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Job Market Analyzer

Analyze job market trends — salary data, in-demand skills, and hiring patterns

🤖 00 ↓  |  👤 00
intermediate35 minutes🔄 7 swappable alternatives

🧂 Ingredients

🔌 APIs

search_job_boards_and_salary_sites_for_market_data

🔄 Alternatives:

Serpapi Google results via API, structured dataGoogle Search Direct Google search APIBing Search Microsoft search with good API

extract_job_posting_details_and_salary_data

🔄 Alternatives:

Scrapingbee Handles JS renderingBrowserless Full browser for scraping

track_salary_data_and_trend_analysis

🔄 Alternatives:

Airtable Better for structured data + APINotion Databases More flexible views

📋 Step-by-Step Build Guide

STEP 1

1. Define your scope: target roles (e.g., 'AI Engineer', 'Product Manager'), ind

1. Define your scope: target roles (e.g., 'AI Engineer', 'Product Manager'), industries, and geographies

Define your scope: target roles (e.g., 'AI Engineer', 'Product Manager'), industries, and geographies

Steps:
1. Validate all required inputs are available
2. Execute the operation described above
3. Verify the result meets expected output format
4. Handle errors gracefully — retry transient failures, log and alert on persistent ones
5. Return structured output with status and any relevant data

If any required data is missing, request it from the user before proceeding.
STEP 2

2. Search job boards for current openings matching your criteria

2. Search job boards for current openings matching your criteria

Search job boards for current openings matching your criteria

Steps:
1. Validate all required inputs are available
2. Execute the operation described above
3. Verify the result meets expected output format
4. Handle errors gracefully — retry transient failures, log and alert on persistent ones
5. Return structured output with status and any relevant data

If any required data is missing, request it from the user before proceeding.
STEP 3

3. Extract from postings: title, company, salary range, required skills, experie

3. Extract from postings: title, company, salary range, required skills, experience level, remote/hybrid/onsite

Parse the input data and extract the specified fields.

Processing steps:
1. Parse the raw input (JSON response, transcript text, HTML content)
2. Identify and extract each required field
3. Normalize data formats: dates to ISO 8601, amounts to numbers, text trimmed
4. Validate extracted data — flag missing or malformed fields
5. Structure the output as a clean JSON object

For text extraction (transcripts, articles):
- Use pattern matching for structured data (dates, amounts, URLs)
- Use semantic understanding for unstructured data (key decisions, action items, sentiment)

Return both the extracted data and a confidence indicator for each field.
STEP 4

4. Aggregate salary data: median, 25th/75th percentiles by role, experience leve

4. Aggregate salary data: median, 25th/75th percentiles by role, experience level, and location

Process the data and calculate the requested metrics.

Steps:
1. Validate input data — check for nulls, out-of-range values, duplicates
2. Apply the calculation/aggregation logic
3. Compare against benchmarks or previous periods if available
4. Format results with appropriate precision (2 decimal places for percentages, whole numbers for counts)

Include: current value, previous value, change (absolute and %), trend direction (↑↓→).
Flag any anomalies: values >2 standard deviations from the mean.

If insufficient data for a reliable calculation, state the minimum needed and return partial results.
STEP 5

5. Identify most in-demand skills: rank by frequency of appearance in postings

5. Identify most in-demand skills: rank by frequency of appearance in postings

Identify most in-demand skills: rank by frequency of appearance in postings

Steps:
1. Validate all required inputs are available
2. Execute the operation described above
3. Verify the result meets expected output format
4. Handle errors gracefully — retry transient failures, log and alert on persistent ones
5. Return structured output with status and any relevant data

If any required data is missing, request it from the user before proceeding.
STEP 6

6. Track emerging skills: new requirements appearing more frequently over time

6. Track emerging skills: new requirements appearing more frequently over time

Track emerging skills: new requirements appearing more frequently over time

Steps:
1. Validate all required inputs are available
2. Execute the operation described above
3. Verify the result meets expected output format
4. Handle errors gracefully — retry transient failures, log and alert on persistent ones
5. Return structured output with status and any relevant data

If any required data is missing, request it from the user before proceeding.
STEP 7

7. Monitor specific companies: who's hiring aggressively, who's slowing down

7. Monitor specific companies: who's hiring aggressively, who's slowing down

Monitor specific companies: who's hiring aggressively, who's slowing down

Steps:
1. Validate all required inputs are available
2. Execute the operation described above
3. Verify the result meets expected output format
4. Handle errors gracefully — retry transient failures, log and alert on persistent ones
5. Return structured output with status and any relevant data

If any required data is missing, request it from the user before proceeding.
STEP 8

8. Monthly: market report — salary benchmarks, top skills, hiring trends, emergi

8. Monthly: market report — salary benchmarks, top skills, hiring trends, emerging roles, and career recommendations

Compile the gathered data into a structured report.

Format as clean Markdown with:
- Title/date header
- Executive summary (2-3 sentences)
- Key metrics section with actual numbers
- Detailed sections with bullet points
- Action items or recommendations at the end

Keep it scannable — busy people read reports in 30 seconds.
Use emoji sparingly for visual anchors (📊 metrics, ✅ wins, ⚠️ concerns, 📋 action items).
Include data comparisons: "X this period vs Y last period (↑Z%)"

If any data source was unavailable, note it clearly: "⚠️ [Source] data unavailable — excluded from this report."

🤖 Example Agent Prompt

Define your scope: target roles (e.g., 'AI Engineer', 'Product Manager'), industries, and geographies

Steps:
1. Validate all required inputs are available
2. Execute the operation described above
3. Verify the result meets expected output format
4. Handle errors gracefully — retry transient failures, log and alert on persistent ones
5. Return structured output with status and any relevant data

If any required data is missing, request it from the user before proceeding.

Copy this prompt into your agent to get started.