How do I install the Job Description Analyzer skill?

Run npx skillmds@latest add ebarti/job-description-analyzer in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Job Description Analyzer safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Job Description Analyzer?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Job Description Analyzer free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Job Description Analyzer?

ebarti (@ebarti) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Job Description Analyzer by ebarti — Agent Skill

Name: Job Description Analyzer
Author: ebarti

Job Description Analyzer

Parses a raw job description into a structured TargetProfile — role, seniority, must-have vs nice-to-have requirements, hard/soft skills, responsibilities, ATS keywords, and a truthful crossover (their-term ↔ your-term) map. Distilled from "Resumes For Dummies" (AI-era ed.). Built for automated pipelines (e.g. JobHunter) as the shared front-end that feeds both resume tailoring and scoring. Use this skill when: - Turning a job posting into structured requirements before tailoring a resume - Separating must-have from nice-to-have qualifications - Splitting hard skills (tools/certs) from soft skills - Extracting ATS keywords to mirror truthfully - Inferring a role's target seniority and focus - Building a crossover map between a JD's wording and a candidate's vocabulary - Providing a TargetProfile to resume-content-writer or resume-fit-scorer

ebarti 0ac5422 9 files · 40.9 KB Updated Sep 17, 2026

File contents

job-description-analyzer

Converts a messy job posting into one clean, machine-usable TargetProfile: what's required vs preferred, the hard and soft skills, the responsibilities, the exact ATS keywords to mirror, the inferred seniority/focus, and a truthful crossover map. This is the shared source of truth so tailoring and scoring agree on the target. Built from "Resumes For Dummies" (AI-era ed.), chapters 1, 3, 5, 6.

Role in the JobHunter pipeline

[job-description-analyzer] → resume-content-writer → resume-fit-scorer
       TargetProfile  ───────────────┴───────────────────────┘
       (one source of truth feeds both tailoring and scoring)

See workflows/analyze-job-description.md for the full I/O contract and the TargetProfile schema.

Quick Start

Read guidelines.md — it routes from your task/symptom to the right files.
For an end-to-end analysis, follow workflows/analyze-job-description.md.
Load only the reference files the guidelines point to.
Emit a TargetProfile (or the caller's output_schema); keep crossover mappings truthful.

References

Category	Files	Purpose
`references/requirement-extraction/`	knowledge, rules, examples	JD anatomy; must vs nice; hard vs soft skills; seniority inference
`references/keywords-and-crossover/`	knowledge, rules, examples	ATS keywords; crossover mapping; over-cloning risk

Workflows

Task	Workflow
Analyze a JD into a structured TargetProfile	`workflows/analyze-job-description.md`

Guidelines

See guidelines.md for task/symptom-based file selection, a decision tree, the full file index, and common combinations.

---name: job-description-analyzerdescription: Parses a raw job description into a structured TargetProfile — role, seniority, must-have vs nice-to-have requirements, hard/soft skills, responsibilities, ATS keywords, and a truthful crossover (their-term ↔ your-term) map. Distilled from "Resumes For Dummies" (AI-era ed.). Built for automated pipelines (e.g. JobHunter) as the shared front-end that feeds both resume tailoring and scoring. Use this skill when: - Turning a job posting into structured requirements before tailoring a resume - Separating must-have from nice-to-have qualifications - Splitting hard skills (tools/certs) from soft skills - Extracting ATS keywords to mirror truthfully - Inferring a role's target seniority and focus - Building a crossover map between a JD's wording and a candidate's vocabulary - Providing a TargetProfile to resume-content-writer or resume-fit-scorer---# job-description-analyzerConverts a messy job posting into one clean, machine-usable `TargetProfile`: what's required vs preferred, the hard and soft skills, the responsibilities, the exact ATS keywords to mirror, the inferred seniority/focus, and a truthful crossover map. This is the shared source of truth so tailoring and scoring agree on the target. Built from "Resumes For Dummies" (AI-era ed.), chapters 1, 3, 5, 6.## Role in the JobHunter pipeline```[job-description-analyzer] → resume-content-writer → resume-fit-scorer TargetProfile ───────────────┴───────────────────────┘ (one source of truth feeds both tailoring and scoring)```See `workflows/analyze-job-description.md` for the full I/O contract and the `TargetProfile` schema.## Quick Start1. Read `guidelines.md` — it routes from your task/symptom to the right files.2. For an end-to-end analysis, follow `workflows/analyze-job-description.md`.3. Load only the reference files the guidelines point to.4. Emit a `TargetProfile` (or the caller's `output_schema`); keep crossover mappings truthful.## Contents### References| Category | Files | Purpose ||----------|-------|---------|| `references/requirement-extraction/` | knowledge, rules, examples | JD anatomy; must vs nice; hard vs soft skills; seniority inference || `references/keywords-and-crossover/` | knowledge, rules, examples | ATS keywords; crossover mapping; over-cloning risk |### Workflows| Task | Workflow ||------|----------|| Analyze a JD into a structured TargetProfile | `workflows/analyze-job-description.md` |## GuidelinesSee `guidelines.md` for task/symptom-based file selection, a decision tree, the full file index, and common combinations.

Frequently asked questions