Промпт: разбор вакансий и скиллов рынка
Готовил его под Claude Chrome extension для job-бордов: открываешь вакансии в браузере, расширение читает их прямо со страницы и собирает структурный разбор - требования по частоте, глубину AI (0-4), распределение по доменам и пробелы рынка.
INPUT DATA. Промпт на английском - так стабильнее; вывод можешь попросить на русском.TASK 1 — Core requirements map
For each job description provided, extract and categorize all stated requirements into:
- Hard skills (tools, techniques, methodologies)
- Soft skills (communication, critical thinking, ownership, etc.)
- Domain knowledge (fintech, payments, crypto, mobile, customer care, etc.)
- Experience level (Junior / Middle / Senior / Lead) and years required
- Language requirements
Then aggregate across all postings and rank requirements by frequency (most common → least common).
TASK 2 — AI knowledge requirements: level of detail
Identify every mention — explicit or implicit — of AI, machine learning, or data-driven tooling in the job descriptions. For each mention, classify it by depth:
| Level | Definition | Example signal words |
|---|---|---|
| 0 – None | No AI mention at all | (absence) |
| 1 – Awareness | AI is mentioned as part of company description only | "AI-powered platform", "company uses AI" |
| 2 – User | Candidate expected to use AI tools as part of workflow | "work with AI-driven dashboards", "use AI copilots" |
| 3 – Collaborator | Candidate expected to define requirements for AI features or data products | "define tasks for analysts", "measure impact of ML releases" |
| 4 – Builder/Owner | Candidate expected to lead AI/ML product direction or own AI-related deliverables | "own PRD for AI features", "design AI workflows" |
Produce:
- A table showing each job posting and its AI Knowledge Level (0–4).
- A short summary of the overall AI maturity signal in the BA job market based on the sample.
TASK 3 — Domain distribution
From the job titles, descriptions, and company contexts, classify each posting into one or more of the following business domains. Then calculate the percentage distribution:
- Payments & Acquiring
- Banking & Lending (consumer credit, KYC, origination, deposits)
- Customer Care & Support Operations
- Crypto & Blockchain
- Mobile Products
- Trading & Financial Markets
- Authentication & Security
- Telephony & CRM
- Multi-domain / General
Produce:
- A domain tag for each job.
- A percentage breakdown chart (text format or table).
- A brief insight: which domains are over-represented and why.
TASK 4 — Skills gap & trend analysis
Based on the full dataset:
- What skills appear in Senior/Lead roles but NOT in Junior/Middle roles?
- What tools or methodologies are universally expected regardless of level?
- What is missing that you would expect to see in a modern BA role (e.g., prompt engineering, AI tool fluency, data literacy)?
- What does the geographic/remote distribution tell us about the hiring market?
Input data
You are running inside the Claude Chrome extension on a job board.
Read the job descriptions directly from the open page(s)/tabs —
title, company, level, and full text. If multiple postings are
open, analyze all of them.
(If this prompt is used in Claude Code instead, the job
descriptions are pasted below — one per section.)Output format
- Use structured markdown with headers and tables.
- Keep insight sections to 3–5 sentences each.
- Conclude with a one-paragraph Executive Summary suitable for a hiring manager or BA candidate.