Human Resources

Skill 54 of 200

Comp Analysis

Analyze compensation — benchmarking, band placement, and equity modeling.

1 minute · 292 words · 9 sections

Install

$ npx skills add anthropics/knowledge-work-plugins --skill comp-analysis

$ npx skills add anthropics/knowledge-work-plugins

» /plugin marketplace add anthropics/knowledge-work-plugins

The first command installs just this skill, by the name in its SKILL.md; the second installs the whole repository.

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md (opens in a new tab).

Analyze compensation data for benchmarking, band placement, and planning. Helps benchmark compensation against market data for hiring, retention, and equity planning.

Usage

/comp-analysis $ARGUMENTS

What I Need From You

Option A: Single role analysis “What should we pay a Senior Software Engineer in SF?”

Option B: Upload comp data Upload a CSV or paste your comp bands. I’ll analyze placement, identify outliers, and compare to market.

Option C: Equity modeling “Model a refresh grant of 10K shares over 4 years at a $50 stock price.”

Compensation Framework

Components of Total Compensation

Base salary: Cash compensation
Equity: RSUs, stock options, or other equity
Bonus: Annual target bonus, signing bonus
Benefits: Health, retirement, perks (harder to quantify)

Key Variables

Role: Function and specialization
Level: IC levels, management levels
Location: Geographic pay adjustments
Company stage: Startup vs. growth vs. public
Industry: Tech vs. finance vs. healthcare

Data Sources

With ~~compensation data: Pull verified benchmarks
Without: Use web research, public salary data, and user-provided context
Always note data freshness and source limitations

Output

Provide percentile bands (25th, 50th, 75th, 90th) for base, equity, and total comp. Include location adjustments and company-stage context.

## Compensation Analysis: [Role/Scope]

### Market Benchmarks
| Percentile | Base | Equity | Total Comp |
|------------|------|--------|------------|
| 25th | $[X] | $[X] | $[X] |
| 50th | $[X] | $[X] | $[X] |
| 75th | $[X] | $[X] | $[X] |
| 90th | $[X] | $[X] | $[X] |

**Sources:** [Web research, compensation data tools, or user-provided data]

### Band Analysis (if data provided)
| Employee | Current Base | Band Min | Band Mid | Band Max | Position |
|----------|-------------|----------|----------|----------|----------|
| [Name] | $[X] | $[X] | $[X] | $[X] | [Below/At/Above] |

### Recommendations
- [Specific compensation recommendations]
- [Equity considerations]
- [Retention risks if applicable]

If Connectors Available

If ~~compensation data is connected:

Pull verified market benchmarks by role, level, and location
Compare your bands against real-time market data

If ~~HRIS is connected:

Pull current employee comp data for band analysis
Identify outliers and retention risks automatically

Tips

Location matters — Always specify location for benchmarking. SF vs. Austin vs. London are very different.
Total comp, not just base — Include equity, bonus, and benefits for a complete picture.
Keep data confidential — Comp data is sensitive. Results stay in your conversation.

About this skill

Trigger

Analyze compensation — benchmarking, band placement, and equity modeling. Trigger with "what should we pay a [role]", "is this offer competitive", "model this equity grant", or when uploading comp data to find outliers and retention risks.

The verbatim description from this skill’s front matter — the string an agent matches on to decide whether to load it.

Colophon for this issue

Colophon

How this issue was made

Source

github.com/anthropics/knowledge-work-plugins, branch main, last pushed 23 September 2026.

Licence

Apache-2.0 — the text of every skill is reproduced unmodified, frontmatter included, under the upstream licence.

Discovery

200 skills found by walking the repository tree for SKILL.md, not by matching a directory convention. 27 distinct layouts observed: bio-research/skills/*/SKILL.md, cowork-plugin-management/skills/*/SKILL.md, customer-support/skills/*/SKILL.md, data/skills/*/SKILL.md, design/skills/*/SKILL.md, engineering/skills/*/SKILL.md, enterprise-search/skills/*/SKILL.md, finance/skills/*/SKILL.md, human-resources/skills/*/SKILL.md, legal/skills/*/SKILL.md, marketing/skills/*/SKILL.md, operations/skills/*/SKILL.md, partner-built/apollo/skills/*/SKILL.md, partner-built/brand-voice/skills/*/SKILL.md, partner-built/common-room/skills/*/SKILL.md, partner-built/slack/skills/*/SKILL.md, partner-built/zoom-plugin/skills/*/SKILL.md, partner-built/zoom-plugin/skills/contact-center/*/SKILL.md, partner-built/zoom-plugin/skills/meeting-sdk/*/SKILL.md, partner-built/zoom-plugin/skills/meeting-sdk/web/*/SKILL.md, partner-built/zoom-plugin/skills/video-sdk/*/SKILL.md, partner-built/zoom-plugin/skills/virtual-agent/*/SKILL.md, partner-built/zoom-plugin/skills/zoom-mcp/*/SKILL.md, pdf-viewer/skills/*/SKILL.md, product-management/skills/*/SKILL.md, productivity/skills/*/SKILL.md, sales/skills/*/SKILL.md.

Issue colours

Resolved from the awesome-design-md registry — hue 39°, chroma 0.113. Two accent tones are generated per issue and each is proven against its own ground before it ships: a single accent that passes AA on both light and dark paper is arithmetically impossible.

Typefaces

Display: Copernicus is not available to us; set in Newsreader.

We never load a typeface at request time from a third-party origin, so a brand face we do not already self-host is substituted rather than fetched.

Heading repairs

1 repair applied to this skill so the document has one h1 and no skipped levels:

Removed “/comp-analysis”, a leading h1 that duplicated the skill title.

Spec compliance

53 editorial notes across 41 of 200 skills. They are printed in the margin of each skill rather than as errors here.

and 31 more.

Images

Images inside a skill come from the upstream repository. Where the author gave no alternative text we mark the image decorative rather than inventing a description — a plausible caption we made up is worse than none for the reader who depends on it.

Marketplace

A plugin manifest is published at .claude-plugin/marketplace.json by Anthropic, declaring 120 plugins. It is read for editorial metadata only — never as the skill index, which is always the repository tree.

Signal

Install counts come from skills.sh. They measure downloads, not quality, and an unranked repository is not an unread one.

Agent surfaces

The whole issue is available as one markdown document at /anthropics/knowledge-work-plugins.md, and each skill at its own .md URL.

Publication

Set by Skills Docs from the source repository. Body text is Literata at the reader’s chosen size and measure; code is Geist Mono. Nothing on this page was written by us except this paragraph.

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md (opens in a new tab).

Analyze compensation data for benchmarking, band placement, and planning. Helps benchmark compensation against market data for hiring, retention, and equity planning.

Usage

/comp-analysis $ARGUMENTS

What I Need From You

Option A: Single role analysis “What should we pay a Senior Software Engineer in SF?”

Option B: Upload comp data Upload a CSV or paste your comp bands. I’ll analyze placement, identify outliers, and compare to market.

Option C: Equity modeling “Model a refresh grant of 10K shares over 4 years at a $50 stock price.”

Compensation Framework

Components of Total Compensation

Base salary: Cash compensation
Equity: RSUs, stock options, or other equity
Bonus: Annual target bonus, signing bonus
Benefits: Health, retirement, perks (harder to quantify)

Key Variables

Role: Function and specialization
Level: IC levels, management levels
Location: Geographic pay adjustments
Company stage: Startup vs. growth vs. public
Industry: Tech vs. finance vs. healthcare

Data Sources

With ~~compensation data: Pull verified benchmarks
Without: Use web research, public salary data, and user-provided context
Always note data freshness and source limitations

Output

Provide percentile bands (25th, 50th, 75th, 90th) for base, equity, and total comp. Include location adjustments and company-stage context.

## Compensation Analysis: [Role/Scope]

### Market Benchmarks
| Percentile | Base | Equity | Total Comp |
|------------|------|--------|------------|
| 25th | $[X] | $[X] | $[X] |
| 50th | $[X] | $[X] | $[X] |
| 75th | $[X] | $[X] | $[X] |
| 90th | $[X] | $[X] | $[X] |

**Sources:** [Web research, compensation data tools, or user-provided data]

### Band Analysis (if data provided)
| Employee | Current Base | Band Min | Band Mid | Band Max | Position |
|----------|-------------|----------|----------|----------|----------|
| [Name] | $[X] | $[X] | $[X] | $[X] | [Below/At/Above] |

### Recommendations
- [Specific compensation recommendations]
- [Equity considerations]
- [Retention risks if applicable]

If Connectors Available

If ~~compensation data is connected:

Pull verified market benchmarks by role, level, and location
Compare your bands against real-time market data

If ~~HRIS is connected:

Pull current employee comp data for band analysis
Identify outliers and retention risks automatically

Tips

Location matters — Always specify location for benchmarking. SF vs. Austin vs. London are very different.
Total comp, not just base — Include equity, bonus, and benefits for a complete picture.
Keep data confidential — Comp data is sensitive. Results stay in your conversation.