Imported from subagentceo/knowledge-engineering (cowork/skills/plugins/carta-cap-table:carta-compensation-benchmarks/SKILL.md). Install upstream with npx skills add subagentceo/knowledge-engineering --skill carta-cap-table:carta-compensation-benchmarks. Copyright stays with the author.

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.

Chat reply format (single role)

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 |

The 4-year grant view is the default — it matches the "Equity (4 year award)" columns in the CTC Total Compensation product UI (set by passing `equity_quantity=FOUR_YEAR_GRANT` to `compensation:get:benchmark`; see Step 4). If the user explicitly asks for "annual" / "NTM" / "next-twelve-months" equity, pass `equity_quantity=NTM_VESTING` instead and rename this header to **Equity (Annual NTM Vesting)**.

(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]` placeholder is required and varies by dimension — see the "Required attribution" block below for the three exact phrasings. Do NOT hardcode `post money valuations between` here regardless of what corp you're looking at; the phrase depends on `peer_group.dimension`.

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 — 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). The skill chooses one of three exact phrasings:
   • post_money → "post money valuations between"
   • capital_raised → "capital raised between"
   • headcount → "headcount of"
2. <peer_group_label> — peer_group.label from the same response (e.g. "$50M-$100M", "$1M-$10M", "100-500 employees").
3. <Month> <YYYY> — a calendar date derived from benchmark_version.created. NOT a version number.

Examples of correct values:

peer_group.dimension	peer_group.label	benchmark_version.created	Correct attribution
post_money	"$50M-$100M"	"2026-05-06T14:42:41Z"	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" when the corp's plan actually uses capital-raised or headcount — that misrepresents the comparison set
• ❌ 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

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 right phrase (post money valuations between / capital raised between / headcount of)?
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.

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 — 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.

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, call search_tools({"query": "compensation get benchmark"}) to re-read the valid enum list, or ask the user.

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", "$1M-$10M", "100-500 employees") is required for the data-source footnote. The notional_available boolean tells you the equity column order (see Step 5).
• peer_group.dimension — one of post_money / capital_raised / headcount. This tells you which bucket param to pass in Step 4 (post_money_bucket / capital_raised_bucket / headcount_bucket) AND which phrasing to use in the data-source attribution. Many corps default to capital_raised — do not assume post_money.

Step 4 — Fetch the benchmark

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"
  #         "headcount"                →  "headcount_bucket"
  #         null / unknown             →  STOP — see Step 4a below
  #
  #     The value is always `peer_group.code` from the plan response.
  "<peer_group_dimension>_bucket": <peer_group.code>,   # ONLY ONE — see mapping above

  # ⚠ DO NOT include the other two bucket params in the same call. The enum
  # values are dimension-specific (PostMoneyBuckets vs CapitalRaisedBuckets
  # vs HeadcountBuckets are disjoint), so a code value valid for one bucket
  # param is invalid for the other two — including more than one will either
  # silently override (which one wins is undefined and may change) or return
  # HTTP 400.
  #
  # Example: Meetly's plan returns peer_group = {dimension: "post_money",
  # code: "ONE_HUNDRED_MILLION", label: "$100M-$250M"}. The right call passes:
  #     "post_money_bucket": "ONE_HUNDRED_MILLION"
  # Not:
  #     "capital_raised_bucket": "ONE_HUNDRED_MILLION"   ← 400 Bad Request
  #     (also wrong: passing both — only one bucket param per call)

  # --- Match the CTC product UI's defaults so the skill's numbers tie out
  #     against what HR users see at /benchmarks/employee?corporationId=N
  "equity_quantity": "FOUR_YEAR_GRANT",          # The default in the CTC UI's "Equity (4 year award)"
                                                  # columns. Pass "NTM_VESTING" only when the user
                                                  # explicitly asks for "annual" / "NTM" equity.
  "equity_competitiveness_percentile": 50,        # Market median. Overrides the plan's per-job-area
  "salary_competitiveness_percentile": 50,        # competitiveness target. Pass a different integer
                                                  # (e.g. 75) if the user asks for a different posture.
  "industry": "all"                               # No industry filter; pass an industry enum if the
                                                  # user explicitly scopes the query to one vertical.
}})

No input pay required. This command returns raw market bands (salary, equity, total cash) directly.

Why all those defaults? The skill's output is most useful when its numbers tie out against the CTC product UI's "Employee Benchmarks" page. The frontend defaults to equity_quantity=FOUR_YEAR_GRANT, equity_competitiveness_percentile=50, salary_competitiveness_percentile=50, industry=all, plus the corp's plan-default peer group dimension. Sending the same params produces matching numbers; omitting them returns plan-derived bands instead of the percentile data HR users compare against.

Peer-group override (user-driven sensitivity analysis). When the user explicitly asks to see a different peer group than the corp's plan default ("show me $10M-$25M benchmarks instead" or "what would this look like for a 100-500 person company"), DROP the plan-default bucket param entirely and replace it with the override. Do not include both — the API's behavior when more than one bucket is non-null is undefined and may change.

So a Meetly-corp call that normally has post_money_bucket: "ONE_HUNDRED_MILLION" (plan default), when the user asks for "show me the $1M-$10M raised peer group instead", becomes:

call_tool({"name": "compensation__get__benchmark", "arguments": {
  "corporation_id": 7, "job": "ENGINEER", "level": "ENTRY",
  # post_money_bucket DROPPED — replaced by capital_raised_bucket below
  "capital_raised_bucket": "ONE_TO_TEN_MILLION",
  "equity_quantity": "FOUR_YEAR_GRANT",
  ...
}})

User-phrasing → override mapping:

Important: the naming asymmetry between the three enums is intentional, not a typo.

• PostMoneyBuckets names a bucket by its lower bound only: TEN_MILLION IS the $10M-$25M band.
• CapitalRaisedBuckets and HeadcountBuckets name a bucket by its explicit range: TEN_TO_TWENTY_FIVE_MILLION is the $10M-$25M band.

Do NOT "fix" post_money_bucket: "TEN_MILLION" to post_money_bucket: "TEN_TO_TWENTY_FIVE_MILLION" thinking it's a typo. The post-money enum has no _TO_ form — passing TEN_TO_TWENTY_FIVE_MILLION returns HTTP 400.

Reference for the full enum sets is in compensation:get:benchmark's description (run search_tools({"query": "compensation get benchmark"}) if you need to verify a specific value).

Step 4a — Unknown / missing peer_group.dimension (STOP)

If compensation:get:plan returned a peer_group.dimension value that is not one of post_money / capital_raised / headcount, OR peer_group.dimension is null / absent entirely, stop. Do not attempt the benchmark fetch. Do not guess a bucket param.

Tell the user:

"I can't pull benchmarks for [Company Name] — the corporation's plan returned an unexpected peer-group dimension (<dimension value>), so I don't know which peer-group bucket to query. This usually means compensation-service shipped a new dimension type the skill hasn't been updated for. Please reach out to the CTC team to confirm the corp's plan configuration."

Why this is a stop-not-guess: each dimension's bucket param accepts values from a different enum. Picking a default would either send a code value that's invalid for the chosen param (HTTP 400) or — worse — accidentally return data from the wrong peer-group dimension, which would mislead the user with numbers that don't match what they see in the product UI.

If the dimension is one of the three known values, continue to Step 4 above.

Step 4 — bulk-fetch nuances

Single-job bulk: omit level to get every level for one job in one call (~17 rows, fits well under the response budget).

Multi-job bulk (CSV across all functions): issue one call per job area in parallel — do not omit both job and level. The unfiltered query returns ~22 jobs × ~17 levels in a single payload that exceeds the 40K-char response budget and will be rejected with "response too large". Iterating per-job stays inside the budget and parallelizes cleanly.

Step 5 — Present results

The response shape is:

{
  "benchmarks": [
    {
      "job": "ENGINEER", "ladder": "IC", "level": "SENIOR1",
      "salary_benchmarks": {
        "yearly_salary": {"low": "158000.00", "mid": "186000.00", "high": "214000.00"},
        "percentiles": {"p25": "...", "p50": "...", "p75": "...", "p90": "..."},
        "currency_code": "USD"
      },
      "equity_benchmarks": {
        "as_fd_percentage": {"low": "...", "mid": "0.012600", "high": "..."},
        "as_shares": {"low": "...", "mid": "7717.00", "high": "..."},
        "as_notional_value": {"low": "...", "mid": "41000.00", "high": "..."},
        "percentiles": {...},
        "quantity": "FOUR_YEAR_GRANT"
      },
      "tcc_benchmarks": {
        "yearly_tcc": {"low": "...", "mid": "210000.00", "high": "..."},
        "percentiles": {...}
      },
      "geo_adjustment": {"label": "...", ...}
    },
    ...
  ],
  "count": N,
  "benchmark_version": {"id": ..., "version_major": ..., "version_minor": ..., "created": "...", "description": "..."}
}

Three rating types — show only the percentile distribution (p25/p50/p75/p90) for each. The low/mid/high band fields are derived from the corp's pay-philosophy target percentile and a configured spread; they're noise for benchmark queries. Read the percentiles directly from the response.

Null percentile handling — no fallbacks allowed: If a percentile field (p25, p50, p75, p90) is null or absent, render it as — in the table. Do not substitute yearly_salary.mid, yearly_salary.low, or any other band field. Do not infer, interpolate, or borrow values from adjacent fields. A — is correct and honest; a band value presented as a percentile is misleading.

Sparse benchmark version message: If some or all percentiles are null (older benchmark versions may only populate a subset), add a note below the table:

Some percentiles are unavailable — this corporation's plan uses an older benchmark version (v[X.Y], [Month YYYY]) with limited coverage. To see full percentile ranges, the plan can be updated to a current benchmark version.

Do not editorialize beyond this. Do not say "the data is incomplete" or suggest the data is wrong — it's accurate for that version, just limited.

Use the single-role chat reply format and CSV column structure spelled out in the CRITICAL block at the top of this file. Do not improvise different shapes.

For role-specific emphasis when there's space to call out a single number per rating:

• All roles — quote salary_benchmarks.percentiles.p50 (median market salary) and equity_benchmarks.percentiles.p50.as_notional_value (median annual equity in $)
• Sales / sales-leadership roles — lead with tcc_benchmarks.percentiles.p50 (median total cash) since variable pay is a major part of sales comp; salary alone understates the comp picture
• Equity — surface as_fd_percentage, as_shares, AND as_notional_value percentiles; users want all three (what % of company, how many shares, what's it worth)

Equity column order (tables, CSVs, chat formatting)

The order in which equity columns appear depends on the corp's peer group, surfaced by compensation:get:plan as peer_group.notional_available:

Rationale: actual notional-value benchmarks are only available for valuations ≥ $500M. Below that, the notional is a derived figure, so FD% (the structural metric) leads. Apply this ordering to:

• CSV column order: equity_fd_pct_* / equity_shares_* / equity_notional_* groups
• Markdown / chat tables: column position
• Per-row callouts: which equity number you quote first

If peer_group is missing from the plan response (very old plans, mis-configured corps), fall back to the default order (FD% → shares → notional).

If a benchmarks array entry has empty salary_benchmarks, empty equity_benchmarks, AND empty tcc_benchmarks (i.e. only geo_adjustment populated), that's a genuine data coverage gap for that role/level/version. Flag it as a separate "no data" section and exclude the row from the data table — do not emit a zero-filled row.

Version Override

If the user asks about the current benchmark version or wants to query against a different version, follow the workflow in ${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-benchmarks/references/benchmark-versions.md.

Subscription gating (REQUIRED before any benchmark query)

HARD STOP RULE: is_subscribed: false is FINAL. Do NOT call plans/ or benchmark/ for that corp. Do NOT "try anyway to see what comes back". Do NOT rationalize ("local/test environments may still return data"). The subscription_status response is authoritative — if the UI says no subscription, the API has no data, and you must respect that.

Compensation-service's plans/ and benchmark/ endpoints return 200 even for corporations that don't have an active CTC subscription — the response just has empty/null ratings. You cannot infer subscription status from those calls. That's why subscription_status exists.

Once you have resolved the corporation (Step 1), call compensation:get:subscription_status as Step 2 — before any plans/ or benchmark/ call, and before asking the user for a role (see Step 2 above). It returns {corporation_id, is_subscribed}. "First" here means first among the compensation calls, not before corp resolution — you still need a corporation_pk from Step 1 to make this call.

Single-corp query:

1. call_tool({"name": "compensation__get__subscription_status", "arguments": {"corporation_id": <id>}})
2. If is_subscribed is false:
   • Tell the user: "Compensation benchmarks require a Carta Total Compensation subscription. Visit this page to request a demo: https://carta.com/demo/total-comp/?&utm_medium=product&utm_source=carta-web&utm_campaign=ctc-plugin-inq-amer-q2-26"
   • STOP. Do not call plans/, benchmark/, benchmark_versions, or any other compensation endpoint for this corp.
   • Do not generate a CSV/JSON file for this corp. No "framework" file. No "structure-only" file. Nothing.
   • Do not "try the benchmark to see if it works anyway". The answer is no.
3. If is_subscribed is true → proceed with the benchmark workflow.

Multi-corp / bulk query:

1. Call compensation:get:subscription_status for each corp up front. Partition them into subscribed and unsubscribed lists.
2. Run benchmark queries (plans/, benchmark/) only for corps in the subscribed list. Never query the API for corps in unsubscribed.
3. In the chat reply, list unsubscribed corps separately: "The following corporations don't have an active CTC subscription and were excluded: [Corp A], [Corp B]."
4. In any generated file (CSV/JSON), unsubscribed corps must not appear as data rows. Optionally include a separate "Excluded (no CTC subscription)" section for transparency. Never invent zeros or blanks for them.

Anti-patterns to avoid

• ❌ User says "corp 728" → subscription_status returns is_subscribed: false → agent thinks "let me try the benchmark anyway to see if local/test data exists" — NO. Stop and tell the user.
• ❌ Generating a CSV with empty rows when the corp isn't subscribed — that's a wasted file with no data.
• ❌ Saying "the local test environment doesn't have benchmark data seeded" when the actual cause is "this corp doesn't have CTC". The agent's diagnosis is wrong, and the user-facing message conflates two unrelated problems.

Access gating (no-CTC-access vs. no-subscription)

A compensation:* read can fail for two unrelated reasons that must NOT be conflated, because they have different remediations:

• No subscription — the corporation doesn't have Carta Total Compensation. Remediation: a sales/demo conversation.
• No access — the corporation HAS CTC, but the current user doesn't hold a CTC role on it (Company Viewer, Compensation Manager, Company Editor, Company Admin). A user can be a stakeholder, board member, or cap-table admin without any CTC role. Remediation: an internal role grant — NOT a sales conversation.

You distinguish these — the MCP layer does not. The carta MCP gateway is a thin, domain-agnostic adapter: when a compensation command hits a 403 it surfaces the raw error, it does NOT probe subscription state or compose a friendly message. The authoritative signal for the distinction is compensation:get:subscription_status: when it succeeds it returns {corporation_id, is_subscribed} — false is no-subscription, true means a later plan/benchmark 403 is no-access. When it itself returns a 403, that 403 is the signal: the caller lacks a CTC role, so it's no-access. Step 2 already calls it up front — use its result (or its 403) to classify, per the table below.

How to classify a failure:

The no-access message (surface this verbatim):

"Your account doesn't have a CTC role for this corporation, contact a company admin for access"

This wording is deliberately neutral on subscription state: it's correct for both no-access rows above, including the subscription_status-403 case where the corp's subscription status is unknown (a 403 only establishes the caller lacks a CTC role, not that the corp has CTC). Do not assert that the corporation has Carta Total Compensation.

HARD STOP RULE: Both outcomes are FINAL. Do NOT re-authenticate, retry, or "try a different command to see if it works" — re-authentication issues a fresh token for the same user and does not grant a role they don't have. Do NOT generate a CSV/JSON file for that corp. No partial output.

Multi-corp / bulk query: call subscription_status for each corp up front; for corps that come back is_subscribed: true, a subsequent benchmark 403 means no-access. Partition and surface the two failure classes separately:

• "The following corporations don't have an active CTC subscription and were excluded: [Corp A]."
• "Your account doesn't have a CTC role for the following corporations, contact a company admin for access: [Corp B]."

Anti-patterns to avoid

• ❌ Treating the failure as a transient auth issue and asking the user to re-login. Re-authentication issues a fresh token for the same user — it does not grant a role they don't have.
• ❌ Expecting the gateway to hand you a finished, user-ready message. It returns a raw sanitized 403 — YOU classify it using the subscription_status result and compose the message.
• ❌ Showing a CTC product demo link for the access-denied case. The user needs an internal role grant, not a sales conversation — and on a subscription_status 403 you don't even know the corp lacks CTC, so a demo link would be a guess.
• ❌ Treating a 403 on subscription_status as "subscription unknown, try the benchmark anyway". A 403 there means no-access — stop and send the no-access message.

Error Handling

What next?

After delivering benchmarks, offer these follow-up options:

• Export to CSV — "Want me to export this as a CSV file?"
• Look up another role — "Want benchmarks for a different role or level?"
• Compare with a different location — "Want to see how these numbers change for a different city or country?"
• Version comparison — "Want to compare against a prior benchmark version?"