comp-analysis
comp-analysis benchmarks compensation for specific roles against market data, analyzes internal salary band placement and equity grants, and identifies retention risks. Use it when setting pay for new hires, evaluating offer competitiveness, modeling equity refreshes, or uploading internal compensation data to find outliers and misalignment with market rates.
git clone --depth 1 https://github.com/openyak/openyak /tmp/comp-analysis && cp -r /tmp/comp-analysis/backend/app/data/plugins/human-resources/skills/comp-analysis ~/.claude/skills/comp-analysisSKILL.md
# /comp-analysis > If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md). 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. ```markdown ## 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 1. **Location matters** — Always specify location for benchmarking. SF vs. Austin vs. London are very different. 2. **Total comp, not just base** — Include equity, bonus, and benefits for a complete picture. 3. **Keep data confidential** — Comp data is sensitive. Results stay in your conversation.
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