career-ops

Months of sending CVs into silence. So I built the filter I needed. 740 listings. 68 applied. 12 interviews. 1 offer. I was its first user. I got the job. Then I open-sourced it.

Companies use AI to filter candidates. I just gave candidates AI to choose companies.
On your machine, it tells you which jobs are real, which ones fit, and never applies in your name.

_{That red tab is the product. Do not apply. The full story →}

The people who came after me wrote down how they got hired.

_{Every card is a public issue you can open. Landed yours? Leave your card →}

Your turn

Paste one job you were about to apply to tonight. If it comes back do not apply, you just got the night back. If it comes back with a plan, you know what to do next.

npx @santifer/career-ops init

_{Open source. Local. In the AI CLI you already use. Free and local models included.}

You don't have to search alone

The silence after you hit send is not about you. Enough people saw the same thing to write the practice down, in six lines:

Apply better to fewer. Signal over volume. Evidence over keywords. A human decides. Local-first. Dignity on both sides of the table.

A few of them, in their own words.

_{Every signature is a commit you can audit. Read them all, or add yours →}

Hiring will not fix itself. The people going through it can, and they are already in the room, comparing notes and fixing each other's setups. This is where the fix gets written. A room, not a club. Let's build together.

Sponsors

career-ops is free for candidates, forever. The companies below fund the maintainer's time and keep it that way.

SerpApi · Build a portfolio project with live search data. SerpApi gives developers structured JSON/Markdown from Google Search, Maps, Shopping, and other engines through a simple API call.

Sponsorship buys clearly labeled visibility, never influence: no amount of money changes the roadmap or places anything in the product. Sponsors never appear in evaluations, rankings or recommendations.

What career-ops does for you

Paste a job. It tells you whether that night is worth it.

• Fake or stale? It flags ghost jobs and scams before you write a word.
• Not you? It scores the role against your real CV and tells you to skip a weak fit. You can override it.
• Worth it? It drafts the CV, the cover letter and the answers. You read them. You send them.
• Who do I talk to? It finds the person and drafts the note. It never sends it.
• Where does it all go? Every application stays on your machine. Nothing is uploaded to us.
• What should I learn? After a run of noes, it names the gap.

The first runs are rough. It does not know you yet. Talk to it: your CV, what you want, what you refuse. Think of it as a recruiter's first week.

On first launch it asks for all that in chat. Nothing to configure by hand.

What career-ops will not do

• Auto-submit an application. It drafts the answer to every field; you review and click Submit. The script never POSTs (prepare-application.mjs).
• Send an email. Drafts only. There is no mail transport anywhere in this codebase.
• Phone home. No telemetry, no backend of ours. Your CV goes from your machine to the AI provider you chose, and nowhere else. The only public ledger is this repo: HIRED.md and its issues.
• Push you to apply below 4.0/5. It will tell you not to. You can override it, and it will say so.

It reformulates your CV; it must never fabricate it. Today that rule lives in the prompts, not yet in an enforcing gate. Read every CV before you send it. Details in the FAQ.

Features

Quick Start

Fastest way — one command:

npx @santifer/career-ops init

💡 npx ships with Node.js — it runs the installer once, without installing anything globally. No Node yet? Install it first. (Already using a Claude Code / Gemini / Codex CLI? Then you already have it.)

This clones the latest release into ./career-ops and installs dependencies. Then:

cd career-ops
claude   # or codex / qwen / opencode / agy / grok — open your AI CLI here

On first launch, career-ops walks you through setup — your CV, profile and target roles — just by chatting. Nothing to edit by hand.

git clone https://github.com/career-ops-hq/career-ops.git
cd career-ops && npm install
npx playwright install chromium   # only needed for PDF generation
# On a non-Debian/Ubuntu Linux distro (Fedora, Arch, ...), Chromium's system
# libraries aren't installed by the line above — install them yourself with
# your distro's package manager if PDF generation fails to launch the browser
# (Playwright's own docs list the required libraries per platform).

# 2. Check setup
npm run doctor                     # Validates all prerequisites

# 3. Configure
cp config/profile.example.yml config/profile.yml  # Edit with your details
cp templates/portals.example.yml portals.yml       # Customize companies

# 4. Add your CV
# Create cv.md in the project root with your CV in markdown

# 5. Open your AI CLI in this directory
claude   # or codex / opencode / qwen / agy / grok

# Then ask your CLI to adapt the system to you:
# "Change the archetypes to backend engineering roles"
# "Translate the modes to English"
# "Add these 5 companies to portals.yml"
# "Update my profile with this CV I'm pasting"

# 6. Start using
# Paste a job URL or JD text to trigger auto-pipeline
# If your CLI supports slash commands, use /career-ops (or its CLI-specific alias)
# In Codex, ask for the same mode in plain language, e.g.:
# "Run the career-ops scan mode"
# "Run the career-ops pipeline mode for data/pipeline.md"
# "Run the career-ops pdf mode for the latest evaluated role"
# "Run the career-ops tracker mode and summarize the current statuses"

Global install

npm i -g @santifer/career-ops

This installs the career-ops binary globally so you can run it directly instead of via npx. Unlike npx @santifer/career-ops init (which bootstraps a project directory), the global install gives you a persistent career-ops command available anywhere in your terminal.

Which one should you use?

• npx @santifer/career-ops init — best for first use; creates a dedicated project folder.
• npm i -g @santifer/career-ops — best once you have a project folder and want to run career-ops commands directly.

The system is designed to be customized by your AI coding CLI itself. Modes, archetypes, scoring weights, negotiation scripts -- just ask it to change them. It reads the same files it uses, so it knows exactly what to edit.

See docs/SETUP.md for the full setup guide, docs/RUNNING_ON_A_BUDGET.md for instructions on running career-ops cheaply using custom or local models (and docs/FREE_TIER.md for running it at zero cost on Antigravity CLI's free tier), docs/AUTOMATION.md for scheduling recurring scans and a zero-token triage-to-shortlist recipe, docs/APPLY_AUTOFILL.md for details on the ATS auto-fill flow, docs/LINKEDIN_JOIN.md for cross-referencing a LinkedIn connections export against the companies in your funnel, and docs/FAQ.md for answers to common setup questions, including how story provenance prevents invented numbers. Design principles live in ARCHITECTURE.md; runtime flows in docs/ARCHITECTURE.md.

Usage

career-ops uses a shared command router. In CLIs that register slash commands, it looks like this:

/career-ops           → Show all available commands
/career-ops {JD}      → AUTO-PIPELINE: evaluate + report + PDF + tracker (paste text or URL)
/career-ops pipeline  → Process pending URLs from inbox (data/pipeline.md)
/career-ops oferta    → Evaluation only, blocks A to H (no auto PDF)
/career-ops ofertas   → Compare and rank multiple offers
/career-ops contacto  → LinkedIn power move: find contacts + draft message
/career-ops deep      → Deep research prompt about company
/career-ops interview-prep → Generate company-specific interview prep doc
/career-ops interview    → Interactive profile/CV onboarding interview
/career-ops eu-swe    → Calibrate a European SWE application before CV/apply/interview
/career-ops eu-fintech → Scan 21 EU fintech portals for Product Manager roles (zero-token)
/career-ops interview/plan → Time-blocked prep plan for an upcoming interview
/career-ops interview/practice → Practice interview, one question at a time with feedback
/career-ops interview/debrief → Post-interview debrief: close gaps, predict next round
/career-ops interview-redflag → Analyze employer warning signs before joining a company
/career-ops pdf       → PDF only, ATS-optimized CV
/career-ops text      → Tailored markdown CV (mirrors cv.md, no PDF)
/career-ops latex     → Export CV as LaTeX/Overleaf .tex
/career-ops latex-tex → Tailor your own resume.tex in place (opt-in; cv.md stays default)
/career-ops cover     → Cover letter: standalone JD paste or /career-ops cover {slug}
/career-ops email     → Formal application email draft (draft-only; never sends, submits, or clicks)
/career-ops add       → Add a project/paper/role to your CV (fetch + preview + confirm)
/career-ops expand    → Auto-discover and add missing competencies from profile links
/career-ops training  → Evaluate course/cert against North Star
/career-ops project   → Evaluate portfolio project idea
/career-ops tracker   → Application status overview
/career-ops agent-inbox → Queue/drain requests for the next session (data/agent-inbox.md)
/career-ops apply     → Live application assistant (reads form + generates answers)
/career-ops scan      → Scan portals and discover new offers
/career-ops discover  → Resolve a company list to scannable ATS boards + append to portals.yml (zero-token)
/career-ops batch     → Batch processing with parallel workers
/career-ops patterns  → Analyze rejection patterns and improve targeting
/career-ops offer-prep → Read a received offer/contract with the candidate: clause walk + lawyer questions (not legal advice)
/career-ops titles    → Suggest adjacent job titles from your CV to broaden the search
/career-ops upskill   → Aggregate skill-gap analysis from your evaluated reports
/career-ops followup  → Follow-up cadence tracker: flag overdue, generate drafts
/career-ops reply-watch → Classify employer replies and suggest tracker updates
/career-ops outcome   → Record application outcome & archive artifacts
/career-ops calibrate → Advisory report: do your evaluation scores predict your real outcomes? Reads /outcome data; never changes scoring
/career-ops update    → Update career-ops system files with diff preview + compat check

Or just paste a job URL or description directly -- career-ops auto-detects it and runs the full pipeline.

In Codex, slash commands are not guaranteed. Use the same mode names in a prompt instead, or call them from codex exec.

How It Works

You paste a job URL or description
        │
        ▼
┌──────────────────┐
│  Archetype       │  Classifies: LLMOps / Agentic / PM / SA / FDE / Transformation
│  Detection       │
└────────┬─────────┘
         │
┌────────▼─────────┐
│  A-H Evaluation  │  Match, gaps, comp research, STAR stories, legitimacy
│  (reads cv.md)   │
└────────┬─────────┘
         │
    ┌────┼────┐
    ▼    ▼    ▼
 Report  PDF  Tracker
  .md   .pdf  entry

Antigravity CLI Integration

career-ops supports Antigravity CLI natively, the same way it supports Claude Code and OpenCode. All slash commands are available through the shared skill entrypoint, using the same modes/*.md evaluation logic.

Google has transitioned consumer Gemini CLI access to Antigravity CLI. GEMINI.md is now a no-op compatibility guard so Antigravity does not duplicate the full project instructions when it reads both AGENTS.md and GEMINI.md.

Native Antigravity CLI

# 1. Run in the career-ops directory
cd career-ops
agy

# 2. Use the unified /career-ops command with subcommands:
/career-ops "Senior AI Engineer at Anthropic..."
/career-ops pipeline
/career-ops scan
/career-ops pdf
/career-ops tracker

The skill is defined using the open standard in .agents/skills/career-ops/SKILL.md and symlinked/referenced for each supported CLI (e.g. .claude/, .cursor/, .qwen/, .antigravitycli/, .grok/).

Codex Integration

career-ops supports Codex through the same shared router, but the invocation model is different from CLIs that auto-register slash commands. For the full guide, see docs/CODEX.md.

Interactive Codex

cd career-ops
codex

Slash commands are not guaranteed in Codex. If /career-ops is unavailable, ask Codex to run the mode directly in plain language:

Evaluate this JD with career-ops auto-pipeline: https://company.com/jobs/123
Run the career-ops scan mode and summarize new matches.
Run the career-ops pipeline mode for data/pipeline.md.
Run the career-ops pdf mode for the latest evaluated role.
Run the career-ops tracker mode and summarize the current statuses.

One-shot Codex (codex exec)

codex exec "Evaluate this JD with career-ops auto-pipeline: https://company.com/jobs/123"
codex exec "Run career-ops scan mode in this repo and summarize new matches."
codex exec "Run career-ops pipeline mode for data/pipeline.md."
codex exec "Run career-ops pdf mode for the latest evaluated role."
codex exec "Run career-ops tracker mode and summarize the current statuses."

Grok Build CLI Integration

career-ops supports Grok Build CLI natively, the same way it supports Claude Code and OpenCode. AGENTS.md is auto-loaded as project rules, and all slash commands are available through the shared skill entrypoint.

Native Grok Build CLI

# 1. Run in the career-ops directory
cd career-ops
grok

# 2. Use the unified /career-ops command with subcommands:
/career-ops "Senior AI Engineer at Anthropic..."
/career-ops pipeline
/career-ops scan
/career-ops pdf
/career-ops tracker

For headless batch workers, use grok -p "prompt" (add --yolo to auto-approve tool executions).

Standalone Gemini API Script (No CLI install needed)

# 1. Get a free API key at https://aistudio.google.com/apikey
cp .env.example .env
# Edit .env, set GEMINI_API_KEY=your_key_here

# 2. Install dependencies
npm install

# 3. Evaluate a job description
node gemini-eval.mjs "We are looking for a Senior AI Engineer..."
node gemini-eval.mjs --file ./jds/my-job.txt
node agent-inbox.mjs add "..."   # queue a request for the next session
npm run gemini:eval -- "JD text here"

Free tier: Both options work without billing. Native CLI uses Google OAuth; the API script uses gemini-3.6-flash (rate limits are model- and tier-dependent; see Google AI docs for current quotas).

Pre-configured Portals

The scanner comes with 100+ companies ready to scan and 45+ search queries across major job boards. Copy templates/portals.example.yml to portals.yml and add your own:

AI Labs: Anthropic, OpenAI, Mistral, Cohere, LangChain, Pinecone Voice AI: ElevenLabs, PolyAI, Parloa, Hume AI, Deepgram, Vapi, Bland AI AI Platforms: Retool, Airtable, Vercel, Temporal, Glean, Arize AI Contact Center: Ada, LivePerson, Sierra, Decagon, Talkdesk, Genesys Enterprise: Salesforce, Twilio, Gong, Dialpad LLMOps: Langfuse, Weights & Biases, Lindy, Cognigy, Speechmatics Automation: n8n, Zapier, Make.com European: Factorial, Attio, Tinybird, Clarity AI, Travelperk

Job boards searched: 55+ provider modules cover ATS APIs, board-wide feeds, XML/RSS feeds, markdown feeds, and local parsers. See Supported job boards for the full table.

By default node scan.mjs (a.k.a. npm run scan) trusts what each ATS feed returns. Some companies leave stale postings in their public API even after the role is closed, so those expired entries can leak into pipeline.md. Pass --verify to launch Playwright after the API pass and drop expired postings before they hit the pipeline:

node scan.mjs --verify          # zero-token discovery + Playwright liveness check

The verification is sequential and only runs against new offers (after dedup), so the cost stays bounded.

Dashboard TUI

The built-in terminal dashboard lets you browse your pipeline visually:

npm run serve:dashboard   # launch the TUI
npm run build:dashboard   # optional: build the standalone binary

Features: 6 filter tabs, 4 sort modes, grouped/flat view, lazy-loaded previews, inline status changes.

There is also an experimental web UI (alpha, opt-in — nothing runs unless you start it): see web/README.md.

Project Structure

career-ops/
├── AGENTS.md                    # Canonical agent instructions (all CLIs)
├── CLAUDE.md                    # Claude Code wrapper (imports AGENTS.md)
├── CODEX.md                     # Codex wrapper (imports AGENTS.md)
├── OPENCODE.md                  # OpenCode wrapper (imports AGENTS.md)
├── GEMINI.md                    # Legacy no-op guard to avoid Antigravity duplicate context
├── cv.md                        # Your CV (create this)
├── article-digest.md            # Your proof points (optional)
├── config/
│   └── profile.example.yml      # Template for your profile
├── modes/                       # Skill modes
│   ├── _shared.md               # Shared context (customize this)
│   ├── oferta.md                # Single evaluation
│   ├── pdf.md                   # PDF generation
│   ├── cover.md                 # Cover letter generation
│   ├── email.md                 # Formal application email drafts
│   ├── scan.md                  # Portal scanner
│   ├── batch.md                 # Batch processing
│   └── ...
├── templates/
│   ├── cv-template.html         # ATS-optimized CV template
│   ├── portals.example.yml      # Scanner config template
│   └── states.yml               # Canonical statuses
├── batch/
│   ├── batch-prompt.md          # Self-contained worker prompt
│   └── batch-runner.sh          # Orchestrator script
├── dashboard/                   # Go TUI pipeline viewer
├── data/                        # Your tracking data (gitignored)
├── reports/                     # Evaluation reports (gitignored)
├── output/                      # Generated PDFs (gitignored)
├── fonts/                       # Space Grotesk + DM Sans
├── docs/                        # Setup, customization, budget guide, architecture
└── examples/                    # Sample CV, report, proof points

External Data Directory (Optional)

By default, user-layer data (such as cv.md, portals.yml, and data/ / reports/ / output/ folders) lives inside the project root folder.

To separate your personal data from the code (making it easier to switch branches, pull updates, or test multiple profiles), you can configure an external data directory using the following precedence:

1. Environment Variables: Set the CAREER_OPS_ROOT or CAREER_OPS_DATA_DIR environment variable:

   export CAREER_OPS_ROOT=~/my-career-data
   

2. Marker File: Create a .career-ops-data file in the repository root containing the path to your data directory.
3. Default: Defaults to the repository root.

Once resolved, all user files are resolved and written relative to that folder, while prompt files and scripts continue to resolve relative to the repository.

• Tracker Override: You can also set CAREER_OPS_TRACKER to override the applications tracker file path directly.
• Writes: All write operations (such as merges) canonically target {DATA_ROOT}/data/applications.md.

The Go dashboard TUI, Node.js scripts, and AI agent modes all automatically respect this resolution hierarchy.

Tech Stack

• Agent: AI coding CLI with shared skills and modes (AGENTS.md + CLI wrapper)
• PDF: Playwright + HTML template
• Cover letters: HTML template + Playwright (A4 PDF, same pipeline as CVs)
• Scanner: Playwright + Greenhouse API + WebSearch
• Dashboard: Go + Bubble Tea + Lipgloss (Catppuccin Mocha theme)
• Data: Markdown tables + YAML config + TSV batch files

_{Also runs on any agent-skill-standard CLI. See Supported CLIs.}

Also Open Source

• cv-santiago -- The portfolio website (santifer.io) with AI chatbot, LLMOps dashboard, and case studies. If you need a portfolio to showcase alongside your job search, fork it and make it yours.

FAQ

What is career-ops? career-ops (career-ops.org, also known as careerops) is an open-source AI job search that runs locally in your AI coding CLI (Claude Code, Codex, OpenCode and others) and leaves every decision to you. It evaluates job offers against your CV, generates ATS-tailored PDFs, finds the right person to contact, and tracks everything in one place: you always have the final call. It is the first reference implementation of the CareerOps Manifesto.

Can the tailored CV invent things? It must not, and the prompts say so: reformulate, never fabricate. That rule is not yet enforced by a gate in code. Two open issues track it: #2677 (job titles must match cv.md) and #1411 (fail-closed faithfulness check). Until they merge, read every CV before you send it. The Legal Disclaimer says the same in longer words.

Can I run career-ops for free, or on a cheaper / local model? Yes. career-ops is CLI-agnostic and runs on free and local models — via OpenRouter free models, Ollama, or any OpenAI-compatible endpoint — so you are not tied to a paid subscription. See docs/RUNNING_ON_A_BUDGET.md for the full setup.

I pay for Claude Pro/Max but career-ops is burning API credits. Why? Because an ANTHROPIC_API_KEY in your environment takes precedence over your logged-in subscription: the CLI uses the key and bills per token. Run echo $ANTHROPIC_API_KEY, and if it prints anything, remove it from your shell profile, restart the terminal and run /login. Batch mode is the exception, since claude -p workers do not use the interactive login: run claude setup-token once and export the result as CLAUDE_CODE_OAUTH_TOKEN. Full walkthrough in docs/RUNNING_ON_A_BUDGET.md.

Which AI CLIs does career-ops work with? career-ops runs on any major AI coding CLI — Claude Code, Codex, Gemini / Antigravity, OpenCode, Grok, Qwen and more — through the open Agent Skill Standard, so it is never locked to a single vendor. Use the CLI you already have.

How do I install career-ops on Windows? career-ops runs on Windows. Platform-specific setup and the known sharp edges — Git Bash discovery, line endings, Task Scheduler — are in docs/WINDOWS.md. If skills fail to load with a symlink error during install, the fix is in docs/FAQ.md. Full steps are in docs/SETUP.md.

Does career-ops auto-apply to jobs for me? No. career-ops is a filter, not a spray-and-pray auto-applier. The AI evaluates, ranks and drafts; you review and decide. It never submits, sends, or clicks anything — you always have the final call. That human-in-the-loop design is the whole point.

Is career-ops free and open source? Yes. career-ops is free and open source, and for the candidate it always will be — it is the first reference implementation of the CareerOps Manifesto. Read it, and if it says what you believe, sign it.

About the Author

I'm Santiago Fernández de Valderrama Aparicio (santifer) -- Head of Applied AI, former founder (built and sold a business that still runs with my name on it). I built career-ops to manage my own job search. It worked: I used it to land my current role.

Curious how this repo is maintained in ~4 hours a week? Read Agentic maintenance: how career-ops is run by a fleet of AI agents.

My portfolio and other open source projects → santifer.io

Wikidata: Santiago Fernández de Valderrama Aparicio · career-ops.

Disclaimer

career-ops is a local, open-source tool, NOT a hosted service. By using this software, you acknowledge:

1. You control your data. Your CV, contact info, and personal data stay on your machine and are sent directly to the AI provider you choose (Anthropic, OpenAI, etc.). We do not collect, store, or have access to any of your data.
2. You control the AI. The default prompts instruct the AI not to auto-submit applications, but AI models can behave unpredictably. If you modify the prompts or use different models, you do so at your own risk. Always review AI-generated content for accuracy before submitting.
3. You comply with third-party ToS. You must use this tool in accordance with the Terms of Service of the career portals you interact with (Greenhouse, Lever, Workday, LinkedIn, etc.). Do not use this tool to spam employers or overwhelm ATS systems.
4. No guarantees. Evaluations are recommendations, not truth. AI models may hallucinate skills or experience. The authors are not liable for employment outcomes, rejected applications, account restrictions, or any other consequences.

See LEGAL_DISCLAIMER.md for full details. This software is provided under the MIT License "as is", without warranty of any kind.

Contributors

Every person who has shipped code, docs, translations or tests is listed in CONTRIBUTORS.md — including non-code contributions, which the graph above cannot show.

Got hired using career-ops? Share your story!

License & Trademark

The code is licensed under MIT. The "career-ops" name and brand are governed by the Trademark Policy, permissive for community use, reserved for commercial product naming and endorsement.

Star history

_{Created and maintained by Santiago Fernández de Valderrama Aparicio (@santifer)}

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