Carta Compensation App

---
name: carta-compensation-app
description: >
  Serves a live, clickable CTC console — builds one corporation's Carta Total Compensation
  data into a React app on localhost, where you filter by job area, compare peer groups and
  explore the scorecard yourself. CLAUDE CODE ONLY; it serves a local web app. Use it when
  the ask is to run an app, launch a console, or explore the data live, rather than to be
  told a figure. Invoke with a corporation name or id, e.g. "launch a comp dashboard for Acme".
  Its Benchmarks and Scorecard tabs are surfaces inside the app, not separately routable.
  Route by deliverable, not subject — an answer, CSV or figure to quote belongs to a sibling
  even when the wording overlaps: use carta-compensation-benchmarks for a role's market rate,
  and carta-compensation-scorecard for roster positioning, compa-ratios or who is below market.
  So "what are our comp benchmarks" is a sibling even though it names them, while "open our
  comp benchmarks so I can filter them" is this skill. NOT for a single role lookup. READ-ONLY.
argument-hint: "<corporation name or numeric corporation id — required>"
version: 0.1.0
model: inherit
allowed-tools:
  - mcp__carta__welcome
  - mcp__carta__call_tool
  # `fetch` is for MCP *command*-style names (context_tools:get:profile). It is NOT
  # needed for the scorecard commands: call_tool works with the generated tool name,
  # which keeps the hyphen — compensation__get__employee-scorecard. Only colons
  # become `__`. Verified against a live MCP; see the failure-modes table.
  - mcp__carta__fetch
  - mcp__carta__search_tools
  - mcp__carta__list_accounts
  # `carta-prod` is the explicit production variant of the same server (`carta` is how
  # it usually registers). Nonprod connectors are deliberately absent: this skill is
  # published to customers, and Step 1 discovers whatever server is connected anyway —
  # granting a staff test connector up front would let a customer session reach it.
  - mcp__carta-prod__welcome
  - mcp__carta-prod__call_tool
  - mcp__carta-prod__fetch
  - mcp__carta-prod__search_tools
  - mcp__carta-prod__list_accounts
  - Read
  - Write
  - AskUserQuestion
  - Bash(uv run ${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/ctc_paths.py *)
  - Bash(uv run ${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/save_benchmark_result.py *)
  - Bash(uv run ${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/save_roster_page.py *)
  - Bash(uv run ${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/save_equity_refresh_page.py *)
  - Bash(uv run ${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/save_report_insights.py *)
  - Bash(uv run ${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/save_corporation_info.py *)
  - Bash(uv run ${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/build_datadir.py *)
  - Bash(uv run ${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/serve.py *)
---

<!-- 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>

<!-- [PATTERN carta-writing-style v0.0.2] [PATTERN etiquette v0.0.6] [PATTERN text v0.0.8] [PATTERN tables v0.0.12] [PATTERN carta-watermark v0.0.10] [PATTERN base v0.1.0] -->

# CTC Dashboard (local React console)

Builds a corporation's CTC benchmark baseline, writes it to a local data dir in the
**dashboard console JSON schema**, and launches the prebuilt React app via `serve.py`.
**The browser never calls the Carta MCP** — this skill fetches the data; the server only serves
JSON + the app source.

**Read-only.** The `compensation:*` MCP namespace exposes no write commands. This console
displays CTC data and (in later tabs) models scenarios locally. Plan activation, payband edits,
employee edits, offer letters, HRIS resync and every other mutation stay in the CTC product.

## No demo data — real corporation required
**Never** fabricate, synthesize, or fall back to demo data, and never launch against an empty or
partial data dir. Every dashboard runs against **one real corporation's** CTC data — either
fetched fresh or served from a prior local fetch (cache). If the invocation names no corporation,
do **not** auto-pick or guess — ask, then proceed once they answer.

## Step -1 — Confirm this client can run the console (REQUIRED, before anything else)

This skill runs local scripts (`uv run …`) and serves a React app from `127.0.0.1`. That works in
**Claude Code only**. In Cowork, Claude Desktop, or claude.ai there is no local shell and no
browser reachable at localhost, so every step below fails — and it fails at the *first* Bash call,
after the user has already waited, with a permission error that does not explain why.

**If `Bash` is not available in this session, stop before Step 0 and route instead:**

> "The CTC console runs a local web app, so it only works in Claude Code. For the same data
> here I can use **carta-compensation-benchmarks** (market rates for a role) or
> **carta-compensation-scorecard** (your roster and compa-ratios) — which would you like?"

Do not attempt a partial run, and do not fall back to summarising benchmark data in chat yourself:
those two skills already do it properly, with the attribution and PII rules this one does not carry.

## Launch order — cache-first, MCP-minimal
Building needs a full MCP sweep; **launching a warm cache needs exactly one call.** Resolve the
name and check the local cache *before* touching any MCP, then spend a single `get:plan` call on
the version gate (Step 0) to confirm the cached release is still the one the corp's plan points
at. A matching version launches on local data alone.

## Step 0 — Resolve identity + check the local cache

```bash
uv run "${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/ctc_paths.py" resolve "<corp name>"
```

Read-only; prints `slug`, `cache_root`, `raw_dir`, `dashboard_dir`, `snapshot_age_days`,
`snapshot_benchmark_version_id`, `snapshot_benchmark_version`, `snapshot_skill_version` and
`current_skill_version` without creating anything. Use the printed `dashboard_dir` **verbatim**
— never recompute a cache path.

- `snapshot_age_days=none` with `suggested_match=` lines → did-you-mean picker via
  `AskUserQuestion`.
- `snapshot_age_days=none`, no suggestions → build (Step 1).
- `snapshot_age_days=<N>` → cache hit. **Now run the version gate below** — do not launch on age
  alone.

### The version gate (REQUIRED on every cache hit)

**Age is not freshness**, and there are two independent ways a fresh-looking cache can be stale:
the **benchmark release** it holds, and the **skill version** that built it. Check both before
launching. Neither is visible on screen — a stale dashboard renders perfectly, which is exactly
what makes it worth a gate.

**Age is not freshness.** Carta publishes benchmark releases on its own cadence, and a
corporation's plan can be re-pinned to a newer release at any time — so a cache written
*yesterday* can already be several releases behind. A purely time-based gate serves superseded
percentiles under a citation that names the old release, and the figures look authoritative
because every other part of the dashboard is correct. That is a correctness bug, not a staleness
inconvenience: someone sets salaries from these numbers.

So on a cache hit, make **one** cheap call before launching (Step 1's MCP identification applies):

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

Compare its `benchmark_version.id` against the printed `snapshot_benchmark_version_id`:

| Condition | Action |
|---|---|
| ids **match** and age <30d | Launch (Step 4) silently. |
| ids **match** and age ≥30d | Offer "use cached" vs "re-fetch" — the release is current, only the fetch is old. |
| ids **differ** | **Re-fetch (Step 2), regardless of age.** Say why in one line: "Carta published a newer benchmark release (v21.0 → v25.5) — refetching." Do not offer "use cached" as the default here; the cached figures are superseded. |
| `snapshot_benchmark_version_id=none` | Treat as a mismatch and re-fetch. A cache whose version cannot be read must not be trusted. |
| The plan call fails (403/5xx) | Fall back to the age rule and **say so**: "Couldn't confirm the benchmark release is current, so this may be up to N days behind." Never silently pretend the check passed. |

#### Skill version — the second staleness axis (no MCP call)

`resolve` prints `snapshot_skill_version` (what built the cache) and `current_skill_version`
(what a rebuild would produce). Compare them:

| Condition | Action |
|---|---|
| **equal** | Nothing to say. The cache was built by this skill; launch. |
| **differ** | **Tell the user and offer a rebuild** — do not rebuild unprompted, and do not launch silently. One line naming both versions and what it means, then `AskUserQuestion`: rebuild (recommended) vs open the cached build. |
| `snapshot_skill_version=none` | A cache built before the field existed. Same as "differ": say the cache predates version tracking and offer the rebuild. |
| `current_skill_version=none` | The running skill's version could not be read. Say so, skip this check, and fall through to the benchmark gate — never treat unknown as a match. |

**Why offer rather than force**, when a benchmark mismatch re-fetches outright: superseded
percentiles are *wrong*, while a cache from an older skill is *correct but possibly incomplete*.
The figures still tie out; what may be missing is a capability — a tab, a peer-group dimension —
that this version could add. Wrong data justifies spending the user's calls without asking;
missing features do not, especially now that a default rebuild is a ~77-call sweep.

**Say what changed in terms of the dashboard, not the version string.** "Built with skill 0.1.0,
now on 0.2.0" tells the user nothing they can act on. Name the consequence: *"This dashboard was
built before peer-group switching existed, so it has no dimension or bucket pickers. Rebuilding
adds them — about 77 calls, a few minutes."* If you cannot say what a rebuild would add, say that
plainly rather than inventing a benefit.

**Version equality is the signal — do not second-guess it.** This skill's `version:` is bumped
deliberately when a change warrants rebuilding a cache; an unchanged version means the maintainers
judged the change cache-compatible. Never compare timestamps, file contents, or the plugin version
as a substitute — `plugin.json` moves whenever any sibling skill in the plugin changes, which has
nothing to do with this dashboard's data.

> **Bump `version:` in this skill's frontmatter when a change alters what a build produces** — a
> new tab, another data dimension, a different data-dir shape. That bump is the whole mechanism by
> which existing users are told their dashboard can be improved; skip it and they keep opening a
> cache that silently lacks the new capability. Leave it alone for wording, docs and fixes that
> produce a byte-identical build.

**This intentionally costs one MCP call on a warm launch.** The previous zero-call path was
faster but could not distinguish "4 days old and current" from "4 days old and 12 releases
behind" — and it is the *only* thing standing between a user and year-old compensation figures.
One `get:plan` call is the cheapest possible check: it is the same call Step 2b already makes, it
returns in one round trip, and on a match nothing else is fetched.

> **Use the plan's version, not the newest published one.** `compensation:list:benchmark_versions`
> may show a release *newer* than the plan's (releases land before plans are re-pinned). Always
> follow `get:plan` — that is what the CTC product UI shows, so pinning to a newer release would
> make this dashboard disagree with the product.

A numeric corp id instead of a name → `ctc_paths.py find-by-id <id>`.
No corporation in the invocation → `ctc_paths.py list-dashboards` to offer resuming a prior one.

**Run this silently.** The user's first line should be the greeting, not narration of the steps.
Cache age in words ("3 days old"), never as a raw `field=value`.

## Step 1 — BUILD: identify the Carta MCP + resolve the corporation

Reached **only on a build/refresh** (cache miss, stale re-fetch, or an explicit "refresh"). A
warm-cache launch needs only the MCP identification below plus the single `get:plan` call for
Step 0's version gate — not the rest of this step's resolution work.

**Identify the Carta MCP server.** Scan the tools available in the conversation for any matching
`mcp__*__welcome`. Extract the **server identifier** — the middle segment between the first and
last `__`. Examples: `mcp__carta__welcome` → `carta`; `mcp__carta-prod__welcome` → `carta-prod`.

- **None found:** stop and tell the user, do not fabricate data:
  > "No Carta MCP is connected. Building needs one — connect a Carta MCP (the **carta-cap-table**
  > plugin provides it). Your cached dashboards still open without it."
- **Exactly one:** call `mcp__<SERVER>__welcome` to verify. This is `<SERVER>`.
- **Multiple:** ask via `AskUserQuestion`. Default to `carta` (production) if present.

**Don't call any other `mcp__<SERVER>__*` tool before `welcome`** — every other command is gated.

**Classify the environment from `<SERVER>`'s name.** A name containing `test`/`sandbox`/`demo`/
`preprod`/`preproduction` (case-insensitive) → `cartaEnvironment = "nonprod"`. Everything else —
`carta`, `carta-prod`, any other name, or an opaque UUID — → `"production"`. This is a
customer-facing plugin, so an unrecognized identifier is far more likely to be a production
connector we haven't named than a staff test session. Carry this into Step 2e's `meta.json`.

**Resolve the corporation.** Priority order — stop at the first that succeeds:

1. **A numeric id in the prompt** ("corp 7") → use that integer verbatim. No lookup.
2. **A name** → `list_accounts(search="<name>")`, filtered to entries whose `id` starts with
   `corporation_pk:`. One match → use it. Several → `AskUserQuestion` with the names **copied
   verbatim** from the response.
3. **No hint** → `list_accounts()` bare. Exactly one corporation → use it silently (don't ask
   someone to confirm something they have no choice about).
4. **Zero `corporation_pk:` entries** → the caller may have no cap table at all. Check with
   `fetch({command: "context_tools:get:profile", params: {}})` — it is a command, not a flat
   tool, so `call_tool` returns "Unknown tool". If its `corporations[]` is empty, say CTC needs a
   cap table and stop. Do **not** ask them to name a corporation they don't have.

> **HARD RULE — only ever act on a name and `corporation_pk` returned verbatim by
> `list_accounts` for *this* query.** Never invent or auto-correct a company name, never blend two
> returned names, never reuse one remembered from earlier in the conversation. If the search
> returns nothing, say so and ask for the exact name or numeric id — a benchmark for the wrong
> corp is worse than asking again.

**Key the cache on the canonical corporation name** (the one the API returned, not the one typed):

```bash
uv run "${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/ctc_paths.py" resolve "<canonical name>"
```

Use its printed `raw_dir` / `dashboard_dir` as the build target. Because every build keys on the
canonical name, any typed variant or a re-fetch lands on the **same** directory — a rebuild
refreshes that cache in place and a corp can never spawn a duplicate.

Then tell the user: "✅ Resolved <canonical name>. Starting data fetch…" — the only checkpoint
between here and the launch.

## Step 2 — Fetch → raw files

> **What's happening:** pulling the corporation's plan and market benchmarks from Carta. Results
> land as raw JSON in the local cache — nothing is sent back to Carta.

Follow `references/queries.md` for the exact arguments.

> ### The capture contract — read this before the first call
>
> **Never re-type, summarise, or hand-transcribe an MCP result into a file.** A benchmark response
> is ~15k tokens of decimal strings (or numbers, for the export); retyping one is slow and a
> single wrong digit silently corrupts a salary figure that then looks authoritative in the
> dashboard. Every response reaches disk through `save_benchmark_result.py`, one of three ways:
>
> **Case 1 — the harness persisted the result to a file** (the common case for `plan` /
> `subscription_status`). The tool result says something like *"Output has been saved to …"*.
> **Read that path from the message and pass it straight through** — the payload never has to
> pass through your reply:
> ```bash
> uv run "${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/save_benchmark_result.py" \
>   <printed_result_path> "<raw_dir>/plan.json"
> ```
> Don't reconstruct the path — the location is client-dependent. Don't hunt for a sibling
> `*-blob-*.json`; the helper unwraps the envelope itself.
>
> **Case 2 — a small INLINE result** (`subscription_status`, or any small probe). **Write** the
> tool result **verbatim** to a `.raw` file with the Write tool — copy, don't paraphrase or
> reformat — then pass that file:
> ```bash
> Write <raw_dir>/<name>.raw          ← the raw tool result, unedited
> uv run "${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/save_benchmark_result.py" "<raw_dir>/<name>.raw" "<raw_dir>/<name>.json"
> ```
>
> **Case 2b — an inline result too large to retype comfortably.** Both capture scripts accept
> `-` as the source and read the payload from **stdin**, which is the right route whenever the
> harness did NOT persist the result to a file and the payload is big (a full export page is
> ~25k tokens). Pipe it rather than hand-copying it into a heredoc:
> ```bash
> pbpaste | uv run "${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/save_benchmark_result.py" - "<raw_dir>/<name>.json"
> ```
> `-` composes with `--export-page` and with `save_roster_page.py` too — verified, and it
> produces byte-identical output to the file path.
>
> **Or write the result to a `.raw` file with the Write tool**, then pass that path. The Write
> tool reproduces content exactly, so this is a legal capture route and NOT the hand-copying the
> contract forbids — the distinction is mechanical reproduction versus retyping. Do NOT use a
> shell heredoc for this: quoting mangles payloads and it invites transcription. **Verify every
> page after capture** — `--export-page` prints the row and job-area counts it captured, and they
> must match what the response reported (`row_count`, `jobs_covered`). A mismatch means the
> payload did not survive; re-capture rather than building on it.
>
> **If none of these routes is available, stop and say so** rather than retyping a benchmark
> payload by hand: that is the one thing this contract exists to prevent, and a truncated or
> mistyped page is worse than no dashboard.
>
> **Case 3 — a `compensation:export:benchmarks` page** (this is how every benchmark row is now
> fetched — see Step 2c). Same source rules as Case 1/2/2b (persisted path, verbatim `.raw` file,
> or `-` for stdin),
> but pass `--export-page` and give it `<raw_dir>` itself as the destination, not one `.json`
> file — a page covers up to 6 job areas at the recommended `job_limit`, so it fans out into that many
> `benchmark_<JOB>.json` files in one call:
> ```bash
> uv run "${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/save_benchmark_result.py" \
>   --export-page <printed_result_path_or_.raw_file> "<raw_dir>"
> ```
>
> Every case the helper does the unwrapping (bare JSON, content blocks, base64 `resource.blob`,
> string-valued `result`, or the columnar export envelope) and **exits 2 rather than write
> something the builder would misread**.
>
> **If you find yourself about to type benchmark numbers into a heredoc or a Python literal, stop
> — you are in the failure mode this contract exists to prevent.** Use Case 1, 2, or 3.

**2a. Subscription gate (REQUIRED — do this before anything else).**

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

- `is_subscribed: true` → continue.
- `is_subscribed: false` → stop, surface the subscription message, fetch nothing.
- `403` → the caller has no CTC role on this corp. Stop; do not retry.

A corp with no subscription has no benchmark data, so every later call would be wasted.

**2b. Plan.** Write the raw response to a temp file, then normalize:

```bash
uv run "${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/save_benchmark_result.py" \
  <result_path> "<raw_dir>/plan.json"
```

Capture from it: `benchmark_version.id` (pins 2c) and `peer_group.{code,label,dimension}`. If
`dimension` is missing or is not one of `post_money` / `capital_raised` / `headcount`, **stop** —
guessing produces numbers that don't match the product UI.

**2c. Benchmarks — paged bulk export, not one call per job area.**

There are **22 job areas** (listed in `references/queries.md` §3), but the fetch is now
`compensation:export:benchmarks` — a bulk **columnar** export that returns several job areas'
full matrices (every level, both the IC and LEADER ladders) in one response, replacing the old
one-call-per-job-area sweep.

**Always pass `job_limit: 6`, starting at `job_offset: 0`.** A full sweep is then **4 pages**
(22 areas ÷ 6). Do *not* omit `job_limit` hoping for a bigger page:

> **The binding limit is a 10-second SERVER TIMEOUT, not a row cap.** Omitting `job_limit`
> asks for every remaining area at once, which on a real corp takes ~17s and fails with
> `exceeded 10000ms time limit` — burning a call and returning nothing. The response *cap* is
> ~300 columnar rows (~12 areas at ~17 rows each), so the old "omit it, you'll get up to 12"
> advice is arithmetically true and operationally wrong: the request times out long before it
> hits the row cap. Verified against a live MCP — the timeout error itself recommends
> `job_limit: 6`, and 6 completes comfortably (observed 55 and 102 rows per page).

Call it with `equity_quantity: "FOUR_YEAR_GRANT"`, `benchmark_version_id`, exactly one
`<dimension>_bucket` param, `job_offset`, and `job_limit: 6`.

> **PAGING IS THE DEFAULT, not an opt-in.** A response that omits `jobs`/`job_limit` is the FIRST
> PAGE — not the whole matrix. There is no way to get all 22 areas in one call.
>
> **Keep calling with `job_offset` = the previous response's `next_job_offset` until that field
> comes back `null`.** `total_job_areas` tells you how many exist overall (22), so you can tell a
> stalled sweep from a finished one without counting job names yourself. Treating one response as
> the complete cube would silently publish 6 of 22 job areas as if it were everything — this is
> the exact failure mode the paging discipline below exists to prevent.

Capture **every page** the moment it arrives, via the export-specific mode:

```bash
uv run "${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/save_benchmark_result.py" \
  --export-page <printed_result_path_or_.raw_file> "<raw_dir>"
```

This is a **third capture path**, in addition to the two in the contract above — pass it the same
kind of source (a harness-persisted result path, or a verbatim `.raw` file you Wrote first), but
give it `<raw_dir>` itself as the destination, not a single `.json` file. It fans one page out into
one `benchmark_<JOB>.json` per job area the page covered (matching the per-job-area files the old
sweep produced, so `build_datadir.py` is unchanged), and appends the page's paging metadata to
`<raw_dir>/export_pages.json`. It prints whether the sweep is COMPLETE or INCOMPLETE after every
page — **read that line**; it is the authoritative answer to "am I done paging."

> **Never echo benchmark figures into the conversation** — they belong in the data dir. Report
> progress as "N of 22 job areas covered so far" between pages, not row counts or numbers.

**Check coverage between pages** the same way as before:

```bash
uv run "${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/build_datadir.py" \
  --raw "<raw_dir>" --check
```

Same four lines as before (`captured` / `empty` / `CORRUPT` / `plan.json`), plus a fifth when the
raw dir was fetched via the export: `EXPORT SWEEP: <warning>` if the last page's `next_job_offset`
was still non-null — i.e. paging stopped before `total_job_areas` was covered. `build_datadir.py`
**refuses to build** in that state even if every `benchmark_<JOB>.json` file happens to exist, so a
build can never publish a sweep that was cut short mid-page.

**Mid-sweep a non-zero exit is expected** — after page 1 of 2, most areas are legitimately still
`MISSING`. Only the **final** check needs to come back clean (exit 0). Do not treat a non-zero exit
between pages as a failure and re-fetch what you already have.

**When a page-level call fails** (403, 5xx, timeout, malformed response):

1. **Retry that page once**, same arguments (same `job_offset`). Transient 5xx and timeouts
   usually clear.
2. **Never fall back to per-job-area `compensation:get:benchmark` calls to route around a failing
   export call.** That is the ~22-call pattern this command exists to replace — if the export is
   failing, the fix is the export call's arguments (wrong `benchmark_version_id`, bad bucket
   param, auth), not a different endpoint.
3. **If a page fails twice**, stop before building and surface the error — with only 4 calls in
   a full sweep, a page that won't succeed after a retry means something systemic, not bad luck on
   one of 22 job areas. A timeout on a `job_limit: 6` page is NOT a reason to retry with a bigger
   limit; if 6 areas time out, a larger page certainly will.

A hard ceiling regardless of outcome: **at most 8 export calls per build** for the corp's OWN
group (4 pages plus retries). Alternates in 2c-bis carry their own ceiling. If you're about to
exceed either, stop and report — you're in a retry loop.

**2c-bis. The other buckets in the corp's dimension — this is what turns on the peer-group
dropdowns.**

The Benchmarks tab has a dimension picker and a bucket picker, and **it hides both unless the
data dir contains more than one peer group.** They read `alternatePeerGroups`, which
`build_datadir.py` assembles from `peer_<CODE>/` subdirectories of the raw dir. Fetch nothing
here and the controls never appear — not broken, just nothing to switch between.

So: after the corp's own sweep is COMPLETE, **sweep every other bucket in the same dimension.**
Same `benchmark_version_id`, same `equity_quantity: "FOUR_YEAR_GRANT"`, same `job_limit: 6` and
the same page-until-`next_job_offset`-is-null discipline — the only thing that changes is the
`<dimension>_bucket` value. The codes, in order, per dimension:

| Dimension | Bucket codes (low → high) |
|---|---|
| `post_money` | `ONE_MILLION`, `TEN_MILLION`, `TWENTY_FIVE_MILLION`, `FIFTY_MILLION`, `ONE_HUNDRED_MILLION`, `TWO_HUNDRED_FIFTY_MILLION`, `FIVE_HUNDRED_MILLION`, `ONE_BILLION` |
| `headcount` | `ONE_TO_TWENTY_FIVE`, `TWENTY_FIVE_TO_HUNDRED`, `HUNDRED_TO_FIVE_HUNDRED`, `GREATER_THAN_FIVE_HUNDRED` |
| `capital_raised` | `ONE_TO_TEN_MILLION`, `TEN_TO_TWENTY_FIVE_MILLION`, `TWENTY_FIVE_TO_FIFTY_MILLION`, `FIFTY_TO_ONE_HUNDRED_MILLION`, `ONE_HUNDRED_TO_TWO_HUNDRED_MILLION`, `GREATER_THAN_TWO_HUNDRED_MILLION` |

**Sweep all three dimensions, not just the plan's.** The app has a dimension picker as well as
a bucket picker, and it offers whatever dimensions the data dir contains — so fetching only the
plan's dimension leaves that control with one option, which is the same dead-end the bucket
picker had. Post-money valuation, headcount and capital raised are all real ways a customer
asks "who are we being compared against", and the builder already labels each group with its
own dimension and citation phrase.

The bucket param name changes with the dimension — **exactly one** per call:

| Dimension | Param |
|---|---|
| `post_money` | `post_money_bucket` |
| `headcount` | `headcount_bucket` |
| `capital_raised` | `capital_raised_bucket` |

**The plan's own dimension still leads.** The builder puts it first in the picker and the corp's
own bucket stays the default, because that is the peer set the plan actually chose — switching
bucket asks "what if we were valued higher?", while switching dimension changes what a peer *is*.
Both are legitimate; only one is the default.

> **A bucket label alone is ambiguous across dimensions.** `$1M-$10M` exists in both post-money
> and capital raised, and on a real corp they return different figures (167,000 vs 144,000 for the
> same role). That is why each group carries its own dimension and citation, and why the CSV
> filename includes the dimension. Never describe a group by its bucket label alone.

Capture each bucket into its OWN subdirectory — `--export-page` takes the destination, so point
it at `peer_<CODE>/` instead of the raw dir:

```bash
uv run "${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/save_benchmark_result.py" \
  --export-page <printed_result_path_or_.raw_file> "<raw_dir>/peer_<CODE>"
```

Use the bucket code **exactly** as spelled above: `build_datadir.py` reads the code back out of
the directory name to look up that group's label and dimension, so `peer_one_million` or
`peer_ONE-MILLION` lands as an unlabelled group in the switcher.

**Skip the corp's own bucket** — it is already the flat `benchmark_*.json` set at the top of the
raw dir, and fetching it twice would put the same group in the dropdown twice.

> ### Sweep ALL dimensions and ALL buckets by default
>
> **Unless the user has asked for something narrower, fetch every bucket in all three
> dimensions.** Do not ask first, and do not quietly pick a cheaper scope on their behalf — a
> partial sweep is the one outcome that looks like a broken app rather than a saved call. With
> one dimension the dimension picker renders a single option and hides; with one bucket both
> pickers hide, and the user sees a dashboard with no peer-group controls at all and no
> indication that anything was skipped.
>
> Narrow **only** on an explicit instruction ("just post-money", "skip the alternates", "keep it
> quick"). Then say which scope you used and which pickers it leaves empty, so the absence is a
> stated choice rather than a silent gap.

**Cost, and why it is no longer a reason to ask.** There are 18 buckets across the three
dimensions (8 post-money, 4 headcount, 6 capital raised), each a full 4-page sweep: **17
alternates × 4 = 68 extra calls**, against the ~9 for the corp's own group.

Sequentially that is minutes of wall clock, which is what the old "ask first" default was
protecting against. **Fan the sweep out across subagents instead** (see below) and those 68
calls cost roughly one bucket's wall clock, because buckets are independent. Parallelism, not a
smaller scope, is the answer to the cost.

| Scope | Calls | What the pickers show |
|---|---|---|
| **All three dimensions (DEFAULT)** | ~68 | Both pickers fully populated |
| Own dimension, all buckets | ~12–28 | Bucket picker works; dimension picker hides |
| Adjacent buckets only | ~8 | Three buckets in one dimension |
| Own bucket only | 0 extra | Neither picker appears |

The per-dimension figures differ — post-money has 7 alternates (~28 calls), capital raised 5
(~20), headcount 3 (~12) — so quote the number for the dimension actually in play rather than
28 for all three.

A verified reference point: on corp 7 the full 18-bucket sweep completed with every page
returning 200, so 68 is a measured figure rather than a guess.

Ceiling for this step: **at most 6 export calls per bucket** (4 pages plus two retries), and
**stop the whole step after 3 consecutive buckets fail**. A systemic failure — wrong
`benchmark_version_id`, auth, a bad bucket param — will fail on every bucket, and grinding
through 17 of them wastes ~100 calls to learn what the second one already told you.

**A failed alternate is not a failed build.** Alternates are additive: the corp's own group is
already captured and the dashboard is complete without them. If a bucket's sweep won't finish,
delete its partial `peer_<CODE>/` directory and move on — `build_datadir.py` already drops an
alternate with no rows and warns rather than publishing a group that renders as a blank grid,
but removing the directory keeps the warning list honest about what you actually attempted.
Report which buckets are in the switcher and which you dropped.

**2c-ter. Fan the alternates out across subagents.**

**Do the alternates in parallel, one subagent per bucket.** Buckets are independent — each is a
self-contained 4-page sweep writing to its own `peer_<CODE>/` directory, with no shared state
and no ordering between them. Sweeping them in one context is the slowest possible arrangement
and, on a client where MCP results arrive inline rather than as a file path, also the most
fragile: every page's payload has to pass through the orchestrator twice (once arriving, once
being written), and each of those hand-reproduced pages is an opportunity to corrupt a salary
figure. Delegating puts the payloads in the subagent's context instead, so the orchestrator
never handles them.

**Split one bucket per agent, 17 agents.** The harness caps concurrency (~10 at once) and
queues the rest, so this lands in about two waves and the wall clock is a couple of buckets
deep rather than 17. If the MCP starts rate-limiting, fall back to ~6 agents of ~3 buckets
each — do NOT respond to rate limiting by narrowing the scope.

**Do the version gate ONCE, in the orchestrator, before spawning anything.** Resolve the
corporation, run Step 2a/2b, and hand every agent the already-pinned `benchmark_version_id`.
Seventeen agents each calling `get:plan` is 17 wasted calls, and worse, a release published
mid-sweep could leave different buckets pinned to different versions — figures that silently
disagree across the picker.

Each agent's brief needs, at minimum:

- Its bucket code(s), and **the matching bucket param for that dimension** — `post_money_bucket`
  / `headcount_bucket` / `capital_raised_bucket`, exactly one per call.
- The pinned `benchmark_version_id`, `corporation_id`, and `equity_quantity: "FOUR_YEAR_GRANT"`.
- The absolute raw-dir path, and the destination `<raw_dir>/peer_<CODE>` — the code spelled
  **exactly** as in the table above, since the builder reads the dimension and label back out of
  the directory name.
- The paging discipline: `job_limit: 6`, page until `next_job_offset` is null.
- **The capture contract, in full.** The agent must write the tool result verbatim and pass it
  to `save_benchmark_result.py --export-page`; it must never retype or summarise a payload.
- **A verification step, done by PARSING — not by eye.** The script prints the job areas and row
  count it captured; the agent parses the response's own `jobs_covered` and `row_count` and
  compares them programmatically, then re-captures on any mismatch rather than proceeding. Say
  this explicitly in the brief: an agent told only to "confirm the counts match" will skim two
  numbers that look alike, and a single transposed digit inside a 100-row page changes neither
  the row count nor the job list. Structural comparison is what catches a payload that arrived
  intact but was written back wrong — the one failure the capture contract exists to prevent, and
  one that a real sweep has already produced and caught this way.
- The per-bucket ceiling (6 calls) and the instruction to delete a partial `peer_<CODE>/` and
  report it rather than leaving it half-swept.
- **Never echo benchmark figures in its report** — the same rule that applies here. Agents
  report bucket codes, page counts and row counts only.

**Sanity-check the figures themselves once the sweep lands.** Counts and job lists prove a page
arrived; they say nothing about whether its numbers survived being written back out. One cheap
pass over the captured rows catches what counting cannot:

```bash
uv run - <<'PY'
import json, glob, os
bad = 0; rows = 0
for f in glob.glob(os.path.expanduser("<raw_dir>/peer_*/benchmark_*.json")):
    for r in json.load(open(f)).get("benchmarks") or []:
        rows += 1
        p = (r.get("salary_benchmarks") or {}).get("percentiles") or {}
        v = [float(p[k]) for k in ("p25", "p50", "p75", "p90") if p.get(k) is not None]
        # Percentiles are ordered by construction, and a salary outside this range
        # is not a number the API returns -- either signals a mangled write.
        if v != sorted(v) or any(x < 1000 or x > 50_000_000 for x in v):
            bad += 1
print("rows", rows, "anomalies", bad)
PY
```

A transposed digit usually breaks the ordering or leaves the plausible range, so this turns a
silent corruption into a visible one for the cost of a second. It is a smoke test, not a proof —
a wrong digit that happens to preserve both properties still slips through, which is why the
per-page parsed comparison above remains the primary defence.

**Verify the fan-out centrally when the agents return.** Do not trust the reports alone — read
each bucket's `peer_<CODE>/export_pages.json` and confirm `sweep_complete: true`. A retried page
can appear twice in a manifest's `pages` list; that is harmless, because a re-capture overwrites
the same per-job-area files rather than appending, but a manifest whose `sweep_complete` is
false means that bucket really is short and must be re-run or dropped.

**2d. Employee list — one bulk export (feeds the Scorecard tab).**

Fetch this on **every build**, not on request. It is what makes the Scorecard tab exist, and
because a warm cache launches without re-fetching data (Step 0 spends one call on the version
gate only), a build that skips it produces a dashboard whose Scorecard tab is missing for the
next 30 days with no way for the user to ask for it short of an explicit "refresh".

Call **`compensation:export:scorecard`** with just `corporation_id`. It returns every
benchmarked employee in ONE columnar response — no `page`, no `page_size`, nothing to page
through. Capture it with the export flag:

```bash
uv run "${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/save_roster_page.py" \
  --export-scorecard <printed_result_path_or_.raw_file> "<raw_dir>"
```

Same source rules as the capture contract above (harness-persisted path, or a verbatim `.raw`
file you Wrote first), and the same destination convention as `--export-page`: pass `<raw_dir>`
itself, not a single `.json`. The script writes `<raw_dir>/roster_pages.json` in the same schema
the paged path produces, so `build_datadir.py` does not care which route was used.

> **Read the script's last line — it is the authoritative answer to "am I done."** It prints
> `sweep COMPLETE — N of M employees captured`. The export returns everyone or refuses, so an
> INCOMPLETE here is not "fetch another page" — it means the response was filtered or capped, and
> you should stop rather than build on it.

**Never echo employee names, salaries or compa-ratios into the conversation** — they belong in
the data dir, same rule as benchmark figures. Report only "N employees captured".

**If the export returns 400 "at most 200 fit in one export response":** the corporation is too
large for a single response. Fall back to the paged sweep below — it is slower and its pages
overlap, but it has no size ceiling. Do not narrow with `job_filters` to squeeze under the cap: a
filtered employee list silently under-reports how many people sit below market, which is the
failure this whole tab exists to surface.

Three things that will bite otherwise:

- **The generated tool name is `compensation__export__scorecard`** — colons become `__`. Unlike
  `employee-scorecard`, there is no hyphen in this one.
- **It is staff-gated.** A non-staff caller gets a permission error, not an empty list — fall
  back to the paged sweep rather than reporting the corporation has no employees.
- **`403` means no CTC role on this corp** (not "no employees"): stop, don't retry. A genuinely
  empty employee list returns `total_results: 0`.

<details>
<summary><b>Fallback — the paged sweep</b> (only when the export is unavailable or refuses)</summary>

Call `compensation:get:employee-scorecard` with **`page_size: 10`**, then page with
`page: 2, 3, …`. Capture **every page the moment it arrives** (no `--export-scorecard` flag):

```bash
uv run "${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/save_roster_page.py" \
  <printed_result_path_or_.raw_file> "<raw_dir>"
```

> **Why 10 and not 30.** A `page_size` of 30 is REJECTED — the response exceeds the 80,000-char
> transport limit. Verified against a live MCP on a 134-employee employee list. 10 is the
> documented safe value in that error message.

**Budget — the overlap makes this bigger than it looks.** Pages return overlapping rows, so a
page of 10 yields fewer than 10 NEW employees (observed ~30-40% repeats, i.e. ~6-7 new per page).
Budget **`ceil(total_results / 6) + 2` calls**. The authoritative signal is the script's `N of M`
line rising each page — stop only when a page adds **zero** new employees twice in a row.

- **The generated tool name keeps its hyphen** — `compensation__get__employee-scorecard`.
- **`page_size` is snake_case.** `pageSize` is not rejected — it is *ignored*, and you get the
  default 10 instead, i.e. a short employee list that looks real.
- **Never pass `score=LOW/MID/HIGH` to shrink a page.** It filters on the *overall* band, which
  is nullable, so it silently drops every employee without one and undercounts the list.

</details>

If the roster sweep fails outright, **still build** — `build_datadir.py` omits `roster.json`,
`snapshot.json` records `hasRoster: false`, and the Scorecard tab simply does not appear.
Losing one tab is better than losing the Benchmarks dashboard too. Say which tab is missing
and why.

**2d-bis. Equity refresh report** — the Refresh planner tab.

> ⛔ **SKIP THIS STEP.** Two separate reasons, and both still hold:
>
> **The MCP command is not released.** The export command this step needs does not
> exist in carta-mcp's registry yet, so there is nothing to call. It is named
> nowhere in this file on purpose: carta-mcp's `plugin-command-contract` check
> fails every PR in that repo when a published skill names a command the registry
> does not have, so a forward reference here blocks unrelated people's work.
>
> **The tab it feeds is hidden.** The planner's own screens are complete as of
> this change — cohort, selection, policy, review and the issuance hand-off — but
> the tab stays switched off at `app/src/App.jsx` → `SHOW_REFRESH_PLANNER = false`
> until the fetch above can actually run. Without the export command there is no
> `planner.json`, so the tab would have no data to show even if it were visible.
>
> `build_datadir` treats the report as optional — with no capture it records
> `hasPlanner: false` and the tab does not appear, exactly as it does today.
>
> **When the command ships**, restore the call and its capture step here, the
> command name in `references/queries.md` §6, and the `source` field in
> `scripts/save_equity_refresh_page.py`. The capture script and the builder are
> already written and tested; only the command reference was removed.
>
> **When the workflow ships**, flip `SHOW_REFRESH_PLANNER` to `true`, delete the
> block above it, and remove this notice. Those three must move together, or the
> docs will promise a tab the app does not show, or vice versa.

**2d-ii. The equity pool (optional).** The planner's review step measures a plan's
draw against the corporation's available pool. That figure is
`efab.available_shares` from the reports-insights endpoint — the equity ledger's own
`available`, summed across pools by compensation-service.

There is **no MCP command for it yet**, so this capture only runs when the payload
is obtained another way. When you have it:

```bash
uv run "${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/save_report_insights.py" \
  "<result path>" "<raw_dir>"
```

Skip it otherwise. The review step then states that no pool figure is in the build
instead of showing a guardrail — which is correct, because the endpoint serves a
cache primed out of band and a corporation whose ledger reports no pools sums to
exactly `0`. Neither is "this company has no shares left", so neither is ever
rendered as a zero balance.

**2d-iii. Corporation equity units (optional).** The planner can show grants as
shares, fully diluted ownership or an equity value, matching CTC's own Equity Unit
dropdown. The last two need a fully diluted share count and a per-share value that
the refresh report does not carry.

```bash
uv run "${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/save_corporation_info.py" \
  "<result path>" "<raw_dir>"
```

The source is the corporation info endpoint. **The price is not a choice** — it is
resolved server-side from the corporation's own settings, so picking between the
preferred price and the 409A value here would disagree with every other CTC
surface.

Skip it otherwise. A unit whose input is missing is dropped from the toggle rather
than shown as `$0` or `0.0000%`, exactly as the product does.

**2e. Write `meta.json`** (next to `raw_dir`, per `ctc_paths.py`):

```json
{"corporation": "<canonical name>", "corporationId": <int>, "cartaEnvironment": "production"}
```

## Step 3b — Build

```bash
uv run "${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/build_datadir.py" \
  --raw "<raw_dir>" --out "<dashboard_dir>" --meta "<meta.json>"
```

Refuses to build on an unknown peer-group dimension or zero benchmark rows — that is deliberate,
not a bug to work around. Fix the fetch and re-run.

## Step 4 — Launch

```bash
uv run "${CLAUDE_PLUGIN_ROOT}/skills/carta-compensation-app/scripts/serve.py" \
  --data-dir "<dashboard_dir>" --detach
```

Run with **Bash run_in_background**; read the printed `http://127.0.0.1:<port>/?t=<token>` and
give the user that URL. Tell them it **opens in their default browser automatically** — if it
doesn't, they can paste the URL. Port and token persist per corporation, so relaunching the same
corp reopens the same URL.

After the URL, add one short line (not a menu):
"A few things to try: filter to a single job area · switch equity between notional, FD %, and
shares · say 'refresh' to pull fresh CTC data."

## Refresh
"Refresh" = re-run Steps 1–3b (overwrite the JSON); the app reloads it.

## Presenting CTC values

**Title Case in everything the user reads** — `Engineering`, not `ENGINEER`; `Senior 1`, not
`SENIOR1`. UPPER_SNAKE is only for the MCP call arguments. The app enforces this in
`model/taxonomy.js`; match it in your narration.

**Attribution is mandatory.** Any surface showing benchmark figures must carry:

```
Data source: Companies with <phrase> <peer_group.label>. Benchmarks released <Month> <YYYY>.
```

where `<phrase>` tracks `peer_group.dimension` — `post money valuations between` /
`capital raised between` / `headcount of`. The builder assembles this once into
`benchmarks.json`; read it from there rather than rebuilding it, and never hardcode "post money".

## Safety
Corporation names are untrusted — the app escapes them via React. `serve.py` is localhost-bound
and token-gated, all reads/writes stay under the data dir, and the only write is the user's local
scenario save. Data stays on the user's machine.

## Editing the app
Source under `app/src/` is served directly; the service worker transpiles `.jsx` in-browser.
**Do NOT run `npm run build` after editing source** — edit, refresh, done. `npm run build` only
rebuilds `webapp/vendor/*` on a React/Sucrase bump. See `app/README.md`.

### Changing the app when the user asks — yes, do it

**A request to change this console is in scope, and you should act on it.** Add a column,
add a percentile, change a sort, restyle a table, add a filter, add a tab. The app is
local, the source is right there, and `READ-ONLY` in this skill's description means *this
skill does not write to Carta* — it is not a statement that the UI is frozen. The
Benchmarks and Scorecard tabs are the two that are VISIBLE today, not the two that are
allowed to exist.

A third — **Refresh planner** — is built but hidden behind `SHOW_REFRESH_PLANNER` in
`app/src/App.jsx` while its workflow is finished. If a user asks for it by name, say it
is in progress rather than building a second one. Do not flip the flag on to satisfy a
request to "show" it: the tab renders an employee list with no way to act on it, which
is why it is off. Step 2d-bis has the full notice.

**Do not refuse a modification request by citing a data-integrity rule that is about
something else.** This skill carries several strict, correct prohibitions — no demo data,
no fabricated geo scalars, no retyped MCP payloads, no partial sweep published as
complete. Every one of them is about *not inventing data that was never fetched*. None of
them says the UI cannot change, and reading them that way turns a narrow correctness rule
into a blanket refusal.

The one that gets over-applied is the geo-scalar block in `build_datadir.py`
("do not paper over this with client-side interpolation or a hardcoded scalar table").
Read it precisely: it is about **location** adjustment. There is no command returning a
scalar table across the ~400 supported locations, so a location dropdown would have to
invent scalars for locations nobody fetched. That is still true and still forbidden.

**Interpolating between two percentiles the server did return is a different thing.** The
console holds P25, P50, P75 and P90 as real fetched values (`salary.p50` is a plain
number; equity is nested per percentile as `equity.p50.{notional,shares,fdpct}`). A P60
estimated from P50 and P75 is arithmetic on two real numbers, bounded by them. Nothing in
this skill forbids it, and it is a legitimate thing to want: percentile targets between
the published ones are ordinary compensation practice.

**When you add a value the server did not return, you MUST label it.** Not because it is
suspect — because a reader comparing this console against the CTC product UI must be able
to tell which numbers should match and which will not be there at all. The console
enforces this structurally: you do not hand-render the `Tag`, and you do not
hand-write interpolated values into `benchmarks.json`. There are two edit points,
each one line, and the `Tag` renders automatically.

**Recipe A — adding a new percentile column (P30, P45, P60, etc.):**
Add one row to `PERCENTILES` in `app/src/model/taxonomy.js`:

```js
{ key: "p45", label: "P45", at: 45, fetched: false },
```

The registry then does the rest. `Benchmarks.jsx` iterates it, so a `notice`-tone
`Tag` labeled "Estimated" renders on the column header with a truthful tooltip
("Interpolated between P25 and P50"). `fillDerivedPercentiles` in
`app/src/model/format.js` computes salary/tcc/equity values at load time from
the two nearest fetched neighbors. Extrapolation is blocked: if no fetched
percentile sits both below and above `at`, the registry throws at module load.

**Recipe B — adding a new benchmark row (a new level, a job area the API did not
return, etc.):** the row goes into `benchmarks.json` alongside the fetched rows,
but you MUST set two fields on it:

```json
{ "job": "ENGINEER", "ladder": "IC", "level": "STAFF3",
  "provenance": "estimated",
  "provenanceNote": "Extrapolated from Staff 2 using XYZ scalar",
  "salary": { ... }, "tcc": { ... }, "equity": { ... } }
```

The Level cell renders `<Tag tone="notice" title={provenanceNote}>Estimated</Tag>`
whenever `provenance !== "fetched"`. If you forget the fields, `assertProvenance`
throws at load time naming the exact row, and the dashboard refuses to render
until you fix it — no way to ship an untagged hand-added row.

**In what you tell the user**, say what the estimate is derived from and that it
will not appear in the product UI. One sentence.

Two things that stay off-limits, because they are about invented data rather than UI:

- **Do not extrapolate past the fetched range.** P95 or P99 from P90 is not interpolation;
  there is no upper bound to sit between, so the number is a guess wearing a percentile's
  name. Same for a percentile below P25.
- **Do not derive a value the server already returns as a field.** Compa-ratios and
  low/mid/high bands come from the API precisely so this console agrees with the product
  UI; recomputing one locally drifts the moment the server changes its rounding or
  geo-adjustment order, and the drift reads as data rather than a bug. See the header of
  `app/src/views/Scorecard.jsx`.

If a request genuinely cannot be satisfied without inventing data — a location dropdown
being the live example — say which data is missing and what would have to exist to make it
possible, rather than declining without a reason.

## Common failure modes

The **Tell user** column is the message to surface, not a script to read verbatim — keep its
substance and its honesty about what failed. Where it is "—", the fix is silent and the user
does not need to hear about it.

| Symptom | Cause | Tell user |
|---|---|---|
| `exceeded 10000ms time limit` or `"Too many job areas"` on `compensation:export:benchmarks` | Too many areas in one call — usually `job_limit` omitted. The limit that bites first is a **10s server timeout**, not the ~300-row cap. Use `job_limit: 6` and page with `job_offset`; an explicit `job_limit` above 12 is a 400, not clamped. | "The request was too large. Retrying in smaller batches…" |
| An export page 403s / 5xxs | Transient. Retry that page once, same `job_offset`. **Never** fall back to per-job-area `compensation:get:benchmark` calls to route around it — fix the export call's arguments instead. | — (retry silently; only speak up if the retry also fails) |
| An export page fails twice | Systemic, not bad luck: one call covers up to 12 of 22 job areas, so a page that won't succeed after a retry means a wrong `benchmark_version_id`, bad bucket param, or auth. Stop before building. | "The benchmark fetch failed twice on the same page, so I stopped rather than build a partial dashboard. [the error]" |
| `build_datadir.py` reports `EXPORT SWEEP: ... stopped early` or refuses to build | Paging didn't reach `next_job_offset: null`. Fetch the remaining page(s) — check `<raw_dir>/export_pages.json` for `last_next_job_offset`. | — (fetch the rest, then build; mention only if pages cannot be completed) |
| `"Unknown tool"` on a scorecard command | The generated tool name **keeps the hyphen**: `compensation__get__employee-scorecard`. Only colons become `__`, so `..._scorecard` (underscore) is not a real tool. `call_tool` works with the correct name — verified against a live MCP. (`fetch` is not registered in current builds, so it is not the fallback either.) | — (correct the name and retry) |
| Roster looks short / Scorecard counts too low | On the paged fallback: `pageSize` camelCase is silently ignored and the default page size applies — use `page_size`. Also check you did not pass `score=`, which filters on the nullable overall band and drops unscored employees. | "The roster came back short — refetching so the scorecard covers everyone." |
| `compensation:export:scorecard` returns 400 "at most 200 fit" | The corporation is larger than one export response. Use the paged fallback in Step 2d — do NOT narrow with `job_filters`, which silently under-reports who is below market. | — (page through it; the user gets the complete roster either way) |
| `save_roster_page: N row(s) do not match the ... header` | The export's columns and rows disagree, so the payload is not what the decoder expects. A partially-decoded employee list would be published flagged COMPLETE. Re-fetch; if it repeats, the export's column set changed and the script needs updating. | "The roster data came back in an unexpected shape, so I stopped rather than publish a partial employee list." |
| `build_datadir.py` refuses: "the roster sweep is INCOMPLETE" | Paging stopped before `total_results` was reached. Check `<raw_dir>/roster_pages.json` for `distinct_employees` vs `total_results` and fetch the remaining pages. Never work around this — a partial roster under-reports how many employees are below market. | "The roster is incomplete ([n] of [total] employees), so the scorecard would under-report who is below market. Fetching the rest." |
| Scorecard tab absent on a fresh build | `snapshot.json` says `hasRoster: false`. The roster sweep failed or was skipped; re-run with "refresh". A warm cache re-fetches nothing (Step 0's one call is the version gate), so the tab cannot appear until the next build. | "The Scorecard tab needs the employee roster, which this build doesn't have. Want me to rebuild with a refresh?" |
| Numbers don't match the CTC product UI | Almost always `equity_quantity` (must be `FOUR_YEAR_GRANT`) or a missing/incorrect `*_bucket` param. | "Those figures were fetched with the wrong equity basis or peer group. Rebuilding so they match the product UI." |
| `HTTP 400` on `job`/`focus` | Passing a display label (`"Engineering"`) or a combined value (`ENGINEER/BACKEND`). Two separate params; `job` UPPER_SNAKE, `focus` lowercase. | — (fix the params and retry) |
| Builder exits "peer_group.dimension is …" | `plan.json` is missing or from a different corp. Re-fetch Step 2. | — (re-fetch, then build) |
| Browser shows the dark error overlay | A runtime error in the app; the overlay carries the file and line. | "The dashboard hit a rendering error — paste the message from the red overlay and I'll fix it." |
| Blank page, no overlay | Service worker didn't claim. The shell reloads itself after 3s as a backstop. | "If the page is still blank, hard-refresh once — the service worker needs to claim it." |