders, all required:

1. <peer_group_dimension_phrase> — depends on which peer-group dimension the corp's plan uses (peer_group.dimension from compensation:get:plan). Pick one of three exact phrasings — do NOT hardcode post money valuations between regardless of the corp:
   • post_money → "post money valuations between"
   • capital_raised → "capital raised between"
   • headcount → "headcount of"
2. <peer_group_label> — comes from compensation:get:plan → peer_group.label (e.g. "$50M-$100M", "$1M-$10M", "100-500 employees"). This identifies the band the corp is benchmarked against. Always include it — the citation is incomplete without it.
3. <Month> <YYYY> — a calendar date derived from the benchmark version's created ISO timestamp. NOT a version number.

Examples of correct values:

peer_group.dimension	peer_group.label	benchmark_version.created	Correct attribution
post_money	"$50M-$100M"	"2026-05-06T14:42:41.646134Z"	Data source: Companies with post money valuations between $50M-$100M. Benchmarks released May 2026.
post_money	"$500M-$1B"	"2026-02-15T08:00:00Z"	Data source: Companies with post money valuations between $500M-$1B. Benchmarks released February 2026.
capital_raised	"$1M-$10M"	"2025-06-26T21:19:22Z"	Data source: Companies with capital raised between $1M-$10M. Benchmarks released June 2025.
capital_raised	"$10M-$25M"	"2025-11-30T23:59:59Z"	Data source: Companies with capital raised between $10M-$25M. Benchmarks released November 2025.
headcount	"100-500 employees"	"2026-01-15T08:00:00Z"	Data source: Companies with headcount of 100-500 employees. Benchmarks released January 2026.

Anti-patterns — do NOT do these:

• ❌ Hardcoding post money valuations between for a capital_raised or headcount corp — the phrase MUST track peer_group.dimension
• ❌ Omitting the peer-group sentence — the citation must always name the comparison set
• ❌ Data source: ... released v24.6 — that's the version number, not the date
• ❌ Data source: ... released benchmark v24.6 (May 2026) — drop the version, just use the month + year
• ❌ Omitting it from the CSV because "the chat reply has it"
• ❌ Putting it only in a separate "Source" sheet without also placing it visibly in the data
• ❌ Using version_major, version_minor, or the version string anywhere in the attribution
• ❌ Using the internal enum code instead of the human label ($50M-$100M) — always use peer_group.label, never peer_group.code

Where to place it

Output type	Placement (required)
Chat reply	Last line of the message, italicized, after a --- horizontal rule
Markdown file	Last line of the file, italicized, after a --- horizontal rule
CSV file	Final row, e.g. Data source,Companies with capital raised between $1M-$10M. Benchmarks released June 2025. (use 1 cell or split across 2; both work)
JSON export	Top-level "_source": "Companies with capital raised between $1M-$10M. Benchmarks released June 2025." field

(The examples above use capital_raised phrasing as a reminder that the dimension phrase is not always "post money valuations between" — swap in the phrase that matches the corp's peer_group.dimension.)

Pre-send checklist (run before every response that touches benchmark data)

1. Did I read peer_group.dimension from the compensation:get:plan response and pick the matching phrase (post money valuations between / capital raised between / headcount of) — NOT a hardcoded "post money"?
2. Did I read peer_group.label and put it in the citation?
3. Did I derive the date from the benchmark version's created ISO timestamp? (Not from version, version_major, version_minor.)
4. Did I format it as <Month name> <YYYY> with no version number?
5. Is the attribution in the chat reply?
6. If I generated a file, is the attribution INSIDE the file too?
7. If multiple versions were used, did I list each with its own date?

If any answer is no, fix it before sending. This is non-negotiable, even when the user asks for terse output.

When to Use

• "What's the market rate for a senior engineer in San Francisco?"
• "Benchmark this role: Staff Product Manager, NYC"
• "How does our offer compare to market for a mid-level designer?"
• "What's the equity benchmark for a Director of Engineering?"
• "Show me CTC data for a [role] at [company]"

Prerequisites

1. A corporation — resolved automatically from your accounts (see Step 1 below). If you have multiple corps, you'll be asked once to pick one.
2. A role description or job title — free text is fine, the rolematcher maps it to the CTC taxonomy.

Workflow

Step 1 — Resolve corporation (REQUIRED before anything else)

Do this ONCE, upfront, before calling any compensation endpoint. Do not start fetching subscription status or plan data until you have a confirmed corporation_id.

Resolve in this priority order — stop as soon as one path succeeds:

Path 1 — Explicit numeric ID in the prompt (highest priority, no API call needed)

If the user mentioned a numeric corporation ID anywhere (e.g. "corp 7", "corp id 7", "corporation_id=7", "for company 728"), use that exact integer. Do not call list_accounts. Do not search for it. Do not substitute a similar-looking ID.

Anti-patterns:

• ❌ User says "corp 7" → agent calls list_accounts(search="7") — list_accounts searches by name substring, not ID. "7" matches every corp with "7" in its name.
• ❌ User says "corp 7" → agent picks a corp from a previous turn. Each prompt's corp ID overrides any prior context.

Path 2 — Company name in prompt

If the user named a company (e.g. "benchmarks for Acme"), call list_accounts(search="Acme"). Filter results to entries where id starts with corporation_pk:. If exactly one match, use it. If multiple, proceed to Path 4.

HARD RULE — only ever use a name and corporation_pk that appear verbatim in the list_accounts response. The corp you act on MUST be one returned by the API for this query, copied exactly. Never:

• invent, complete, or correct a company name the API didn't return,
• blend or merge two different returned names into one (e.g. seeing "Acme Labs" and "Acme Health" and proceeding with "Acme"),
• assume a corp exists because the user named it — if list_accounts(search=...) returns it, it exists; if it doesn't, it doesn't,
• reuse a name/ID remembered from earlier in the conversation instead of the current response.

If the search returns no corporation_pk: matches, do NOT guess or substitute the closest-looking corp. Tell the user you couldn't find a company by that name and ask them to confirm the exact name or give the numeric corp ID — then re-run list_accounts. A benchmark for the wrong (or a non-existent) corp is worse than asking again.

Path 3 — Single account (auto-select, no question needed)

If the user gave no corp hint at all, call list_accounts() with no search. Filter to corporation_pk: entries. If exactly one corporation is returned, use it automatically — do not ask the user to confirm something they have no choice about.

Path 4 — Multiple accounts (ask once, cleanly)

If multiple corporations are found, use AskUserQuestion immediately:

• Question: "Which company should I look up benchmarks for?"
• Options: corporation names copied verbatim from the list_accounts response (cap at 10; offer "Other" if more). Do not paraphrase, shorten, or normalize the names — present them exactly as returned so the user picks a real corp.

After the user picks, map their selection back to the exact list_accounts entry it came from and use that entry's corporation_pk. If the user typed a free-text answer via "Other" that doesn't match a returned name, treat it as a new name hint and re-run Path 2 — do not approximate it to one of the listed corps.

Do not show the user a raw JSON dump of accounts. Do not attempt any compensation call before they answer.

HARD STOP — user dismissed the question:

If the user closed the prompt, said "cancel", "never mind", or otherwise did not select an option — STOP. Do not guess a corp. Do not call any compensation endpoint. Reply:

"No problem — let me know which corporation to look up benchmarks for (name or numeric ID) when you're ready. If there's something else I can help with in the meantime, just ask."

Picking a corp the user didn't authorize would return data for the wrong company. There is no recovery from that mistake.

Path 5 — No corporations at all (Fund-Admin-only user — STOP, do not ask)

If Paths 2/3/4 returned zero corporation_pk: entries, the user may have no cap table access at all — a Fund Admin user whose access is fund accounting only. Confirm before doing anything else:

1. call_tool({"name": "context_tools__get__profile", "arguments": {}}) — returns corporations[], the corporations the user holds a cap-table role on (it excludes NO_ACCESS roles, and returns [] for a user with no corporation roles).
2. If corporations[] is empty → the user has no cap table. Send the no-cap-table CTC message from Subscription gating below (the "your firm" variant) and STOP. Do not ask them to name a corporation — they don't have one. Do not call any compensation endpoint.
3. If corporations[] is non-empty → the user does have cap tables; the name search just missed. Do not send an upsell. Ask them to confirm the exact company name or numeric corp ID (same handling as a Path 2 miss) and re-resolve.

Why profile and not list_accounts for this check: list_accounts groups corporations and funds under the same corporation_pk: prefix, so a Fund Admin user's funds can read as cap table access. context_tools:get:profile returns corporations only.

Note: list_contexts / set_context are for Fund Admin firms — they do not return corporations. Always use list_accounts for corporation lookup.

Extract the numeric corporation_pk (the integer after corporation_pk:) for all subsequent calls.

Step 2 — Verify CTC subscription (REQUIRED — HARD GATE)

STOP. The subscription check is a hard gate. Do not ask for — or even mention — the role until is_subscribed: true comes back.

Asking for the role is Step 3a, and Step 3a does not begin until this step returns is_subscribed: true. In the turn where you run the subscription check, your message to the user must contain only that you are verifying CTC access — nothing about a role, job title, level, or "in the meantime / simultaneously / while that runs." A corp with no CTC subscription (or no caller access) has no benchmark data, so any role the user gives would be wasted effort. Verify first; ask for the role only once you know there is data to return.

Anti-patterns — never do these (they defeat the gate):

• ❌ "Let me check the subscription and get the role from you simultaneously…"
• ❌ "Verifying access — meanwhile, what role do you want benchmarks for?"
• ❌ "Meetly has a subscription. What role…" bundled into the same turn as outcomes you haven't branched on yet
• ❌ Calling the carta-compensation-rolematcher skill, or asking the user for a title/level, before is_subscribed: true

The role question is a separate turn that happens after a confirmed is_subscribed: true.

call_tool({"name": "compensation__get__subscription_status", "arguments": {"corporation_id": <corporation_pk>}})

Three outcomes:

• is_subscribed: true → only now proceed to Step 3a and ask for the role.
• is_subscribed: false → stop and send the subscription message (see Subscription gating). Do not call plans/ or benchmark/ — they return empty data anyway and waste a round-trip. Do not invoke the rolematcher — don't make the user level a role we can't benchmark.
• 403 → the caller lacks a CTC role on this corp. Stop and send the no-access message (see Access gating). Do not retry or re-authenticate.

Step 3a — Map role to CTC taxonomy

Invoke the carta-compensation-rolematcher skill to classify free-text job titles, descriptions, or pasted job postings into the CTC taxonomy. Use the Skill tool, not Read:

Skill("carta-compensation-rolematcher")

Pass the user's role description as input. Do not freelance the mapping — the rolematcher has the canonical job_area / focus / level / track logic and will return values that align with the CTC enums.

When to invoke: anytime the user provides a job title or job description in the context of a benchmark/comp conversation, even if their phrasing sounds like something else. Treat all of these as rolematcher invocations:

• "What role is this?" (explicit)
• "Match this job description to the CTC taxonomy" (explicit)
• "What would be a good role for this job description?" (asking for the taxonomy match)
• "What's a good job description for this role?" (the user is showing you a JD and asking about classification — even though "for this role" sounds like reverse direction, in a benchmark context the JD is the input and the taxonomy match is the output)
• "What level is this?" / "What level does this map to?"
• "How does this fit?" (when paired with a JD or title)
• Any time the user pastes a multi-line job description after a benchmark query — they're almost always asking how it maps

If the user's question is ambiguous between "classify this for benchmark" vs "help me write the JD copy" vs "give me career advice", invoke the rolematcher first to get the classification, then use the result to answer their actual question. Do not skip the rolematcher and freelance — the user is in a comp/benchmark conversation.

Capture the output:

• job_area — passed to the API as job, NOT as job_area (see the rename note below). Must be one of: ACCOUNTING, ADMIN, CEO, CORPORATE_AFFAIRS, CUSTOMER_SUCCESS, DATA, DESIGN, ENGINEER, FINANCE, HR, IT, LEGAL, MANUFACTURING, MARKETING, OPERATIONS, PRODUCT, PROJECT_MANAGEMENT, RESEARCH, SALES, STRATEGY, SUPPORT, OTHER
• focus (e.g. "backend", "devops and site reliability", null) — job-area-dependent; the rolematcher returns lowercase multi-word strings matching the taxonomy verbatim — pass them through as-is to the API
• level — must be one of (low to high seniority): ENTRY, MID1, MID2, SENIOR1, SENIOR2, STAFF1, STAFF2, PRINCIPAL, VP1, VP2, C_LEVEL, CEO
• track — the value returned by the rolematcher (ic, manager, executive, or UNKNOWN). Map to is_leader: manager or executive → true, ic → false. If UNKNOWN, stop and ask the user before calling the API — see Error Handling.

Rename the field on handoff: job_area → job. The rolematcher's output label and the CTC product both say "job area", but the parameter on compensation:get:benchmark is job. Passing job_area returns HTTP 400 — there is no job_area parameter on any compensation endpoint.

❌ {"job_area": "ENGINEER", "level": "SENIOR1"} ✅ {"job": "ENGINEER", "level": "SENIOR1"}

focus and level keep their names; only job_area is renamed.

If the rolematcher returns a value not in these enums (e.g. LEAD1, PRODUCT_MANAGER), map it to the closest valid value before calling the API. If unsure, read the valid enum list from search_tools({"query": "compensation get benchmark"}) — do NOT guess a plausible-looking name or invent a compensation:list:* command to look it up. Ask the user if still ambiguous.

If the user provides only a job title, that is sufficient minimum input for the rolematcher.

Anti-patterns:

• ❌ Reading the rolematcher SKILL.md file directly instead of invoking the skill — the skill has tools and runtime context the inline read can't replicate
• ❌ Freelancing the taxonomy mapping ("this looks like SENIOR2 to me") — always defer to carta-compensation-rolematcher for the classification

Step 3b — Fetch the corporation's active benchmark version + peer group

call_tool({"name": "compensation__get__plan", "arguments": {"corporation_id": <corporation_pk>}})

Capture three things from the response:

• benchmark_version.id — use as benchmark_version_id in the next step.
• peer_group — {code, label, dimension, notional_available}. The label (e.g. "$50M-$100M") is required for the data-source footnote. The dimension — one of post_money / capital_raised / headcount — selects BOTH the data-source attribution phrase (see "Required attribution") AND which bucket param to pass in Step 4. Many corps default to capital_raised, NOT post_money — do not assume. The notional_available boolean tells you the equity column order (see Step 5).
• If peer_group.dimension is missing or not one of those three values, follow Step 4a (STOP).

Step 4 — Fetch the benchmark

CRITICAL — Valid enum values: read them, don't guess them. Two failure modes to avoid.

Every filter param on compensation:get:benchmark (job, level, focus, the three *_bucket params, equity_quantity) takes a fixed UPPER_SNAKE_CASE enum value. The API validates by exact enum name and returns HTTP 400 for anything else. Two things burn retries:

1. There is NO compensation:list:* command for these enums. Do not invent one. compensation:list:job_types, list:jobs, list:peer_groups, list:post_money_buckets, list:capital_raised_buckets, list:headcount_buckets — none of these exist. Calling them returns Unknown command and wastes a turn. The only compensation:list:* command is compensation:list:benchmark_versions. To see the valid filter values, read the compensation:get:benchmark command help — it enumerates every job, level, and bucket value:

search_tools({"query": "compensation get benchmark"})

Read the enum lists from that help; never self-discover via a guessed list: command.

2. Pass the enum NAME, not the human display label. The API wants MARKETING, not "Marketing"; CUSTOMER_SUCCESS, not "Customer Success" or "Customer Support"; SENIOR1, not "Senior 1". For buckets, pass the enum name (TWENTY_FIVE_MILLION), not the dollar label ("$25M-$50M"). The Title-Case forms are for user-facing text only (see the casing rule at the top of this file) — they are never valid API values. If you only have a free-text role, that's what the carta-compensation-rolematcher in Step 3a is for; it returns canonical enum names. Do not hand-translate a display label into a guessed enum.

Anti-patterns (all observed in real failures):

• ❌ call_tool({"name": "compensation__list__job_types"}) → Unknown tool. Read search_tools({"query": "compensation get benchmark"}) instead.
• ❌ job: "Marketing" / job: "Engineering" / job: "Customer Support" → HTTP 400. Use MARKETING / ENGINEER / CUSTOMER_SUCCESS.
• ❌ job: "PRODUCT_MANAGER" → HTTP 400 (invented). The value is PRODUCT. When unsure, read the help — don't guess a plausible-looking name.
• ❌ capital_raised_bucket: "$250M-$500M" (a label) or a fabricated name → HTTP 400. Pass a real CapitalRaisedBuckets name from the help.

Bucketing across many job functions? Iterate over the valid job enum names from the help — do not loop over display labels.

call_tool({"name": "compensation__get__benchmark", "arguments": {
  "corporation_id": <corporation_pk>,
  "job": <job_area>,                        # omit to get ALL job areas
  "level": <level>,                         # omit to get ALL levels for the job
  "focus": <focus>,                         # omit if null
  "is_leader": <true if track == "manager" or track == "executive" else false>,
  "benchmark_version_id": <benchmark_version.id>,
  "location": <location string>,            # optional, for geo adjustment

  # --- The corp's plan-default peer group. Include EXACTLY ONE bucket
  #     param. Do NOT include two or three. Pick the key by string-mapping
  #     `peer_group.dimension` from the plan response:
  #
  #         peer_group.dimension          key to use
  #         ────────────────────────      ─────────────────────────
  #         "post_money"               →  "post_money_bucket"
  #         "capital_raised"           →  "capital_raised_bucket

…(truncated)