| name | salary-research |
| description | White-hat salary benchmarking for a (geography, role) pair. Cached at cache/salary-<geo>-<role>.md so repeat calls don't re-fetch. Public sources only — postings, Levels.fyi, Glassdoor, Payscale, BLS/Eurostat, SEC, Blind/Fishbowl as signal. Confidence-tagged per data point. |
| disable-model-invocation | false |
| allowed-tools | Read, Write, Bash(mkdir *), Bash(test *), Bash(date *) |
Salary Research
Compensation ranges for a target role in a given market, using only publicly available data. Cached so you don't waste a search budget on the same query.
Inputs
$ARGUMENTS:
- Required:
--role=<title> and --geo=<market> (e.g. --role="staff-pm" --geo="tel-aviv", or --geo=remote-emea).
- Optional:
--seniority=<junior|mid|senior|staff|principal> — disambiguates if role doesn't already imply.
- Optional:
--currency=<CCY> — default = market default (USD for US/remote, EUR for EU, ILS for Israel).
- Optional:
--company=<slug> — narrows to a specific employer.
- Optional:
--refresh — ignore cache and re-fetch.
Examples
/career:salary --role="staff-pm" --geo="tel-aviv"
/career:salary --role="ai-researcher" --geo=remote-emea --refresh
/career:salary --role="solutions-architect" --geo="dublin" --company=stripe
White-hat rule
Use only:
- Public job postings (employer sites, LinkedIn, Indeed).
- Salary platforms (Levels.fyi, Glassdoor, Payscale, Salary.com, LinkedIn Salary Insights).
- Government wage data (BLS, Eurostat, national stats).
- SEC / regulatory filings (exec comp).
- Anonymous community sources (Blind, Fishbowl) — signal, not fact.
- Published comp surveys.
Never impersonate recruiters, submit fake applications, scrape behind auth, or pressure contacts to leak bands.
Procedure
1. Resolve cache path
${WORKING_FOLDER}/cache/salary-<geo-slug>-<role-slug>.md. If exists and --refresh not set and mtime < 90 days, read it and short-circuit to step 5 (with a "cached" note).
2. (Optional) Routing decision
If config RESEARCH_MODEL != claude, call research-router --task=salary-research and dispatch the bulk fetch to the routed model.
3. Research
For the (role, geo) pair:
- 3+ salary platforms where possible.
- Capture base / bonus / equity separately when distinguishable.
- Per-company level equivalencies where relevant.
- Geographic adjustment if source data is in a different market.
- Confidence per data point:
Confirmed (employer/regulator) | Reported (platform aggregate) | Inferred (triangulated).
If --company set, narrow to that employer plus 3-5 peers as comparison.
4. Write findings
Cache file shape:
# {{role}} — {{geo}} salary benchmark
**Generated:** YYYY-MM-DD
**Currency:** {{CCY}}
**Sources used:** {{list}}
## Summary
p25 / p50 / p75 / p90 total comp: {{...}}
## By company
### {{company}}
- source: {{url}}
- confidence: Confirmed | Reported | Inferred
- | level | title | base | bonus | equity (annual) | total |
- notes: signing / refreshers / vest schedule.
## Caveats
- {{any market-specific notes, e.g. ILS volatility, EU equity rarity, etc.}}
5. Print summary
cache: cache/salary-<geo>-<role>.md ({{cached|fresh}})
range: {{p25}}–{{p75}} ({{currency}})
sample: {{N companies, M data points}}
Guardrails
- White-hat only. Don't roleplay as a recruiter or applicant for data.
- Tag confidence honestly. "Inferred" beats a fake "Confirmed."
- Cache hits don't get a refresh — explicitly require
--refresh. The user is paying for the search budget.
- If geo is "remote", state which timezone bands the data covers; remote-PT and remote-EU pay differently.
Failure modes
- Insufficient data (< 3 sources) → still write the cache, but mark
confidence: low at the top and recommend /career:research-company for specific targets.
- Currency conversion ambiguous → use one clear CCY and note the exchange-rate date.
