comp-analysis

Name: comp-analysis
Author: anthropics

23kstars2.7kforks

How to install comp-analysis?

anthropics/knowledge-work-plugins/comp-analysis

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

Installs into the current project.

Summary

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.

Files · 1

View on GitHub

SKILL.md

1	# /comp-analysis
2
3	> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md).
4
5	Analyze compensation data for benchmarking, band placement, and planning. Helps benchmark compensation against market data for hiring, retention, and equity planning.
6
7	## Usage
8
9	```
10	/comp-analysis $ARGUMENTS
11	```
12
13	## What I Need From You
14
15	Option A: Single role analysis
16	"What should we pay a Senior Software Engineer in SF?"
17
18	Option B: Upload comp data
19	Upload a CSV or paste your comp bands. I'll analyze placement, identify outliers, and compare to market.
20
21	Option C: Equity modeling
22	"Model a refresh grant of 10K shares over 4 years at a $50 stock price."
23
24	## Compensation Framework
25
26	### Components of Total Compensation
27	- Base salary: Cash compensation
28	- Equity: RSUs, stock options, or other equity
29	- Bonus: Annual target bonus, signing bonus
30	- Benefits: Health, retirement, perks (harder to quantify)
31
32	### Key Variables
33	- Role: Function and specialization
34	- Level: IC levels, management levels
35	- Location: Geographic pay adjustments
36	- Company stage: Startup vs. growth vs. public
37	- Industry: Tech vs. finance vs. healthcare
38
39	### Data Sources
40	- With ~~compensation data: Pull verified benchmarks
41	- Without: Use web research, public salary data, and user-provided context
42	- Always note data freshness and source limitations
43
44	## Output
45
46	Provide percentile bands (25th, 50th, 75th, 90th) for base, equity, and total comp. Include location adjustments and company-stage context.
47
48	```markdown
49	## Compensation Analysis: [Role/Scope]
50
51	### Market Benchmarks
52	\| Percentile \| Base \| Equity \| Total Comp \|
53	\|------------\|------\|--------\|------------\|
54	\| 25th \| $[X] \| $[X] \| $[X] \|
55	\| 50th \| $[X] \| $[X] \| $[X] \|
56	\| 75th \| $[X] \| $[X] \| $[X] \|
57	\| 90th \| $[X] \| $[X] \| $[X] \|
58
59	Sources: [Web research, compensation data tools, or user-provided data]
60
61	### Band Analysis (if data provided)
62	\| Employee \| Current Base \| Band Min \| Band Mid \| Band Max \| Position \|
63	\|----------\|-------------\|----------\|----------\|----------\|----------\|
64	\| [Name] \| $[X] \| $[X] \| $[X] \| $[X] \| [Below/At/Above] \|
65
66	### Recommendations
67	- [Specific compensation recommendations]
68	- [Equity considerations]
69	- [Retention risks if applicable]
70	```
71
72	## If Connectors Available
73
74	If ~~compensation data is connected:
75	- Pull verified market benchmarks by role, level, and location
76	- Compare your bands against real-time market data
77
78	If ~~HRIS is connected:
79	- Pull current employee comp data for band analysis
80	- Identify outliers and retention risks automatically
81
82	## Tips
83
84	1. Location matters — Always specify location for benchmarking. SF vs. Austin vs. London are very different.
85	2. Total comp, not just base — Include equity, bonus, and benefits for a complete picture.
86	3. Keep data confidential — Comp data is sensitive. Results stay in your conversation.

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comp-analysis

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View on GitHub

How to install comp-analysis?

anthropics/knowledge-work-plugins/comp-analysis

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

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# /comp-analysis
2
3	> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md).
4
5	Analyze compensation data for benchmarking, band placement, and planning. Helps benchmark compensation against market data for hiring, retention, and equity planning.
6
7	## Usage
8
9	```
10	/comp-analysis $ARGUMENTS
11	```
12
13	## What I Need From You
14
15	Option A: Single role analysis
16	"What should we pay a Senior Software Engineer in SF?"
17
18	Option B: Upload comp data
19	Upload a CSV or paste your comp bands. I'll analyze placement, identify outliers, and compare to market.
20
21	Option C: Equity modeling
22	"Model a refresh grant of 10K shares over 4 years at a $50 stock price."
23
24	## Compensation Framework
25
26	### Components of Total Compensation
27	- Base salary: Cash compensation
28	- Equity: RSUs, stock options, or other equity
29	- Bonus: Annual target bonus, signing bonus
30	- Benefits: Health, retirement, perks (harder to quantify)
31
32	### Key Variables
33	- Role: Function and specialization
34	- Level: IC levels, management levels
35	- Location: Geographic pay adjustments
36	- Company stage: Startup vs. growth vs. public
37	- Industry: Tech vs. finance vs. healthcare
38
39	### Data Sources
40	- With ~~compensation data: Pull verified benchmarks
41	- Without: Use web research, public salary data, and user-provided context
42	- Always note data freshness and source limitations
43
44	## Output
45
46	Provide percentile bands (25th, 50th, 75th, 90th) for base, equity, and total comp. Include location adjustments and company-stage context.
47
48	```markdown
49	## Compensation Analysis: [Role/Scope]
50
51	### Market Benchmarks
52	\| Percentile \| Base \| Equity \| Total Comp \|
53	\|------------\|------\|--------\|------------\|
54	\| 25th \| $[X] \| $[X] \| $[X] \|
55	\| 50th \| $[X] \| $[X] \| $[X] \|
56	\| 75th \| $[X] \| $[X] \| $[X] \|
57	\| 90th \| $[X] \| $[X] \| $[X] \|
58
59	Sources: [Web research, compensation data tools, or user-provided data]
60
61	### Band Analysis (if data provided)
62	\| Employee \| Current Base \| Band Min \| Band Mid \| Band Max \| Position \|
63	\|----------\|-------------\|----------\|----------\|----------\|----------\|
64	\| [Name] \| $[X] \| $[X] \| $[X] \| $[X] \| [Below/At/Above] \|
65
66	### Recommendations
67	- [Specific compensation recommendations]
68	- [Equity considerations]
69	- [Retention risks if applicable]
70	```
71
72	## If Connectors Available
73
74	If ~~compensation data is connected:
75	- Pull verified market benchmarks by role, level, and location
76	- Compare your bands against real-time market data
77
78	If ~~HRIS is connected:
79	- Pull current employee comp data for band analysis
80	- Identify outliers and retention risks automatically
81
82	## Tips
83
84	1. Location matters — Always specify location for benchmarking. SF vs. Austin vs. London are very different.
85	2. Total comp, not just base — Include equity, bonus, and benefits for a complete picture.
86	3. Keep data confidential — Comp data is sensitive. Results stay in your conversation.

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