Raise Vs Jump

Raise vs Jump Skill

"Job hoppers earn more" is true on salary and incomplete on everything else: equity that vests on a cliff you keep resetting, the promotion you were two quarters from, the months of search time, the reputation cost of a short-stint résumé. This skill runs the salary math properly — trajectories, not single offers — and then insists on the checklist of what the salary math can't see, because that checklist decides more of these choices than the compounding does.

What This Skill Produces

• The trajectory table — year-by-year salary and cumulative earnings for both paths
• The crossover year — when cumulative jump-earnings pass cumulative stay-earnings
• The gap at horizon — final salary gap and cumulative gap, on stated assumptions
• The not-in-the-model checklist — scored for this user's actual situation

Required Inputs

Ask for these if not provided:

• Current salary and realistic stay-raise % — their employer's actual recent raises, not the poster in the break room (default 3%, labeled)
• Jump assumptions — bump per jump (default 15%), years between jumps (default 3), raises between jumps (default 2% — jumpers often land at the top of a band and stall)
• The invisible items — unvested equity and its schedule, pension/tenure benefits, promotion proximity, how they'd handle a search

Programmatic Helper

python3 scripts/raise_vs_jump.py --salary 120000
python3 scripts/raise_vs_jump.py --salary 120000 --stay-raise 3.5 --jump-bump 18 --jump-every 3 --json

Deterministic. Models salary only — the script prints its own not-modeled list, and the skill's job is to make that list concrete for the user.

Framework: What the Salary Math Hides

• Vesting resets are the jump tax — walking away from unvested equity and restarting a cliff is often worth more than the bump; compute it in dollars, not vibes
• The stay path has step functions too — a promotion is a 10–20% event; if one is genuinely close (named role, named timeline — not a vague "soon"), model it as a stay-side jump
• Raises between jumps sag — new hires land high in the band and then stall; that's the --jump-year-raise 2 default, and it's why the crossover is later than the first bump suggests
• Search risk is asymmetric — a jump that takes 4 months of search or ends in a bad fit erases years of edge; weight it by how in-demand the user actually is
• Three jumps is a pattern — recruiters read tenure; the strategy that maximizes 5-year earnings can shrink 15-year options

Output Format

Raise vs Jump: [scenario]

The Trajectories

[Script output: year-by-year table, crossover year, gaps at horizon]

What the Table Says

[Two sentences: the size of the pure-salary edge and how sensitive it is to the bump/raise assumptions.]

The Checklist the Table Can't See

The Honest Read

[One paragraph: what the numbers plus the checklist actually suggest — a recommendation with its reasoning, not a dodge.]

Educational model, not financial or career advice — the checklist items are prompts for the user's judgment, not scores from the model.

Quality Checks

• Trajectories and crossover shown — never just "the offer is 15% more"
• Unvested equity is computed in dollars if it exists
• A genuinely-close promotion is modeled, a vague one is named as vague
• The honest read takes a position with reasoning
• The disclaimer line appears in the artifact

Anti-Patterns

• Do not compare a single offer number to a single current salary — trajectories or nothing
• Do not ignore equity because it's "complicated" — the complication is the money
• Do not treat "my manager hinted at promotion" as a modeled event — named role and timeline, or it's noise
• Do not assume the jump cadence repeats forever without naming the résumé-pattern cost
• Do not hide behind "it depends" — deliver the honest read with its reasoning