job_posting_analysis_skill

Overview

This skill extracts technology requirements from public job postings and company career pages to infer a company's likely tech stack and hiring signals. It locates careers pages, detects applicant tracking systems, parses role descriptions for technology mentions, and summarizes frequency and role-based patterns. Outputs include extracted technologies, role distribution, inferred stack areas, and confidence indicators. The goal is to turn hiring text into actionable intelligence about platform choices and priorities.

How this skill works

The skill first locates a company's careers or jobs page using common paths, subdomains, and site search heuristics. It detects ATS platforms by matching known URL patterns to signal company size and hiring maturity. Job descriptions are parsed with regex patterns and curated keyword lists (languages, frameworks, databases, cloud, tools) to extract technology mentions and context. Technologies are weighted by mention frequency across postings and combined with role-type signals to infer frontend, backend, and infrastructure priorities.

When to use it

• When you need to infer a company's tech stack from public hiring material
• Prior to penetration testing or asset reconnaissance to prioritize technology-specific checks
• When evaluating targets for bug bounty triage to focus on likely platforms
• To enrich OSINT profiles with engineering size and hiring signals
• When comparing tech trends across competitors or market segments

Best practices

• Respect robots.txt and only fetch public pages; avoid creating accounts or applying to jobs
• Distinguish required vs. nice-to-have mentions when weighting importance
• Combine job-posting signals with direct technical evidence (fingerprints, headers, repos) for higher confidence
• Rate-limit fetches per the documented limits and log all requests for audit
• Treat ATS detections and inferred stacks as probabilistic; report confidence and contexts

Example use cases

• Scan a target's career pages to prioritize testing frameworks (e.g., React, Next.js) and libraries
• Triage bounty targets by identifying common backend languages and cloud providers
• Map hiring signals to likely CI/CD and infrastructure tools for attack surface modeling
• Track technology adoption trends across an industry by aggregating many companies' postings
• Estimate engineering team size and growth stage from counts of open roles and ATS type