A Claude Skill that takes a role’s level, geography, and a comp-survey export (Radford, Pave, Carta), and produces a structured pay-band recommendation per component (base, equity, bonus / OTE) with named percentile, source-survey citation, and the calibration notes the recruiter brings to the offer call. Replaces the recruiter’s open-tab-spreadsheet juggle with a single document the hiring manager and finance approver can sign off on. Posts the public-facing range (NYC LL 32-A, CO/CA/WA pay-transparency compliant) as a separate output.
When to use
You’re posting a new role and need a public range that is defensibly sourced (not the vague industry standard framing, not 75th percentile without naming the survey or the geography).
You’re preparing an offer and need the band the hiring manager can approve without a half-day finance back-and-forth.
You’re auditing existing comp bands quarterly and want a structured comparison of “what we pay” vs. “what the survey says” per role family.
When NOT to use
Unilateral comp decisions outside an approved approval chain. The skill produces a recommendation. The compensation philosophy and approval matrix are owned by People Ops / Finance / Comp Committee. The skill informs them; it does not replace them.
Equity comp at pre-Series-B startups. Equity benchmarking at very-early stages is more about the firm’s specific cap table and dilution path than about market data. The survey numbers don’t carry there.
Negotiation script generation. The skill outputs a band; it does not author negotiation language. Auto-generated comp-negotiation language reads as cold and damages candidate experience.
Candidate-specific exception decisions. “Can we offer 15% above the band for this candidate?” is a question for the hiring manager and finance, not for the skill. The skill informs by surfacing the band; it does not approve exceptions.
Geographies where the survey has thin data. Surveys cover the US, EU, and major APAC markets well; emerging-market data (LatAm, Africa, smaller APAC) is thinner. The skill flags low-N geographies in the output.
Setup
Drop the bundle. Place apps/web/public/artifacts/compensation-benchmark-skill/SKILL.md into your Claude Code skills directory.
Configure the survey source. The skill reads exports from Radford, Pave, Carta, or a custom CSV. Per-source schema lives in references/1-survey-source-schemas.md. The skill does not call survey APIs directly — exports go through your comp-analyst’s approved access path.
Set the firm’s compensation philosophy. What percentile does the firm pay at (50th, 60th, 75th)? Does base+equity sum to a target percentile, or is each calibrated separately? The philosophy lives in references/2-comp-philosophy-template.md and is the input the skill calibrates against.
Configure approval-chain output. The skill emits the public-facing range as a separate output (NYC LL 32-A, CO/CA/WA pay-transparency compliant). Wire that output to your job-posting publication step (Greenhouse / Ashby job description), or copy by hand, depending on your team’s process.
Dry-run on a closed offer. Benchmark a role you closed last quarter. Compare the skill’s band to what the offer actually was. If the divergence is large, either the survey export is off-cycle or the firm’s philosophy file doesn’t match how offers are actually being approved.
What the skill actually does
Five steps. The order keeps the deterministic survey lookups before the LLM-driven calibration, because letting the model paraphrase survey numbers introduces drift the recruiter can’t audit.
Validate the role definition. Check that the role’s level, geography, and function are present and match values in the survey export. Halt on missing or ambiguous fields (“Senior Engineer” without a level on the firm’s ladder is ambiguous).
Look up survey percentiles. Deterministic lookup, not LLM. For each of base, equity (annualized), and bonus / OTE, pull the 25th / 50th / 60th / 75th / 90th percentiles from the survey export for the matched (level, geography, function) cell. If the cell has fewer than the survey’s documented sample-size threshold (varies by survey: Radford typically 5+, Pave typically 10+), flag low-N and refuse to recommend a percentile-based band — fall back to broader (level, function) without geography or to expanded geography (e.g. “US-wide” instead of “Bay Area”).
Calibrate against firm philosophy. Read the firm’s comp philosophy. Apply the target percentile to the survey numbers. The output is a structured band per component:
Base: target_pct of survey, with a ±10% range to absorb candidate-level variation.
Equity: same; convert to dollar-value at the firm’s strike price for new grants, document the math.
Bonus / OTE: target_pct on the OTE; split base/variable per the firm’s ratio for the function.
Compose the public-facing range. Per NYC LL 32-A and CO/CA/WA pay-transparency requirements, the public posting needs a base salary range. Default: “min of the band’s lower-edge to max of the band’s upper-edge, expressed as a single salary range.” If the role straddles US states with different transparency-law thresholds, the broadest range applies. The skill emits this as a separate output for direct use in the JD.
Emit the recommendation report + audit record. The report has: per-component bands with cited percentile and source survey, calibration notes, low-N or thin-data warnings, and the public-facing range. The audit record is one JSONL line: role, geography, level, percentile-targeted, survey source, survey export date, recommended band — for the firm’s pay-equity audit later in the year.
Cost reality
Per role benchmarked, on Claude Sonnet 4.6:
LLM tokens — typically 5-8k input (role definition + survey export rows + philosophy + skill instructions) and 1-2k output (structured report). Roughly $0.04-0.08 per role. Negligible.
Survey access cost — the survey subscriptions themselves are the binding cost (Radford, Pave, Carta range from $15K-$80K+ annual depending on coverage). The skill assumes the comp analyst already has access; it does not change that math.
Recruiter / comp-analyst time — the win. Hand-composing a comp recommendation is 30-90 minutes per role (survey lookup + spreadsheet juggle + philosophy application + writing the calibration note). The skill is 5-10 minutes including the dry-run sanity check.
Setup time — 30 minutes once for the philosophy file and survey-export integration. The philosophy file is rarely revised; survey exports refresh quarterly.
Success metric
Track three numbers, quarterly:
Offer acceptance rate within 3 weeks — calibrated comp drives acceptance. Below 60% in your geography and you’re under-paying; above 90% you may be over-paying. Both directions matter; the right number depends on the firm’s compensation philosophy (high-equity startups accept lower base; high-base mid-stage firms accept higher base).
Comp-band edit rate post-skill — share of the skill’s recommended bands that the hiring manager or finance edits before approval. Should sit at 10-25%. Above 40% means the philosophy file doesn’t reflect actual approval behavior; below 5% means the panel is rubber-stamping (the failure mode the skill is designed against).
Pay-equity audit drift — at the annual pay-equity review, do the skill’s recommendations correlate with where actual offers landed? If the audit surfaces equity gaps the skill’s recommendations would have closed, the skill is doing its job; if the audit surfaces gaps the skill’s recommendations would have widened, the philosophy file or the calibration is biased.
vs alternatives
vs Pave / Carta / Radford / Mercer reports directly. The reports are the source data; the skill composes them into a per-role recommendation. Pick the reports alone if your comp analyst lives in them and the recruiter only consumes “tell me the 75th.” Pick the skill if the recruiter needs the calibration note + public range + audit record without the analyst in the loop for every role.
vs ChatGPT-style “what should I pay a senior engineer in NYC.” Generic chat returns paraphrased survey data with no audit trail and no version-pinned source — that’s not defensible at pay-equity audit time. The skill cites the survey export by name and date.
vs spreadsheet templates. Templates are fine until the firm’s philosophy changes or the survey export refreshes; then every saved template silently goes stale. The skill reads from current sources every run.
vs no benchmarking. The default at many smaller firms. Predictable failure mode: pay-equity gaps surface at the annual audit, and the recruiter gets blamed for individual offers that were inside the firm’s normal practice. Defensible benchmarking is the cheapest intervention against this.
Watch-outs
Survey-export staleness.Guard: the skill reads the export’s dated metadata and warns if the export is older than 6 months. Survey data shifts faster than annual; quarterly refresh is the floor.
Geography mis-mapping.Guard: the skill matches the role’s geography against the survey’s geography taxonomy explicitly (Pave’s “SF Bay Area” is not the same cell as Radford’s “San Francisco MSA”). If the match is ambiguous, the skill halts and asks the recruiter to disambiguate rather than picking a default.
Low-N cell.Guard: the skill refuses to recommend a percentile-based band when the survey cell has fewer respondents than the survey’s documented threshold. It falls back to a broader cell (broader function, broader geography) and notes the fallback.
Equity-comparison drift.Guard: equity values are annualized and converted at the firm’s current strike price. The conversion math is documented in the report. The audit record stores the raw and converted values so future audits can re-derive.
Public-facing range too tight.Guard: if the public range is so tight that it functions as a single number, the skill warns. Posting “$140K-$145K” is a violation of the spirit (and arguably the letter) of NYC LL 32-A, which requires a “good faith” range. The skill enforces a minimum band width per geography.
Bias propagation through historical comp.Guard: if the firm’s philosophy file is calibrated by “match what we’ve paid in this band before,” the skill propagates whatever pay gaps exist in historical data. The skill flags this when philosophy matching closely tracks historical pay rather than survey percentiles, and recommends the comp analyst run a separate pay-equity check.
Stack
The skill bundle lives at apps/web/public/artifacts/compensation-benchmark-skill/ and contains:
---
name: compensation-benchmark
description: Take a role's level/geography/function plus a comp-survey export (Radford, Pave, Carta, or custom CSV), and produce a structured pay-band recommendation per component (base, equity, OTE) with cited percentiles, calibration against the firm's philosophy, and a public-facing range compliant with NYC LL 32-A and CO/CA/WA pay-transparency requirements. Never approves an offer; never auto-publishes.
---
# Compensation benchmark
## When to invoke
Use this skill when a recruiter or comp analyst needs a per-role pay band based on a survey export and the firm's comp philosophy. Take a role definition, a survey export, and the philosophy file as input and return a structured benchmark report plus a public-facing range.
Do NOT invoke this skill for:
- **Unilateral comp decisions outside the firm's approval matrix.** The skill recommends; People Ops / Finance / Comp Committee approve.
- **Equity at pre-Series-B startups.** Survey data is too thin and firm-cap-table-specific at that stage.
- **Negotiation-script generation.** Different workflow.
- **Approving exception bands** ("can we go 15% above?"). The skill informs; the hiring manager and finance approve.
## Inputs
- Required: `role_definition` — JSON with `level` (firm's ladder, e.g. `L5`), `geography` (e.g. `San Francisco MSA`), `function` (e.g. `software-engineering`).
- Required: `survey_export` — path to a survey export. Schema must match one in `references/1-survey-source-schemas.md`.
- Required: `philosophy` — path to the firm's compensation philosophy file. See `references/2-comp-philosophy-template.md`.
- Optional: `candidate_signal` — free-text note about the candidate (current comp, competing offers, etc.). Used in the calibration note, NOT to skew the recommended band.
## Reference files
- `references/1-survey-source-schemas.md` — per-source schemas with field mapping.
- `references/2-comp-philosophy-template.md` — fillable philosophy file.
## Method
Five steps.
### 1. Validate the role definition
Confirm `level`, `geography`, `function` are present and match values in the survey export. If `level` is on the firm's ladder but the survey uses a different ladder, look up the mapping in the philosophy file. If no mapping exists, halt and ask the user to add it.
If `geography` is ambiguous (e.g. "Bay Area" — does that include South Bay, East Bay, North Bay, the entire MSA?), halt and ask the user to specify against the survey's geography taxonomy.
### 2. Look up survey percentiles
Deterministic lookup — do NOT paraphrase the survey. For each of `base_salary`, `equity_annualized`, `ote` (or `bonus` if non-sales), pull the 25th / 50th / 60th / 75th / 90th percentiles for the matched (level, geography, function) cell.
Check the cell's sample size. If it's below the survey's documented threshold (Radford 5+, Pave 10+, Carta 15+ for equity, custom CSV per the schema's `min_sample_size` field), flag low-N. Fall back to a broader cell:
- First fallback: same level, same function, broader geography (e.g. US-wide).
- Second fallback: same level, same function, all geographies.
Document the fallback chain in the report. Do NOT silently fall back without surfacing.
### 3. Calibrate against firm philosophy
Read the philosophy file. The philosophy specifies the target percentile per component (e.g. base at 60th percentile, equity at 75th percentile, OTE at 50th percentile for sales).
For each component, compute:
- Recommended midpoint = survey's `target_percentile` for the cell
- Band width = midpoint × ±10% (default; configurable per component in the philosophy)
- Lower edge = midpoint × 0.9, upper edge = midpoint × 1.1
If the philosophy specifies a different band-width policy (e.g. wider band for senior roles where individual variance is larger), use that instead.
For equity: convert annualized survey value to dollar grant size at the firm's current strike price. Document the math in the report (`grant_value = annualized_value × vesting_period / strike_price`).
### 4. Compose the public-facing range
Compute the public-facing base salary range:
- Lower edge of public range = lower edge of base band
- Upper edge of public range = upper edge of base band
- Format: e.g. `$170,000-$210,000 USD per year`
Validate band width against the geography's pay-transparency requirements:
- NYC (LL 32-A): "good faith" range required; band narrower than ~15% width raises legal exposure.
- CO (Equal Pay for Equal Work Act): range required, no specific width threshold but functional good-faith requirement.
- CA (SB 1162): range required for postings if the role is to be performed in CA.
- WA (Pay Transparency Act): range required.
If the role straddles multiple jurisdictions, the broadest range applies. If the range is below 15% width, emit a warning (the band is at the edge of "good faith" — consider widening before publishing).
### 5. Emit the report + audit record
Write the report to stdout (or the calling environment's report destination). Append one JSONL line to `audit/<YYYY-MM>.jsonl` with: `role`, `geography`, `level`, `function`, `survey_source`, `survey_export_date`, `philosophy_version`, `target_percentiles`, `recommended_bands`, `public_range`, `low_n_flag`, `fallback_chain` (if any).
The audit record supports the firm's annual pay-equity audit. No PII; this is about the band, not about a specific candidate.
## Output format
```markdown
# Comp benchmark — {role} — {level} — {geography}
Generated: {ISO timestamp} · Skill v1.0 · Model: claude-sonnet-4-6
Survey: {Radford 2026-Q2 / Pave 2026-04 / etc.} · Philosophy: {firm-philosophy.json v3}
{LOW-N WARNING if any component fell back}
## Recommended bands
### Base salary (target: 60th percentile)
- Survey 60th percentile: $185,000
- Recommended band: $166,500 - $203,500
- Calibration note: Tight band (±10%); widen to ±15% for cross-level candidates.
### Equity (target: 75th percentile, 4-year vest)
- Survey 75th percentile annualized value: $90,000
- Total grant value: $360,000 over 4 years
- At firm strike $5.20: 69,231 shares
- Recommended band: 62,300 - 76,200 shares (±10%)
### Cash bonus (target: 50th percentile)
- Survey 50th percentile: $20,000 (annual target)
- Recommended band: $18,000 - $22,000
## Public-facing range (NYC LL 32-A / CO/CA/WA compliant)
`$166,500 - $203,500 USD per year, plus equity grant and target bonus`
Band width: 22% — within "good faith" thresholds.
## Provenance
- Survey: Radford Q2-2026 (export dated 2026-04-15)
- Survey cell sample size: 42 (above Radford's 5+ threshold)
- Philosophy: firm-philosophy.json v3 (updated 2026-01-10)
- Geography mapping: San Francisco MSA matched directly in Radford taxonomy
- Audit record: `audit/2026-05.jsonl` line {N}
## Calibration notes
- The candidate signal noted "competing offer at top of band from peer-tier company" — this is informational; the recommended band did NOT shift in response. If an exception is needed, escalate to the comp committee with the competing offer details.
- This role's geography has a pay-equity gap of -3.2% vs. firm-wide for the same level (per last quarterly audit); recommended band is at the firm's stated philosophy. Audit will surface whether the gap closes.
```
## Watch-outs
- **Survey-export staleness.** *Guard:* warns at >6 months on the export's dated metadata.
- **Geography mis-mapping.** *Guard:* halts on ambiguous geography rather than defaulting.
- **Low-N cell.** *Guard:* refuses to use a low-N cell; falls back with the chain documented.
- **Equity drift.** *Guard:* conversion math documented in the report; raw and converted values both stored in audit.
- **Public range too tight.** *Guard:* warns at <15% band width per pay-transparency-law functional thresholds.
- **Historical-pay bias propagation.** *Guard:* if philosophy is calibrated against historical pay rather than survey percentile, flag and recommend a separate pay-equity check.
# Survey source schemas
The compensation-benchmark skill reads survey exports in one of three supported formats: Radford, Pave, Carta. A custom CSV schema is also supported for in-house surveys or for sources not on this list.
## Radford
Radford ships exports as CSV or XLSX. The skill reads the CSV form (re-export from XLSX if needed).
### Required columns
| Column | Type | Notes |
|---|---|---|
| `level_radford` | string | Radford ladder code (e.g. `P4`). The philosophy file maps this to firm levels. |
| `function_radford` | string | Radford function code (e.g. `Software Engineering`). |
| `geography_radford` | string | Radford geography (e.g. `San Francisco MSA`). |
| `sample_size` | integer | Number of survey respondents in this cell. Skill requires ≥5. |
| `base_salary_p25` | number | 25th percentile base salary, USD. |
| `base_salary_p50` | number | 50th percentile. |
| `base_salary_p60` | number | 60th percentile. |
| `base_salary_p75` | number | 75th percentile. |
| `base_salary_p90` | number | 90th percentile. |
| `equity_annual_p25` | number | 25th percentile annualized equity value, USD. |
| `equity_annual_p50` | number | ... |
| `equity_annual_p60` | number | ... |
| `equity_annual_p75` | number | ... |
| `equity_annual_p90` | number | ... |
| `bonus_target_p50` | number | Target annual cash bonus, USD. (Radford reports target, not actual.) |
### Notes
- Radford's `level` codes (P1-P8 for IC, M1-M5 for management) need a firm-level mapping in the philosophy file. The mapping lives once, used everywhere.
- Geography taxonomy: Radford uses MSAs (e.g. `San Francisco MSA`, `New York MSA`) plus international country/city combos. The skill matches by exact string; "Bay Area" does not match `San Francisco MSA`.
- Sample size <5 → low-N flag. Radford itself suppresses cells below 3.
## Pave
Pave exports as CSV via the API or via UI download.
### Required columns
| Column | Type | Notes |
|---|---|---|
| `level_pave` | string | Pave's level normalization (e.g. `Senior IC`). |
| `function_pave` | string | Pave's function (e.g. `Engineering - Software`). |
| `location` | string | Pave's location string. |
| `n_employees` | integer | Number of employees in the cell. |
| `base_p25`, `base_p50`, `base_p75`, `base_p90` | number | Base salary percentiles. (Pave does not publish p60.) |
| `equity_p25`, `equity_p50`, `equity_p75`, `equity_p90` | number | Annualized equity in USD. |
| `total_comp_p50`, `total_comp_p75` | number | Total comp percentiles, useful for OTE calibration. |
### Notes
- Pave uses its own level normalization across firms; mapping to firm levels lives in the philosophy file.
- Pave's coverage is strongest for tech in the US and EU; APAC and emerging-market data is thinner.
- Sample size <10 → low-N flag (Pave's own threshold).
- `total_comp_p50` includes base + bonus + equity at the median. Useful for the public-range sanity check.
## Carta
Carta's compensation product exports in two flavors: cash-comp report (similar to Pave) and equity-comp report (cap-table-aware).
### Required columns (cash report)
| Column | Type | Notes |
|---|---|---|
| `role` | string | Carta's normalized role label. |
| `seniority` | string | `Junior`, `Mid`, `Senior`, `Staff`, `Principal`. |
| `location` | string | Carta's location string. |
| `n_companies`, `n_employees` | integer | Cell sample sizes (both required). |
| `base_p50`, `base_p75` | number | Base salary percentiles. |
| `total_cash_p50`, `total_cash_p75` | number | Base + bonus. |
### Required columns (equity report)
| Column | Type | Notes |
|---|---|---|
| `role` | string | Same as cash report. |
| `seniority` | string | Same. |
| `location` | string | Same. |
| `company_stage` | string | `Seed`, `Series A`, `Series B`, etc. |
| `equity_pct_p25`, `equity_pct_p50`, `equity_pct_p75`, `equity_pct_p90` | number | Equity as percentage of fully diluted shares outstanding. |
### Notes
- Carta's coverage is strongest for early-stage US startups. For mid-stage and public-company benchmarking, Radford or Pave are stronger.
- Equity reported as `equity_pct_p*` (percent of company), not dollar value. The skill converts using the firm's most recent valuation.
- Sample sizes <15 for equity → low-N flag (equity is more variance-heavy than cash).
## Custom CSV
For in-house surveys or sources not on the list above. The skill reads any CSV with the following minimum columns:
| Column | Type | Required | Notes |
|---|---|---|---|
| `level` | string | yes | Whatever ladder; must map to firm ladder via philosophy file. |
| `function` | string | yes | Whatever taxonomy; must match role definition. |
| `geography` | string | yes | Free-text or coded; must match exactly. |
| `sample_size` | integer | yes | Used for low-N flag. |
| `base_p50` | number | yes | Median base salary, USD. |
| `base_p25`, `base_p75`, `base_p90` | number | recommended | More percentiles enable wider band-targeting options. |
| `equity_value_p50` | number | for equity-bearing roles | Annualized equity value, USD. |
| `bonus_p50` or `ote_p50` | number | for sales / variable-comp roles | Target. |
| `min_sample_size` | integer | yes | The threshold below which the skill flags low-N. Set per-survey based on the survey methodology. |
### Notes
- Custom CSVs are useful for mid-cycle re-benchmarks against a peer cohort (your own team's data plus a few comparable firms) or for in-house comp-committee internal reviews.
- The `min_sample_size` field is critical — without it the skill cannot calibrate the low-N threshold and falls back to a conservative default (15).
## Adding a new survey source
To add a new source:
1. Document the source's export schema in this file with the same shape as the entries above.
2. Update the skill's source detector to recognize the new format (filename pattern, header pattern, or both).
3. Add the source's documented sample-size threshold.
4. If the source uses a different geography or level taxonomy, document the mapping in the philosophy file.
## Refresh cadence
Survey data shifts faster than yearly. The benchmark skill warns at >6 months on the export's dated metadata; that's the floor. Quarterly refresh is the operating norm for serious comp programs.
For Radford: Q1, Q2, Q3, Q4 standard cycles.
For Pave: monthly refresh available via API.
For Carta: quarterly equity reports, monthly cash updates available.
# Compensation philosophy file template
The compensation-benchmark skill calibrates survey data against the firm's compensation philosophy. This file is the philosophy. Copy the JSON below to `philosophy.json` (or wherever your skill config points), fill it in, and version it in git.
The philosophy is rarely revised — usually annually at most, often less. When it changes, every benchmark recommendation post-change uses the new philosophy. The skill captures the philosophy version in the audit record so future audits can reproduce the recommendation.
## JSON shape
```json
{
"philosophy_version": "2026.1",
"effective_from": "2026-01-10",
"approver": "Comp Committee minutes 2026-01-08",
"target_percentiles": {
"engineering": {
"base": 60,
"equity": 75,
"bonus_or_ote": 50
},
"sales": {
"base": 50,
"equity": 50,
"bonus_or_ote": 75
},
"go_to_market_other": {
"base": 60,
"equity": 60,
"bonus_or_ote": 60
},
"g_and_a": {
"base": 60,
"equity": 50,
"bonus_or_ote": 50
}
},
"band_widths_pct": {
"default": 10,
"by_level": {
"junior": 8,
"senior": 12,
"staff": 15,
"principal": 18,
"executive": 25
}
},
"level_mapping": {
"firm_to_radford": {
"L3": "P3",
"L4": "P4",
"L5": "P4",
"L6": "P5",
"L7": "P6",
"L8": "P7"
},
"firm_to_pave": {
"L3": "Mid IC",
"L4": "Senior IC",
"L5": "Senior IC",
"L6": "Staff IC",
"L7": "Principal IC",
"L8": "Senior Principal IC"
}
},
"geography_adjustments": {
"remote_us": 0,
"remote_intl": -15,
"san_francisco_msa": 0,
"new_york_msa": 0,
"seattle_msa": -3,
"austin_msa": -10,
"london": -8,
"toronto": -12,
"remote_latam": -35
},
"current_strike_price_usd": 5.20,
"vesting_period_years": 4,
"equity_grant_type": "RSU",
"exception_band": {
"max_above_top": 15,
"approval_required": "comp_committee"
},
"public_range_policy": {
"minimum_band_width_pct": 15,
"include_equity_target_dollar": true,
"include_bonus_target_dollar": true
},
"pay_equity_audit_cadence_months": 12,
"last_pay_equity_audit": "2026-02-15"
}
```
## Field-by-field
### `philosophy_version`, `effective_from`, `approver`
Versioning. The skill captures `philosophy_version` in the audit record so the recommendation is reproducible against a specific version of this file. `effective_from` is the date the philosophy applies to NEW recommendations — recommendations made before that date used the prior philosophy. `approver` cites the approval source (Comp Committee minutes, board resolution, etc.).
### `target_percentiles`
Per function family, the target percentile per component. The most common patterns:
- **Engineering**: 60th base, 75th equity (founder-friendly equity to attract IC talent), 50th bonus.
- **Sales**: 50th base, 50th equity, 75th OTE (the variable comp is the lever).
- **G&A**: 60th base across components (predictable, market-rate).
If the firm's strategy is "we pay top of market across the board," set everything to 75th. If the firm's strategy is "we pay base at market and over-index on equity," set base to 50th and equity to 75th-90th.
### `band_widths_pct`
The recommended band width as a percentage of the midpoint. Default 10% (recommended midpoint ±10%). Per-level overrides absorb the wider individual variance at senior levels.
If a single number is too rigid, the skill respects the per-level overrides.
### `level_mapping`
Mapping from the firm's internal ladder to each survey's ladder. The skill cannot infer this — it has to be specified per survey the firm uses. If a level mapping is missing, the skill halts and asks the user to add it.
This is the single most-edited part of the philosophy; it's also the most consequential, because the wrong mapping shifts every recommendation by a percentile-band.
### `geography_adjustments`
Per-geography multiplier. `0` means use the survey's value for that geography directly. `-15` means apply a 15% reduction (e.g. for `remote_intl`). The adjustments must be defensible — random adjustments here are how pay-equity gaps creep in.
If the firm has a published location-based pay policy, this section should match it line-for-line.
### `current_strike_price_usd`, `vesting_period_years`, `equity_grant_type`
Used for converting annualized survey equity values to grant size. Strike price is the most-recent 409A or option-grant strike. Vesting is typically 4 years. Grant type matters for tax framing but not for the band math.
### `exception_band`
When the skill is asked about a band-exception ("can we offer above the top?"), the philosophy says how high (`max_above_top: 15` means up to 15% above the top of the recommended band) and who approves. The skill itself does NOT approve exceptions; it surfaces the policy.
### `public_range_policy`
Compliance posture for NYC LL 32-A, CO/CA/WA pay-transparency requirements. `minimum_band_width_pct: 15` is the firm's "good faith" floor — the skill warns if a recommended band falls below this width.
### `pay_equity_audit_cadence_months`, `last_pay_equity_audit`
For the audit-record metadata. The skill notes the cadence and last audit so the recommendation can be flagged if the firm is overdue for an equity audit.
## When to revise the philosophy
- **Strategy shift** — the firm decides to over-index on equity vs. cash. Update target percentiles.
- **New geography** — opening a new region. Add to `geography_adjustments` based on local market data.
- **New survey added** — add a level mapping for the new survey.
- **Pay-equity audit findings** — if the audit surfaces gaps, the philosophy may need revision (band widths, geography adjustments).
Each revision bumps `philosophy_version`. Old audit records remain interpretable against their respective version.
## What the philosophy is NOT
- It is NOT a candidate-by-candidate negotiation guide.
- It is NOT a one-time setup; it evolves with the firm.
- It is NOT confidential to the recruiter — the philosophy should be visible to every hiring manager, ideally documented internally for transparency.
