careerops

CareerOps

The resume engine that never makes things up.

CareerOps is a Claude Code plugin that turns your real career history into tailored, ATS-ready resumes — automatically, repeatably, and without fabricating a single word.

If you find it useful, star the repo — it helps others find it.

The Problem with AI Resumes

Every AI resume tool has the same flaw: it invents. It dresses up your experience with metrics you never hit, skills you never used, and bullet points that sound great but aren't true. That works until the interview — then it fails badly.

CareerOps takes a different approach. It treats your resume as an output of a database, not a document written from scratch each time.

How It Works

Step 1 — Build your fact database once

You capture career achievements interactively. CareerOps stores each one as a structured fact: what you did, the metric, the context, the employer, the date. Locked in. Immutable.

Step 2 — Drop in a job description

CareerOps parses the JD and scores every fact in your database against it — relevance, keyword match, experience level.

Step 3 — Approve the plan, get the resume

CareerOps proposes which roles to expand, compress, or drop based on relevance. You approve the plan. It then writes bullets with full provenance (every bullet cites the fact it came from), renders a PDF via RenderCV/Typst, and runs an 8-gate validator before handing it to you.

Why CareerOps

	Manual writing	Generic AI tools	CareerOps
Fabrication risk	Low	High	None — validator enforces it
Tailored to each JD	Only if you rewrite	Yes, but generic	Yes, from your real facts
Repeatable	No	No	Yes — same facts, different weights
Provenance	None	None	Every bullet cites a fact ID
ATS-safe output	Depends	Usually	Yes — validated, em-dash-free
You own your data	Yes	Depends	Yes — local YAML files, no cloud

Use Cases

• Active job search — apply to 10 roles with 10 tailored resumes, each grounded in real facts
• Career journaling — capture achievements as they happen, never scramble to remember them later
• Senior engineers — too much experience to fit one page; CareerOps scores and ranks what matters for each role
• Career changers — surface transferable skills from your history that align with the target JD
• Anyone who hates writing about themselves — describe what you built; CareerOps handles the framing

Prerequisites

• Claude Code installed
• Python 3.10+
• pip install pyyaml rendercv

Install

npx careerops install

Restart Claude Code. Done — all commands are live under /careerops:.

npx careerops update     # pull the latest version
npx careerops uninstall  # remove cleanly

Getting Started

1. Create your career directory and open Claude

mkdir ~/my-career && cd ~/my-career
claude

2. Run the setup wizard

/careerops:setting-up

Scaffolds your folders, collects your name, email, and links. Takes two minutes.

3. Import your existing resume (optional but recommended)

/careerops:seeding-career-db raw_data/your-resume.tex

Supports .tex and .md. CareerOps extracts structured facts from it so you start with a populated database.

4. Capture new achievements as they happen

/careerops:capturing-fact

CareerOps interviews you and stores the result. Run this any time you ship something worth remembering.

5. Apply to a job

Drop the job description into inbox/, then:

/careerops:analyzing-jd
/careerops:generating-resume

Approve the plan, get the PDF.

Commands

Setup

Command	What it does
/careerops:setting-up	First-run wizard — scaffold directories, collect your profile
/careerops:seeding-career-db	Import an existing resume into your knowledge base

Capture

Command	What it does
/careerops:capturing-fact	Interview to record a new career achievement
/careerops:capturing-evidence	Attach a URL, PR, or document to an existing fact

Apply

Command	What it does
/careerops:analyzing-jd	Parse a JD — scores all your facts by relevance
/careerops:generating-resume	Full pipeline: plan, compose, render PDF, validate, audit
/careerops:auditing-resume	Re-run the quality auditor on a generated resume
/careerops:humanizing-resume	Clean up AI-marker patterns in bullets
/careerops:logging-outcome	Record the result of an application

Health

Command	What it does
/careerops:linting-career	Schema, reference, and em-dash check on all your data
/careerops:getting-help	Quick-start guide — add full for the complete reference

Your Data

~/my-career/
├── career/
│   ├── experiences/        Your work history and facts
│   ├── applications/       One folder per application
│   ├── jd-analysis/        Parsed job descriptions
│   └── config/             Theme, rules
├── inbox/                  Drop JDs here
└── raw_data/               Source resumes for seeding

The plugin lives at ~/.claude/plugins/careerops/. Your data never touches it. No cloud sync, no accounts, no data leaving your machine.

Guarantees

• No fabrication. Every bullet traces to a fact ID you captured. The 8-gate validator enforces this mechanically.
• No em-dashes. Banned at every layer — Python scripts, hooks, and the semantic auditor all scan for them (ATS systems often choke on them).
• Immutable facts. Dates and employers are schema-locked. The pipeline cannot change what you actually did.
• You approve before render. CareerOps proposes a presentation plan and waits for your sign-off before writing a single bullet.

Tech

• Renderer: RenderCV (Typst) — produces clean, ATS-safe PDFs
• Data: Local YAML files with JSON Schema validation
• JD analysis: Claude subagent with relevance scoring
• Validation: Python (8 gates) + Claude semantic auditor

Contributing

Issues and PRs welcome. See CLAUDE.md for plugin architecture, conventions, and hard rules.

Star the repo if CareerOps saves you time.

License

MIT