Offer Comparison Skill

Offers are quoted as feelings — "the startup has more upside" — but they resolve to numbers with dates on them. This skill computes the curves: what each offer pays in each of the next four years, where the lines cross, and which lever in the weaker offer would actually move it.

What This Skill Produces

• The comp table — per-year and cumulative totals per offer, from the script
• The crossover analysis — which offer leads when, and what assumption that ranking is hostage to
• The risk translation — private equity restated honestly rather than at face value
• Negotiation levers — ranked by dollar impact per unit of asking-awkwardness

Required Inputs

Ask for these if not provided:

• Per offer: base, bonus %, equity grant value, vest years, cliff months, vest frequency, 401(k) match (% and cap), any promised refreshers
• The user's horizon — expecting to stay 2 years or 4 changes the answer, because cliffs do
• Equity risk view — public RSUs count at face; for private equity, agree a discount with the user (e.g. 50–75% haircut pre-Series B) and pass the discounted number to the script labeled as such

Programmatic Helper

python3 scripts/offer_comparison.py offers.json
cat offers.json | python3 scripts/offer_comparison.py - --json

Input shape in the script docstring. The script computes vesting month-by-month (a 12-month cliff releases the accrued year), bonuses and match annually, and reports the cumulative leader and crossover year. It values equity at exactly the number you give it — the risk adjustment is your input, visible, never a hidden assumption.

Framework: The Judgment Around the Math

• The cliff vs the horizon — an 18-month expected stay makes year-4 equity fiction; compare at the user's actual horizon, not the grant's
• A risky dollar ≠ a salary dollar — never compare private paper to cash 1:1; show the comparison at 2–3 discount levels if the user resists picking one