A job-posting reader that turns pasted text, URL content, or a PDF, DOCX, Markdown, or plain-text file into structured job requirements and keywords.

Use it to prepare a job description for resume structure checks, keyword matching, and gap analysis.

It converts a posting into the format needed for meaningful resume checks instead of treating it as raw text with no organized skills.

parse-job — Skill for Claude Code and Codex by dcassil: A job-posting reader that turns pasted text, URL content…

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods

npx agentmods add skills/dcassil/resume-kit/parse-job

Any agent

npx skills add dcassil/resume-kit --skill parse-job

Clone the repo

git clone --depth 1 https://github.com/dcassil/resume-kit

Made for: Claude Code, Codex.

Or install resume-intelligence, the plugin that ships this one along with the rest of its 31 skills, 1 command, 1 hook.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/dcassil/resume-kit/parse-job.svg)](https://agentmods.dev/skills/dcassil/resume-kit/parse-job)

Your own site

<a href="https://agentmods.dev/skills/dcassil/resume-kit/parse-job"><img src="https://agentmods.dev/badge/skills/dcassil/resume-kit/parse-job.svg" alt="Measured on agentmods" height="20"></a>

Per session 140 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,563 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5	$0.00140	$0.02563
Opus 5	$0.00070	$0.01282
Sonnet 5	$0.00028	$0.00513
Haiku 4.5	$0.00014	$0.00256

Measured 3d ago against content hash a291bccdb776, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

parse-job scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/resume-intelligence/skills/parse-job/SKILL.md · 227 lines

How it starts

The opening of the file, as written. The whole thing — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Renamed: parse-job was job-to-json before v1.0.0 (see RIT-A-0005).

parse-job — build a JobDescription from a posting

Prerequisites

Run the shared Prerequisites gate first — see _shared/prerequisites.md.

Required input: the job posting as pasted text, a URL's content, or a PDF/DOCX/MD/text file — provided by the caller.
If it is missing: STOP and ask the caller for the posting text, URL, or file. There is no upstream skill — this skill is itself the first step that produces the JobDescription JSON others depend on.

Purpose

check-structure, check-keywords, and check-gaps score a resume against a JobDescription. Deterministic text extraction only captures the raw text — it does NOT populate the structured requirements and keywords, so skills_coverage comes back 0. This skill fixes that: a confined interpretation subagent extracts the structured skills/requirements from the posting so scoring is meaningful — no LLM provider required.

Same shape as parse-resume: extraction is deterministic, interpretation is confined to a subagent, and the pointer is recorded by code (set-active), not by hand-editing config.json.

Which gate applies

resume-tool validate-faithfulness targets a ResumeDocument — it is the authoritative machine gate for parse-resume, not for job postings. Do NOT force the resume faithfulness gate on a JobDescription. For jobs, the faithfulness discipline is enforced by the prose Extraction gates below, which the interpretation subagent must follow (chiefly: raw_text is verbatim and no skills/requirements are invented). If a JSON-schema round-trip is available (JobDescription.model_validate(...)), run it — that is a schema check, still not the resume faithfulness gate.

Before you start: read prior learnings

Read resume-kit/learning/parse-job.md (if present) first, and append any new gotcha you hit so future runs benefit.

Read the full file on GitHub · 227 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

3d ago First seen · 227 lines · 140 tokens per session scan A a291bccdb776

Subscribe to this mod's changes

parse-job is a skill published in the GitHub repository dcassil/resume-kit (0 stars, last pushed 23d ago), licensed Apache-2.0. It adds 140 tokens to every session and 2,563 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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