Salary Market Scanner
Scrape live job boards and salary databases to find real compensation data for any role and location — not outdated surveys, but what companies are actually posting and paying right now.
Pre-flight Check (REQUIRED)
Before making any TinyFish call, always run BOTH checks:
1. CLI installed?
bashwhich tinyfish && tinyfish --version || echo "TINYFISH_CLI_NOT_INSTALLED"
If not installed, stop and tell the user:
Install the TinyFish CLI:
npm install -g @tiny-fish/cli
2. Authenticated?
bashtinyfish auth status
If not authenticated, stop and tell the user:
You need a TinyFish API key. Get one at: https://agent.tinyfish.ai/api-keys
Then authenticate:
tinyfish auth login
Do NOT proceed until both checks pass.
Step 1 — Gather inputs
You need:
- Job title / role — e.g.
Senior Software Engineer,ML Engineer,Product Designer,DevOps Engineer - Location — e.g.
London,Singapore,San Francisco,Remote - Years of experience (optional) — e.g.
3-5 years,senior,entry level - Specific company (optional) — if the user wants to know what a specific company pays
If location is not provided, ask before proceeding. Salary data varies dramatically by market.
Step 2 — Parallel salary scan
Fire all agents simultaneously. Sources vary by location — include the most relevant ones.
bash# Agent 1 — Levels.fyi (best for tech roles, especially US/global big tech) tinyfish agent run \ --url "https://www.levels.fyi/t/{ROLE_SLUG}/?country={COUNTRY}" \ "You are on Levels.fyi showing compensation data for {ROLE} in {LOCATION}. Extract: - Median total compensation - Base salary range (p25 to p75) - Bonus range - Stock/equity range (if shown) - Sample size (number of data points) - Top companies listed and their compensation ranges - Any breakdown by years of experience if visible STRICT RULES: - Do NOT click any company or individual entry - Read only the aggregate data visible on the page - If no data for this location, return {found: false, reason: 'no data for location'} Return JSON: {found: bool, median_total, base_p25, base_p75, bonus_range, equity_range, sample_size, top_companies: [{company, base, total}], yoe_breakdown: []}" \ --sync > /tmp/sal_levels.json & # Agent 2 — Glassdoor salaries tinyfish agent run \ --url "https://www.google.com/search?q=glassdoor+{ROLE_ENCODED}+salary+{LOCATION_ENCODED}+site:glassdoor.com/Salaries" \ "You are on Google search results. Find the most relevant Glassdoor salary page for {ROLE} in {LOCATION} and click it. On the Glassdoor salary page extract: - Median base salary - Salary range (low to high) - Number of salary reports - Additional pay (bonus, profit sharing) range if shown - Top companies paying for this role if listed STRICT RULES: - Click only the first Glassdoor salary result - Do NOT click any other links after landing on Glassdoor - Read only the aggregate salary data visible on the page - If the page asks you to sign in, extract whatever is visible before the gate Return JSON: {median_base, salary_low, salary_high, report_count, additional_pay_range, top_companies: [{company, salary}]}" \ --sync > /tmp/sal_glassdoor.json & # Agent 3 — LinkedIn Jobs (extract posted salary ranges from active listings) tinyfish agent run \ --url "https://www.linkedin.com/jobs/search/?keywords={ROLE_ENCODED}&location={LOCATION_ENCODED}&f_SB2=1&sortBy=DD" \ "You are on LinkedIn job search results for {ROLE} in {LOCATION}, filtered to show salary information, sorted by date. For each job listing card visible on the page: - Click into the listing to open the job detail panel on the right - Look for the salary range in the detail panel (often shown near the top under the job title) - Extract: job title, company name, salary range, employment type - Go back to the listing and repeat for the next one STRICT RULES: - Only extract listings that show an explicit salary — skip those without - Maximum 10 listings then stop - Do NOT navigate away from the search results page Return JSON array: [{title, company, salary_range, employment_type}]" \ --sync > /tmp/sal_linkedin.json & # Agent 4 — Indeed salaries tinyfish agent run \ --url "https://www.indeed.com/career/{ROLE_INDEED}/salaries?from=top_sb&l={LOCATION_ENCODED}" \ "You are on Indeed's salary page for {ROLE} in {LOCATION}. Extract: - Average base salary - Salary range (low to high) - Number of salary reports - Salary by experience level (if shown: entry, mid, senior) - Top paying companies for this role (if listed) STRICT RULES: - Do NOT click any links - Read only the aggregate data on this page Return JSON: {average_salary, salary_low, salary_high, report_count, by_experience: [{level, salary}], top_companies: [{company, salary}]}" \ --sync > /tmp/sal_indeed.json & wait echo "=== LEVELS ===" && cat /tmp/sal_levels.json echo "=== GLASSDOOR ===" && cat /tmp/sal_glassdoor.json echo "=== LINKEDIN ===" && cat /tmp/sal_linkedin.json echo "=== INDEED ===" && cat /tmp/sal_indeed.json
Before running, replace:
{ROLE}— human-readable e.g.Senior Software Engineer{ROLE_ENCODED}— URL-encoded e.g.Senior%20Software%20Engineer{ROLE_SLUG}— Levels.fyi slug e.g.software-engineer{ROLE_INDEED}— Indeed format e.g.software-engineer{LOCATION}— e.g.London,Singapore{LOCATION_ENCODED}— URL-encoded e.g.London%2C%20England{COUNTRY}— country code for Levels.fyi e.g.GB,SG,US
Step 3 — Synthesize the market picture
Combine data from all sources and calculate aggregate ranges.
## Salary Market Report — {ROLE} · {LOCATION} *Live data scraped from Levels.fyi, Glassdoor, LinkedIn, and Indeed* *{date} · Based on {N} total data points* --- ### 💰 Compensation Summary | | Low | Median | High | |---|---|---|---| | **Base Salary** | {low} | {median} | {high} | | **Total Comp** (incl. bonus/equity) | {low} | {median} | {high} | > All figures in {CURRENCY}. "Total comp" includes base + annual bonus + annualized equity where data is available. --- ### 📊 By Experience Level | Level | Typical Base | |---|---| | Entry (0-2 yrs) | {range} | | Mid (3-5 yrs) | {range} | | Senior (6+ yrs) | {range} | | Staff / Principal | {range} | *(Skip levels where no data was found)* --- ### 🏢 What Companies Are Posting From active LinkedIn job listings with disclosed salaries: | Company | Role | Posted Range | |---|---|---| | {company} | {title} | {range} | --- ### 🏆 Top Paying Companies *(From Levels.fyi and Glassdoor)* | Company | Median Base | Median Total | |---|---|---| | {company} | {base} | {total} | --- ### 📈 Market Signals {2-3 sentences on what the data says about this market — is it competitive, is there a wide spread, are companies being transparent about pay?} --- ### 🔍 Data Sources - Levels.fyi: {sample_size} data points / not found - Glassdoor: {report_count} salary reports / not found - LinkedIn: {N} active listings with disclosed salaries - Indeed: {report_count} salary reports / not found
Edge Cases
- Levels.fyi has no data for this location — lean on Glassdoor and Indeed; note that Levels.fyi skews toward US big tech
- Role title is unusual — try common variations (e.g. "ML Engineer" → "Machine Learning Engineer", "AI Engineer")
- Location is a small city — broaden to the country or nearest major city and note the change
- Remote role — scrape for both the user's country and the US market, present both (remote jobs often use US pay bands)
- Non-tech role — skip Levels.fyi (tech-only), rely on Glassdoor and Indeed
- Salary shown in different currencies — normalize to the local currency and note conversion rate used
- User is asking if their salary is competitive — after presenting the data, ask what they're currently earning and give a direct assessment
