Job Offer Comparator ⚖️

A $115k on-site offer can lose to a $95k remote one. Salary is the number companies make easiest to see; everything that actually decides your life — commute hours, health premiums, retirement match caps, equity that may never liquify, and the cost of living in the offer's city — hides in attachments and footnotes.

This skill makes offers comparable by computing, per offer: true total compensation (all cash + benefits − deductions, COL-adjusted), effective hourly rate on real hours (including commute), the $ value of PTO, and the break-even base salary — the exact number to say in a negotiation: "I'd need $X base to say yes."

Overview

Four commands in scripts/offer_compare.py:

1. compare — side-by-side table: raw comp lines → deductions → risk-adjusted → COL-adjusted true comp; then derived metrics (real weekly hours, effective hourly, PTO value) and a plain-language verdict, including the marginal hourly rate of the extra hours the money-rich offer demands. --json for agents.
2. breakeven — for exactly two offers: the base salary the loser needs to match the winner's true comp (holding its own bonus/match/deductions constant) — your negotiation target. Also shows the reverse: how far the winner could drop and still win.
3. annotate — field-by-field guide: typical values and where to find each number in an offer letter/benefits PDF.
4. example — a filled sample offers.json to copy.

When to Use

• "I have two offers — which is actually better?"
• "Is $115k in the office worth it vs $95k fully remote?"
• "The offer is in Austin — how does cost of living change the math?"
• "What salary should I counter with?" (→ breakeven gives the number)
• "Contractor day-rate vs salary — which wins?" (model the contract as an offer with no benefits, high gross)
• "They offer 0.5% equity — how do I account for it?" (risk-discount it)

Don't use for: tax advice (gross-of-tax model), pre-IPO equity valuation (use a 409A/secondary-price approach), or deciding between job families — it prices offers, it doesn't rank careers.

Quick Start

# 0. Get a filled sample and edit it
python3 scripts/offer_compare.py example > offers.json

# 1. Full comparison (remote $95k vs big-city $115k)
python3 scripts/offer_compare.py compare --file offers.json

# 2. Negotiation target: what base makes the loser match the winner?
python3 scripts/offer_compare.py breakeven --file offers.json

# 3. Inline, no file — two quick offers
python3 scripts/offer_compare.py compare \
  --offer '{"name":"Stay","base":90000,"commute_km_each_way":25,"commute_days_per_week":4}' \
  --offer '{"name":"Go","base":104000,"commute_km_each_way":0,"col_index":108}'

# 4. Where does each number come from?
python3 scripts/offer_compare.py annotate

# 5. Machine-readable
python3 scripts/offer_compare.py compare --file offers.json --json

How It Works

gross = base + base×bonus_pct + min(base×match_pct, match_cap)
      + equity_value×(1−equity_risk) + other_benefits×12 + relocation
risk_adj = gross − health_premium×12 − commute_cost
commute_cost = km×2×days×52×cost_per_km + parking×12
TRUE COMP = risk_adj ÷ (col_index/100)
real hours/wk = hours_per_week + overtime + 2×days×(km ÷ 28 km/h)
effective hourly = true comp ÷ (52 × real hours/wk)
PTO value = pto_days × (true comp ÷ 260)

Every assumption is printed above the table, never hidden: 28 km/h door-to-door commute speed, 52 working weeks / 260 working days, equity risk default 0.50 for illiquid private-company grants. The retirement match cap is honored (min(base×pct, cap)) — caps bind more often than people think once base passes ~$80–100k. Taxes are deliberately out of scope: they shift with filing status and jurisdiction; the model compares offers, not tax strategies.

Breakeven solves by bisection on base (handles the match-cap kink where a closed-form would break): find base B such that the losing offer's true comp at B equals the winner's true comp.

Offer Field Reference

Workflow

1. Collect both offers into JSON (start from example, use annotate for any unclear field).
2. Enter expected bonus (not "up to"), risk-discount equity honestly.
3. Run compare — read the verdict block first, then the table.
4. Run breakeven — get the counter-offer number for the losing side.
5. Negotiate with that number; re-run after every revised offer.
6. Sanity-check hours assumptions — the model is only as honest as the overtime you admit to.

Common Pitfalls

1. Comparing nominal salaries across cities. $115k at COL 115 is $100k-equivalent. Set col_index before reading the verdict.
2. Trusting "up to 20% bonus". Enter the expected (usually 50–70% of max, or the 3-year historical payout) — not the maximum.
3. Ignoring the match cap. 6% match capped at $4k on $115k pays $4k, not $6.9k. The table flags a * where the cap binds.
4. Forgetting commute TIME, not just cost. 10 h/week of commuting at a $50/h effective rate is $26k/yr of your life — often bigger than the salary gap itself.
5. Counting equity at face value. A 0.5% private grant is a lottery ticket; risk-discount it (default 0.5) or it will dominate every decision.
6. One-time money as recurring. Relocation/signing bonuses inflate year-one comparisons — the tool flags them; exclude them from long-run decisions.

Verification Checklist

• example > offers.json then compare --file runs and prints assumptions + table + verdict
• Verdict names a money winner AND an hours/life winner
• breakeven prints a target base, and its verification line ("at that base … == target ✓") holds
• Changing col_index from 100 → 115 visibly reduces true comp
• Setting retirement_match_cap below base×match% shows the * capped flag
• compare --json output parses and rows include true_comp and effective_hourly
• python3 scripts/test_offer_compare.py → ALL TESTS PASSED

One-Shot Recipes

Remote vs office, same city:

python3 scripts/offer_compare.py compare \
  --offer '{"name":"Remote","base":95000,"commute_km_each_way":0,"commute_days_per_week":0,"health_premium_monthly":150,"retirement_match_pct":0.04,"retirement_match_cap":10000,"equity_annual_value":12000}' \
  --offer '{"name":"Office","base":115000,"commute_km_each_way":30,"commute_days_per_week":5,"monthly_parking_or_transit":250,"health_premium_monthly":600,"bonus_pct":0.15}'

Same job, different cities (COL):

python3 scripts/offer_compare.py compare \
  --offer '{"name":"Denver","base":105000,"col_index":100}' \
  --offer '{"name":"SF","base":140000,"col_index":165,"commute_km_each_way":40,"commute_days_per_week":3}'

Contractor day-rate vs permanent salary: model the contract as gross = day-rate × billable days (e.g. $650×220 = $143k), zero match/equity/PTO, full health premium, and compare effective hourlies.

python3 scripts/offer_compare.py compare \
  --offer '{"name":"Contract","base":143000,"bonus_pct":0,"retirement_match_pct":0,"health_premium_monthly":650,"pto_days":0,"hours_per_week":45}' \
  --offer '{"name":"Staff","base":118000,"bonus_pct":0.08,"retirement_match_pct":0.05,"retirement_match_cap":9000,"health_premium_monthly":180,"pto_days":24}'

Decision support, not financial advice. Gross-of-tax model; verify offer terms against the actual documents before negotiating.