salary-benchmarking
Lets your agent compare salaries with the market. Helps set fair values for different roles in a company.
What it does
This tool helps you understand how much to pay for different roles in your company by comparing them to market rates and internal salaries. It can generate reports in formats like CSV, SQL, or JSON.
When to use it
- ✓When you want to know if your company's salaries are fair compared to the market.
- ✓When planning new salary ranges for positions.
- ✓To research market salaries for a specific role.
Example of what to ask your AI
“I want to compare Project Manager salaries in Brazil to the market.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
You provide the job roles, location, and current salary data. The tool then compares this with market information and internal salaries to show where the differences lie.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill salary-benchmarkingOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/salary-benchmarking/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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
…
FAQ
Does this tool calculate payroll or taxes?
No, it's only for salary research and comparison, not for payroll calculations, taxes, or legal advice.
Does it use real employee data?
No, the tool creates templates and empty reports. It does not store or process real employee or customer data.
Can I export the data to use in spreadsheets?
Yes, you can export the data in formats like CSV, which can be opened in spreadsheet programs like Excel.
Details
Original description by the author
Salary benchmark register: role, department and grade against market and internal minimum, median and maximum. Use for compensation review.
Popularity
- GitHub stars
- 48k
- Ranking position
- #4046
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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