Career & communication · course 56 of 105

The Salary Negotiation Playbook

Get paid more on a job offer or a raise: your three numbers, when to name one, how to counter and trade on the whole package, then role-plays against a recruiter with a hidden budget.

5 modules

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Runs in Claude (claude.ai, the Claude apps or Claude Code) with your own Claude account. A course opens there as soon as your payment goes through; nothing is shipped.

Preview

What it teaches

How to get paid more on a job offer or a raise without wrecking the relationship: know your three numbers, don’t name the first number early, never give your pay history, counter with one number and a reason, then stop talking, trade on the whole package, stay honest, get it in writing. The AI asks 3 questions, places you, drills you on what recruiters actually say, does the money math with you, then plays the recruiter in full role-plays with a hidden budget you have to find, including one hard recruiter with an exploding offer and a fake “final”. Each module opens with a short card before the drills. Every reply is scored against a written checklist.

How long

2-3 sessions. Roughly 30-60 drill rounds depending on repeats, 2-5 role-plays, then a final test. Real offer or meeting within 14 days? Say so at the start: the tutor skips to the spots and role-play that match it and builds a one-page plan with your own numbers the same session. A one-line save code at the end of each session lets you carry on in a new chat.

From the answer key · M1, row S3

Setup: you make $64k. Target $78k. “And what are you making now?” Type exactly what you’d say.

Think first, then show the answer

Answer“I keep my current pay private, but I’m happy to talk about what this role is worth. I’m targeting $78,000 to $86,000.”

WhyA polite decline [3]; turn to what this role is worth or your expectations [3].

The wrong answer it catchesAny number for current/past pay, exact or rough (“in the 60s”) [3]; a made-up number [8]; hostile tone (“that’s illegal to ask” said as an attack; “I keep that private” is fine) [9]; expectations given as a range with bottom below $78k or a vague one (“high 70s”) [2].

The number in brackets is the Module 0 rule the tutor quotes when you miss that item.

Never share your current or past pay. Turn it to what this role is worth.
The model answer is one good reply, not the only one. Grade by the checklist, never by closeness to the model.

The modules

5 modules, drilled one item at a time. The ones marked optional are offered along the way.

M0
The principles
Short questions
M1
Spots (what recruiters and managers actually say)
“Type exactly what you’d say”
M2
Offer math (exact)
Offer math
M3
Role-plays
Full negotiation; the tutor plays the other side
M4
Equity and benefits math (exact)optional
Options, RSUs, vesting, ownership and dilution, preferences (toy), what the IRS counts as income, benefits in dollars

Before it teaches, it asks

1
What’s your situation: an offer in hand, interviewing, asking for a raise, or getting ready for later? If there’s a deadline or meeting, what date? Rough role and pay level? And what’s hardest for you: naming a number, staying quiet, feeling greedy, something else?
AI voice (ElevenLabs)
2
Setup: first screening call. No range in the job post. Your research: $70-80k. Target $78k. “What are your salary expectations?” Type exactly what you’d say.
3
Setup: offer call. Research $82-90k. Target $88k. Walk-away $82k. “We’re thrilled to offer you $80,000!” Type exactly what you’d say.

What’s exact, and what isn’t

Show

Straight from the course’s own notes.

Exact: The math in M2 and M4.
Scripted: Role-play outcomes. The recruiter’s hidden sheet decides what you get, not the AI’s mood. Three judgment calls remain (does your line use a listed reason, is it warm, does a late add-on need a fresh writing ask); M3 lists them.
Rules of thumb (labeled): Every negotiation rule in M0. They’re mainstream advice, not guarantees. Real recruiters vary.
Not in it yet: Law or tax advice, executive packages, unions and public-sector pay scales, non-US rules, real market pay data for your job (the AI can’t know it; M0 rule 1 says where to look).