Offer Comparison

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
• Refreshers are policy, not promise — model them only if written down; otherwise mention them as upside outside the table
• Levers, ranked: base (compounds into bonus and match) → equity grant → signing bonus (one-time, easiest yes) → cliff/start-date adjustments

Output Format

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Offer Comparison: [A] vs [B]

The Curves

[Script output: per-year, cumulative, leader, crossover]

What the Ranking Is Hostage To

[The 1–2 assumptions that flip the answer — usually the private-equity discount and the stay-horizon — each shown with the flipped result.]

Negotiation Levers

Educational model, not financial advice — verify with a licensed professional before acting on it.

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Quality Checks

• Equity discount for private companies is explicit and the user agreed to it
• The comparison is shown at the user's stated horizon, not only at 4 years
• The hostage-assumptions section shows the flipped ranking, not just names the risk
• Levers carry dollar impacts computed from the actual offers
• The disclaimer line appears in the artifact

Anti-Patterns

• Do not compare a risky equity dollar to a salary dollar 1:1 — the discount is the analysis
• Do not hide the vesting cliff inside annual averages — year 1 with a cliff is its own story
• Do not model unwritten refreshers as income
• Do not declare a winner without naming what assumption the win depends on
• Do not present the model's output without its assumptions attached