Offer prep brief with Claude

A Claude Skill that takes a candidate’s full interview history plus the role’s compensation parameters and produces an offer-prep brief — recommended offer composition, anticipated negotiation points, candidate-specific closing strategy, and competing-offer-handling considerations. Replaces the typical “we’ll figure out the offer when we get there” approach with a 15-minute structured prep that materially improves offer acceptance rate.

What you’ll need

• Claude Code or Claude.ai with custom Skills enabled
• Candidate’s interview record from Ashby, Greenhouse, or Lever — scorecards, debrief notes, candidate’s stated motivations
• Role compensation parameters — salary range, equity range, bonus structure, level mapping
• Optional: market-data benchmarks (Levels.fyi, Pave, Carta market data)

Setup

1. Drop the Skill. Place offer-prep.skill into your Claude Code skills directory. The Skill exposes one callable function: prep_offer.
2. Configure compensation parameters. Edit comp_framework.yaml with: levels, salary bands per level, equity bands per level, bonus structure, geographic adjustments.
3. Test on closed candidates. Run on candidates whose offer prep already happened; compare the Skill’s recommendations to what the team actually did. Tune the framework.

How it works

The Skill takes the candidate context and:

1. Reviews the candidate’s stated motivations. From recruiter screen and HM screen notes — what the candidate said matters to them about the role, compensation, location, growth.
2. Identifies competing-offer signals. From candidate interactions — mentioned other processes, mentioned competing offers, time pressure on decision.
3. Maps to comp framework. Recommends offer composition (base, bonus, equity, signing) within the role’s bands, calibrated to the candidate’s seniority signal from interviews.
4. Drafts negotiation anticipation. What the candidate is likely to push back on; recommended responses; walk-away thresholds.
5. Drafts closing strategy. Specific to this candidate — what to emphasize, what to address proactively, what timing pressure to apply (or relieve).

Output

A complete offer-prep brief with:

# Offer Prep: [Candidate] — [Role]

## Recommended offer
- Base: $X
- Bonus: Y% target
- Equity: Z RSUs / options vesting over 4 years
- Signing: $W (one-time)
- Start date: [target date]
- Total Year 1 cash: $X+Y
- Year 1 + equity: $X+Y+(Z/4)

## Why this composition
[Reasoning — interview signal, level mapping, market context]

## Candidate's stated motivations
- [What they said matters to them, with source notes]

## Anticipated negotiation
- Likely push: [specific to candidate]
  - Recommended response: [specific]
  - Walk-away: [threshold]

## Competing offer signals
- [What we know about other processes the candidate is in]

## Closing strategy
- Lead with: [what to emphasize]
- Address proactively: [concerns from interviews]
- Decision timeline: [recommended approach]
- Hiring manager involvement: [what role they should play]

## Open questions for the team
- [Anything the prep can't resolve and needs the team to decide]

Where it fits

Use this Skill before extending every offer above a certain seniority threshold (typically all senior+ roles, all hires above a comp band). The recruiter and hiring manager both review the brief, then the recruiter extends the offer with the strategy in mind.

The compounding benefit: well-prepped offers convert at materially higher rates than ad-hoc offers. Mature programs report 10-20 percentage point improvement in offer acceptance rate at senior levels.

Watch-outs

• Comp framework quality determines recommendation quality. A vague comp framework produces vague recommendations. Invest in the framework before deploying the Skill.
• Don’t auto-send offers. AI-prepped offer terms still require leadership approval. The Skill produces the recommendation; humans approve and extend.
• Sample-validate the recommendations. Periodically compare Skill recommendations to what the team actually offers; identify drift in either direction.
• Don’t surface protected-class signals. Negotiation strategy should not consider candidate demographics; verify the prompt explicitly excludes protected-class proxies from its reasoning.
• Pay-transparency compliance. Some jurisdictions require posted pay ranges; verify the offer recommendation is within the posted range to avoid legal exposure.