Set a defensible comp band in one session

Seed-to-Series B founder or first people-ops hire at a 10-60 person startup that lacks an HR team with access to paid compensation databases · Assemble in ~10 min; complete benchmarking report in <30 min once installed.

HiringEdit flowon X● golden 85

Not legal or HR-compliance advice. Employment and hiring rules vary by jurisdiction — have qualified counsel review policies, offers, and screening before use.

step 1connector

Define the role, level, and location

⚡ bring your own tool

step 2connector

Pull public compensation data from free sources

⚡ bring your own tool

step 3ready

Synthesize salary bands and structure

gtm-compensation5 alts

step 4partial

Research equity norms by stage and sector

market-research12 alts

step 5ready

Build the offer decision framework

hiring10 alts

step 6ready

Generate the final benchmarking report

market-research-reports12 alts

Did this recipe work for you?

What you get

▸Salary benchmarking table with p25/p50/p75 bands and data-source attribution
▸Equity benchmark summary (grant %, vesting, cliff) segmented by startup stage
▸Offer decision framework with target band and walk-away threshold
▸Shareable benchmarking report (Markdown or structured doc) ready for board or counsel review

tools: Free tier: levels.fyi, Glassdoor, LinkedIn Salary, Carta public reports. Optional Playwright MCP or Firecrawl for automated scrape (usage-based).

tokens: ~$1-3 per role benchmarking run

≈ <$5/mo for occasional use (2-4 hires/mo)

What stays yours

· Manually pull or verify comp data from at least one primary source (levels.fyi or Glassdoor) — the report's credibility depends on data freshness you confirm
· Have an employment attorney or experienced HR advisor review the final offer bands before use, particularly for equity terms, vesting cliffs, and any jurisdiction-specific pay transparency requirements
· Apply judgment on which percentile to target given your specific runway, candidate leverage, and team equity norms — the framework gives the range, not the final number

✓ Last verified 2026-06-11claude-sonnet-4-6 (subagent, test-mode fixtures)

✓ step 1 · Define the role, level, and location

✓ step 2 · Pull public compensation data from free sources

✓ step 3 · Synthesize salary bands and structure

✓ step 4 · Research equity norms by stage and sector

✓ step 5 · Build the offer decision framework

✓ step 6 · Generate the final benchmarking report

view sample outputs from this run

step 3 · Synthesize salary bands and structure

# Salary Band — Senior Product Engineer (Full-Stack)
## SF Bay Area | Series A SaaS/AI | 2025–2026

*Synthesized via gtm-compensation persona from 6 public data sources.*

---

## Methodology

**Data cleaning approach**:
1. Excluded FAANG / public-company rows (structural outliers — total comp inflated by RSUs at market cap scale).
2. Applied Glassdoor large-company skew correction (−12% to base figures from that source).
3. Weighted sources by startup-specificity: Carta and Pave carry 35% weight each; Wellfound 20%; levels.fyi startup tier and Blind 5% each.
4. Outlier removal: dropped any in…

step 4 · Research equity norms by stage and sector

# Equity Benchmark — Grant, Vesting Structure, and Cliff Norms by Stage
## Role: Senior Product Engineer (Full-Stack) | B2B SaaS/AI | 2025–2026

*Synthesized via market-research persona from Carta State of Private Markets 2025, Pave Equity Reports, AngelList/Wellfound job data, and public Holloway/Stripe Atlas equity guides.*

---

## 1. Industry Context — Why Equity Structures Matter at Series A

At Series A, the company has de-risked existence but not scale. A senior engineer joining at this stage takes meaningful risk (product still finding PMF, team structure still forming) and should rece…

step 5 · Build the offer decision framework

# Offer Decision Framework
## Senior Product Engineer (Full-Stack) | Series A SaaS/AI | SF Bay Area

*Synthesized via hiring skill persona. Converts benchmark data into an actionable offer decision.*

---

## 1. Decision Variables

Before constructing an offer, assess the following four variables for this specific candidate:

| Variable | Definition | Options |
|---|---|---|
| **Candidate tier** | How rare is this candidate in the active pipeline? | Standard / Strong / Exceptional |
| **Competitive pressure** | Are you in a competitive process with another offer in hand? | None / Moderate / Hi…

The problem

When I need to set a salary band for a new hire, I want a benchmarking report comparing market data for that role, level, and location, so that I can make a competitive offer without paying $5,000+ for a Mercer survey or flying blind from Glassdoor.

Cybernetic layers

Memorybyo

No persistent comp database in the library yet. Reuse the raw data CSV from step 2 across multiple roles by saving it locally. Each new role runs a fresh session.

Backgroundnot applicable

Benchmarking is a batch job run on-demand before each hire — not a continuous background process. No scheduling needed.

Harnessready

Claude Code is the runtime. Install ready skills into ~/.claude/skills/ and invoke by name. Steps 2 is a manual data-pull step outside the harness.

Click any step to see what fills it — and the ranked alternatives you can swap in.