career-ops vs Claude Ads
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Open-source AI job search agent that scores jobs against your CV and drafts applications for you to submit.
Open source · MIT
Claude Code skill that audits paid ad accounts across 12 platforms and reports what to fix first.
The short answer
Choose career-ops if you need…
- Job seekers who apply to many listings and want to filter them before spending time on each one
- People already using Claude Code, Codex, or OpenCode who want job search inside that environment
- Candidates who prefer keeping their CV and data on their own machine
- Anyone wanting help tailoring a resume and cover letter to specific postings
Choose Claude Ads if you need…
- Agencies and consultants reviewing several client ad accounts
- In-house marketing teams that want a structured audit of their paid campaigns
- People already using Claude Code who want ad-account reviews inside it
- Teams that want reports saved as versioned JSON, Markdown, HTML, or PDF
Side-by-side
| career-ops | Claude Ads | |
|---|---|---|
| Type | AI model / agent | AI model / agent |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 79.4k | 10.7k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | JavaScript | Python |
| Last code update | 4 days ago | 5 days ago |
What each one is
career-ops
career-ops is a free, MIT-licensed tool that runs locally inside an AI coding CLI such as Claude Code or Codex. It scans job boards, scores each listing from 1 to 5 against your CV, and helps tailor an ATS-friendly resume and cover letter, plus interview prep and an application tracker. It helps fill in applications, but you press Submit yourself.
Claude Ads
Claude Ads is a free, MIT-licensed skill for Claude Code. You give it ad account data such as exports, screenshots, or summaries, and it reports what is working, what is broken, and what to fix first across twelve ad platforms. The README says it reads data and reports back, and does not change accounts on its own. Every check ends as pass, fail, unknown, or not applicable, so missing data is shown rather than guessed.
Best for
career-ops
- Job seekers who apply to many listings and want to filter them before spending time on each one
- People already using Claude Code, Codex, or OpenCode who want job search inside that environment
- Candidates who prefer keeping their CV and data on their own machine
- Anyone wanting help tailoring a resume and cover letter to specific postings
Claude Ads
- Agencies and consultants reviewing several client ad accounts
- In-house marketing teams that want a structured audit of their paid campaigns
- People already using Claude Code who want ad-account reviews inside it
- Teams that want reports saved as versioned JSON, Markdown, HTML, or PDF
Strengths
career-ops
- Runs locally, so your CV and job data stay on your machine according to the README's local-first design
- Open source under the MIT license, with a public codebase you can inspect
- Works inside several AI coding CLIs rather than one proprietary service
Claude Ads
- Covers twelve ad platforms with a per-platform audit approach described in the README
- Marks unknown results explicitly instead of guessing when data is missing
- Changes to accounts are draft-first and read-only by default
Not ideal for
career-ops
- People who want a fully automatic bot that submits applications for them, since the README says you press Submit
- People without an AI coding CLI set up, because the tool runs inside one
- Anyone who is uncomfortable with an AI tool reading and rewriting their CV
Claude Ads
- Anyone wanting it to make live changes to ad accounts automatically, since the README says it does not change accounts on its own
- Users without Claude Code or a compatible agent host, since it is built for that runtime
- Teams needing verified support beyond Claude Code, since the README marks other hosts as experimental
Trade-offs
career-ops
- The README is truncated and does not fully document features, so exact capabilities are unclear from it alone
- Output quality depends on the underlying AI model you connect, which the README does not benchmark
- Requires familiarity with a command-line AI tool, which may be a barrier for non-developers
Claude Ads
- Only Claude Code is verified; other hosts are labeled experimental
- Requires you to supply account evidence yourself, and the quality of audits depends on that data
- Some installs depend on platform-specific requirements such as CPython 3.11 or 3.12 and glibc 2.28 or later on Linux
Getting started
career-ops
Initialize career-ops
npx @santifer/career-ops initClaude Ads
Add the marketplace in Claude Code
/plugin marketplace add agricidaniel/claude-adsInstall the plugin in Claude Code
/plugin install claude-ads@ai-marketing-hub-claude-adsFrequently asked questions
What is the difference between career-ops and Claude Ads?
career-ops: Open-source AI job search agent that scores jobs against your CV and drafts applications for you to submit. Claude Ads: Claude Code skill that audits paid ad accounts across 12 platforms and reports what to fix first. career-ops suits job seekers who apply to many listings and want to filter them before spending time on each one, while Claude Ads suits agencies and consultants reviewing several client ad accounts.
Are career-ops and Claude Ads free?
career-ops: Free and open source. Claude Ads: Free and open source.
Is career-ops or Claude Ads more popular?
career-ops has more GitHub stars (79.4k) than Claude Ads (10.7k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to career-ops and Claude Ads?
Other options people compare them with include AI Job Search, AI Berkshire, invisible_dots, Hermes Agent, REA.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.