salary-benchmarking – Coding skill for AI agents

Salary Benchmarking

What it is: Market intelligence.

Overview

Works out the smallest useful Salary Benchmarking setup for the business in front of it, then

builds it only when asked. The default output is a short recommendation, not a

spreadsheet. Artifacts - CSV, SQL DDL, JSON Schema, Notion mapping - are produced on

request, from one field list so they cannot drift apart.

Layer: Layer 2: Acquire. Fits: Scale stage. Table code: n/a.

When to Use This Skill

• salary benchmark
• compensation benchmarking
• market pay research
• salary band planner

Also use it when the user says "market intelligence", or describes the same process happening in a

spreadsheet, a document or someone inboxes.

Do not use it for: payroll calculation, tax filing, or legal advice. This skill produces

empty templates only - it never holds or processes real employee or customer data.

How It Works

Follow the shared execution contract. The module-specific rules below define only domain fields, decisions, calculations, and safety constraints.

Step 1 - Identify intent

Read the request and pick the intent before asking anything.

• "set up" or "build" or "create" -> the user wants artifacts; go to Step 2.
• "our process is ..." or "it is in a sheet" -> the user wants to move an existing process; capture it, then Step 2.
• "is this right" or "review" or "audit" -> the user wants a check, not a build; answer from what they share.
• "how do I ..." -> advice question; answer directly and offer the build only if it helps.

Ask only if this is the highest-value missing fact; otherwise proceed without an opener:

> Q: Which roles need benchmarking?

Step 2 - Ask only what is missing

Skip anything the user already answered, in any earlier message. Ask the rest one at a

time, and stop as soon as the remaining answers would not change the output.

• Roles - Which roles? / How many grades? / All roles or a few?
• Market - Which market or city? / Currency? / Industry benchmark?
• Bands - Do you have bands now? / Fixed or negotiable? / Who approves?
• Current process - Where does pay data come from? / Last reviewed when? / Gaps?
• Outcome - What do you need? / Market ranges, internal bands or a comparison?

Never invent an answer. If the user does not know, record it as unknown and carry on.

Step 3 - Hold the internal context

Hold the answers in this shape. It stays internal - it is not shown to the user unless

they ask, and it never carries a value the user did not give.

module: salary-benchmarking
intent: null            # setup | advice | review | fix | build | convert | export
scale: null             # Starter | Growth | Scale, only if the answer changes it
areas:
  "Roles": null
  "Market": null
  "Bands": null
  "Current process": null
  "Outcome": null
requested_outputs: []   # csv | sql | json | notion | xlsx - requested formats only
confirmed_facts: []     # only what the user actually said
open_questions: []      # the unanswered ones, in the order worth asking

Step 4 - Recommend the smallest workflow

If an artifact was requested, build it after resolving essential missing facts. Otherwise give a short recommendation and offer the relevant artifact.

Recommended approach: Benchmark the roles that are hardest to fill first, and store market range beside internal band so the gap is visible.

Why this one: Benchmarking everything at once never finishes. Three hard-to-fill roles tell you more than fifty that are already correctly paid.

Workflow: Role list → Market data → Internal band → Gap analysis → Band decision

Step 5 - Build only on request

Once the user asks for it, derive the fields from the confirmed context and emit the

requested artifacts. For machine-readable text, keep prose outside the data; for files,

provide a usable link. Report material validation failures or limitations separately.

**A selected Notion output is rendered by notion-manual-import, so route the

Notion step there.** When the user selects Notion, hand that step to

@notion-manual-import: it holds the CSV, the property

mapping, the import steps and the verification checklist, and it renders the Field

Reference below instead of defining a table of its own. Do not restate the mapping

here and do not improvise the import steps. Manual CSV and mapping outputs need no

connection. For requested workspace changes, follow the shared contract: verify actual

tool access and the target before writing. A user saying "connected" is not tool evidence.

Never ask for a Notion password or token.

For an Excel-compatible CSV, use UTF-8 with a byte order mark so Excel opens the

text correctly. A CSV is not an .xlsx workbook; create .xlsx only when the user

requests a workbook.

A CSV carries no types, so after it, name the columns

that need a number, date or currency format applied.

Role Title,Benchmark ID,Currency,Data Source,Department,Grade Level,Internal Max,Internal Min,Last Updated,Market Max,Market Median,Market Min,Notes
Delivery Manager,,INR,Manual,Delivery,L1,1600000.00,1200000.00,2026-01-15,1700000.00,1450000.00,1250000.00,"Benchmarks refreshed in February; two grades sit below the market midpoint."

CREATE TABLE salary_benchmarking (
  role_title VARCHAR(255),
  benchmark_id SERIAL PRIMARY KEY,
  currency VARCHAR(255),
  data_source VARCHAR(255),
  department VARCHAR(255),
  grade_level VARCHAR(100) NOT NULL,
  internal_max NUMERIC(14,2) NOT NULL,
  internal_min NUMERIC(14,2) NOT NULL,
  last_updated DATE NOT NULL,
  market_max NUMERIC(14,2) NOT NULL,
  market_median NUMERIC(14,2) NOT NULL,
  market_min NUMERIC(14,2) NOT NULL,
  notes TEXT,
  created_at TIMESTAMP DEFAULT NOW(),
  updated_at TIMESTAMP DEFAULT NOW()
);

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "title": "Salary Benchmarking",
  "type": "object",
  "additionalProperties": false,
  "properties": {
      "Role Title": { "type": "string" },
      "Benchmark ID": { "type": "integer" },
      "Currency": { "type": "string" },
      "Data Source": { "type": "string" },
      "Department": { "type": "string" },
      "Grade Level": { "type": "string" },
      "Internal Max": { "type": "number" },
      "Internal Min": { "type": "number" },
      "Last Updated": { "type": "string", "format": "date" },
      "Market Max": { "type": "number" },
      "Market Median": { "type": "number" },
      "Market Min": { "type": "number" },
      "Notes": { "type": "string" }
  },
  "required": [
      "Grade Level",
      "Internal Max",
      "Internal Min",
      "Last Updated",
      "Market Max",
      "Market Median",
      "Market Min"
  ]
}

| CSV column | Notion property | Set after import |
|---|---|---|
| Role Title | Title | Use as the database title |
| Benchmark ID | Text (preserve source ID) | Keep imported IDs as Text; optionally add a separate Unique ID property |
| Currency | Text | Leave as Text |
| Data Source | Text | Leave as Text |
| Department | Text | Leave as Text |
| Grade Level | Select (add options after import) | Convert to Select, add options: "L1", "L2", "L3", "L4", "L5", "M1", "M2" |
| Internal Max | Number (format: currency) | Convert to Number, set format to Currency |
| Internal Min | Number (format: currency) | Convert to Number, set format to Currency |
| Last Updated | Date | Convert to Date |
| Market Max | Number (format: currency) | Convert to Number, set format to Currency |
| Market Median | Number (format: currency) | Convert to Number, set format to Currency |
| Market Min | Number (format: currency) | Convert to Number, set format to Currency |
| Notes | Text | Leave as Text |

The rows above are documentation examples only. Emit empty templates unless the user explicitly requests examples. Money stays currency, dates stay date,

and anything pointing at another table stays relation.

Field Reference

| # | Field | Type | SQL | JSON Schema | Notion | CSV example |

|---:|---|---|---|---|---|---|

| 1 | Role Title | text | VARCHAR(255) | string | Text | Delivery Manager |

| 2 | Benchmark ID | id | SERIAL PRIMARY KEY | integer | Text (preserve source ID) | (blank) |

| 3 | Currency | text | VARCHAR(255) | string | Text | INR |

| 4 | Data Source | text | VARCHAR(255) | string | Text | Manual |

| 5 | Department | text | VARCHAR(255) | string | Text | Delivery |

| 6 | Grade Level | select | VARCHAR(100) | string | Select (add options after import) | L1 |

| 7 | Internal Max | currency | NUMERIC(14,2) | number | Number (format: currency) | 1600000.00 |

| 8 | Internal Min | currency | NUMERIC(14,2) | number | Number (format: currency) | 1200000.00 |

| 9 | Last Updated | date | DATE | string, format: date | Date | 2026-01-15 |

| 10 | Market Max | currency | NUMERIC(14,2) | number | Number (format: currency) | 1700000.00 |

| 11 | Market Median | `curren

… (full content is in the download package)