Skill

Salary Benchmarking

Define a register comparing supplied market pay ranges with internal bands for hard-to-fill roles.


53
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Updated 17 days ago
Source checked Oct 7, 2026
Version 19.0.1

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Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/ag-salary-benchmarking | bash

After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.

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Overview

Salary Benchmarking

Define a register comparing supplied market pay ranges with internal bands for hard-to-fill roles. Requested empty artifacts share one field list. Use it when the business needs a salary-band planner or structured comparison of externally obtained market data and internal pay bands.

What it does

Salary Benchmarking defines a Scale-stage Acquire workflow for compensation market intelligence. Start with the hardest-to-fill roles rather than benchmarking everything at once. Store the market range beside the internal band so the difference is visible. The process identifies roles, obtains market data, records internal bands, analyzes gaps and supports a band decision. The register retains minimum, median and maximum market values, internal minimum and maximum, grade, department, currency, data source and update date. These are fields for supplied evidence rather than a promise of live salary research.

The default deliverable is a short recommendation. An explicit setup, build or conversion request produces artifacts after essential missing facts are resolved. Advice receives a direct answer, while a review works from the material provided. This keeps the size of the proposed setup tied to the requested outcome.

When to use - and when NOT to

Use it when the business needs a salary-band planner or structured comparison of externally obtained market data and internal pay bands. It does not provide market data; acquire or pay for the benchmark source separately. It does not calculate payroll or authorize pay changes.

This is an empty-template workflow, not processing of real employee or customer records. Automation, reminders and synchronization require the separate integration layer. Sensitive decisions and adjustments require qualified human review before anything is acted on.

Inputs and outputs

Establish roles and grades in scope; market or city, currency and industry benchmark; current bands, flexibility and approval owner; pay-data source, last review and gaps; and whether market ranges, internal bands or a comparison are wanted. Questions are asked one at a time, excluding anything already answered in earlier messages. Unknown facts stay unknown. Intent, confirmed facts, open questions and requested formats remain internal unless the user asks to inspect them; no missing answer is invented.

The field model includes Role Title (text); Benchmark ID (id); Currency (text); Data Source (text); Department (text); Grade Level (select); Internal Max (currency); Internal Min (currency); Last Updated (date); Market Max (currency); Market Median (currency); Market Min (currency); Notes (long_text).

Grade Level starts with L1, L2, L3, L4, L5, M1, M2.

Requested CSV, SQL DDL, JSON Schema and Notion mappings derive from one field list. Display names remain identical across CSV and JSON, and normalized SQL identifiers are documented. Relation fields keep their relational meaning, dates remain dates and money remains currency. Machine-readable data is separated from explanation, with validation failures and limitations reported separately.

One supplied SQL example is:

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()
);

Excel-compatible CSV uses UTF-8 with a byte order mark. Because CSV has no column types, numeric, date and currency formatting is identified after the data. A CSV is not an .xlsx workbook; the latter is generated only when requested. Example rows require an explicit request and must be clearly synthetic. Sensitive identifiers are not unnecessarily repeated in a review, and generated templates remain empty.

Integrations

Notion output goes to notion-manual-import for the CSV, property mapping, import steps and verification checklist. Manual mappings require no connection. Requested workspace changes require verified tool access, the actual target and authorization; a claimed connection is insufficient evidence. Notion relation columns resolve only after both databases have been imported. Select options are an initial vocabulary that can be renamed to match the business. people-directory supplies the related employee master record; notification-reminder-hub is the separate due-date reminder module. Personal identifiers and sensitive financial details stay out of generated templates.

Source README

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.

Discussion

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