compensation-and-leveling

compensation-and-leveling is a skill for Claude Code from cbrock84/headcount. It costs 75 tokens per session (841 once invoked), scanned A, original, MIT.

A framework for defining job levels, salary ranges, market comparisons, pay equity, and how raises and promotions are decided.

In plain words
What is it for?
Use it to create or revise career levels and salary bands, benchmark pay, and assess compensation requests or promotion decisions.
Why use it?
It replaces ad hoc salary negotiations with clearer, more consistent decisions. It also highlights employment-law and pay-transparency issues that may need HR or legal review.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the people plugin — 12 skills shipped together

Good fit Use it to create or revise career levels and salary bands, benchmark pay, and assess compensation requests or promotion decisions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/cbrock84/headcount/compensation-and-leveling
About the project

headcount is an organization of independently installable Claude Code plugins, each grouping skills for a department such as finance, security, or demand generation. Claude Code users install the departments they need and invoke their skills for specialized work; the catalogue entries are skills and related agent tooling from that organization.

cbrock84/headcount · 2,089 stars · on GitHub · cbrock84.github.io

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add cbrock84/headcount --skill compensation-and-leveling
Clone the repo
git clone --depth 1 https://github.com/cbrock84/headcount

Made for: Claude Code.

Or install people, the plugin that ships this one along with the rest of its 12 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for compensation-and-leveling

README.md
[![agentmods](https://agentmods.dev/badge/skills/cbrock84/headcount/compensation-and-leveling/github.svg)](https://agentmods.dev/skills/cbrock84/headcount/compensation-and-leveling)
Your own site
<a href="https://agentmods.dev/skills/cbrock84/headcount/compensation-and-leveling"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/compensation-and-leveling/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for compensation-and-leveling

Your own site · 80×15
<a href="https://agentmods.dev/skills/cbrock84/headcount/compensation-and-leveling"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/compensation-and-leveling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 75 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 841 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00075 $0.00841
Opus 5.5 $0.00030 $0.00336
Sonnet 5.5 $0.00015 $0.00168
Haiku 4.5 $0.00007 $0.00084

Measured 1mo ago against content hash 5f3691d529e9, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

compensation-and-leveling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/people/skills/compensation-and-leveling/SKILL.md · 82 lines

How it starts

The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Compensation and leveling

Compensation touches employment law, pay transparency requirements, and equal pay obligations that vary by jurisdiction. Structural work here is fine; specific decisions about individuals should be reviewed by qualified counsel or an HR professional.

Leveling first

Pay structure without a leveling framework produces negotiated salaries, and negotiated salaries produce inequity that correlates with who negotiates hardest.

Define each level by scope and impact, not tenure or task list:

  • What ambiguity can they handle — a defined task, a defined problem, an undefined problem, a problem nobody has identified?
  • What is the blast radius of their decisions — their work, their team, the function, the company?
  • What do they do for others: execute, contribute, guide, or set direction?

Levels must be distinguishable in a sentence. If two adjacent levels cannot be told apart by someone who does not know the people in them, they are one level.

Bands

For each level, benchmark against a market defined by the roles you actually compete with for candidates — not the whole industry, and not aspirational peers.

  • Set a target position (at market, above, or below) and state it as policy rather than deciding case by case.
  • Bands wide enough to allow growth within a level, narrow enough to mean something.
  • Re-benchmark on a schedule. Markets move, and bands that do not move create compression that eventually costs more to fix than to prevent.

Compression and equity

Compression — new hires paid near or above tenured staff — is the predictable result of moving markets and static internal pay. It is corrosive because it is discovered, and it is always discovered.

Run a pay equity analysis on a schedule: pay by level, controlling for level and location, disaggregated by demographic. Where a gap exists, fix it directly rather than waiting for the next cycle. Findings here need qualified review before action.

Decisions

Read the full file on GitHub · 82 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago Changed · +7 lines 5f3691d529e9
  2. 1mo ago First seen · 75 lines · 75 tokens per session scan A b39eeeeaf6b0

Subscribe to this mod's changes

compensation-and-leveling is a skill published in the GitHub repository cbrock84/headcount (2,089 stars, last pushed 8d ago), licensed MIT. It adds 75 tokens to every session and 841 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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