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Stop rewriting your resume for every job.

Maestro tailors it from your whole career history in under a minute, and every bullet is something you actually did, so you can defend it in an interview.

• Same score, every time. The match score runs on fixed rules and a small model on your own computer, with no AI service involved. Same resume, same job, same number.
• Every AI edit is a diff. Suggested changes arrive as a before and after. You accept or undo them one by one.
• It fills the form and stops there. The Companion fills job applications from your saved answers and attaches your resume. It never submits. That click is yours. With full automation on, Apply can also click Next through the pages for you and stops on the review page. On known application sites it signs in, or makes the account, with the job-site login you saved.

Install in two commands, or follow the Getting Started guide if this is all new to you.

<!-- hero.gif: career-history animation, approved by the owner 2026-10-12 for the launch. All data is the fictional Jordan Ellis persona (example.com email, 555 phone, sample-company employers, target "Woodgrove Bank"). The resume pages, the 82.4 -> 87.3 Match score and the Apache Spark gap card are real captures from the app (v0.10.1) on a throwaway stack; tailoring took 4.6 s. Source, captures and the encode script live in the private marketing repo (hero-gif/; the Figtree captions are hero-site-figtree/, re-rendered 2026-10-16 to match the site). 1000px / 12fps / 32 colors, no dither, 1.86 MB; keep any replacement under 2 MB. -->

Give your AI your career

Connect Claude, Codex or the ChatGPT desktop app and your assistant gets your career history, your resumes and your jobs. Then just ask.

| | You ask | What happens | |---|---|---| | Extract | "Here's a posting. Should I even apply?" | It saves the job, scores your resumes against it and flags knock-outs like visa or years of experience. | | Maintain | "I shipped the fraud model this quarter. Add it." | A new bullet, as a draft you approve, that every resume can use. | | Maintain | "Make me a resume for fintech ML roles in the UK." | A specialist resume built from your history, targeted to that role and country. | | Hunt | "Every morning, find new ML roles that fit and file the good ones." | The job-hunt skill files them to your Agent inbox. You queue or skip each one. | | Hunt | "Which skills keep showing up that I don't have?" | The gaps across every job you saved, most frequent first. | | Apply | "Tailor this one, walk me through the gaps, then make the PDF." | It asks about each gap instead of inventing, keeps every change as a diff you can undo and renders the PDF. | | Apply | "Fill the applications I queued. Show me each one before it goes." | Nothing is submitted without your yes, unless you turn on full automation. |

That is 99 tools and ready-made skills for a daily hunt, an apply run and email status: see the MCP guide.

Free and open source. Your career history lives on your laptop, not in someone else's cloud, and there is no account. Bring an OpenAI, Gemini or Claude key (scoring, PDFs and tracking work without one), get real LaTeX and Typst PDFs, and take everything with you in one career.md file.

Made for one person on one computer. There is no login, so anything that can reach the app can read and change your whole career record and your saved API keys. Out of the box it only listens on your own machine (127.0.0.1). Never expose the app to a network. The one published path is /api/sync, sealed, and only as sync setup describes. SECURITY.md has the details. Your career record stays on your computer. When you use AI features, resume and job information goes to the AI service you choose.

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Why it works this way

If your resume folder has a v2finalcopy(3) in it, this is for you. Maestro keeps one record of what you have actually done, and every resume is a view of that record. Approved bullets go into a resume word for word, and rewording one is a separate step that asks you first. When a job asks for something your resume does not show, Maestro asks you about it. If it is true, it is saved for every future application. If it is not, it stays a gap.

The score is fixed rules, so the same resume and job always get the same number. It is not an employer's score or a prediction of an interview, so use it to compare your own drafts. Saving a job and tailoring for it took about 10 seconds on Gemini and about 40 on the OpenAI default in our tests, at about $0.01 (OpenAI) or under $0.03 (Gemini) per application (the numbers).

Nothing here is rented. It is free and open source (Apache 2.0), with no account, and you pay only for the AI you use. Your career has one history. Your resume should too.

The long version, with the score, tailoring, templates and the rest of the toolkit, is in Using Maestro CS.

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Quickstart

Install uv once. After that, Maestro is two commands: no Docker, no Git, no clone. uv installs Maestro as a single maestro command and fetches the Python it needs, and the package brings its own web app and its own Node. It runs on macOS, Linux and Windows (x64).

# 1. Install uv once, then open a new terminal.
curl -LsSf https://astral.sh/uv/install.sh | sh     # macOS and Linux

# On Windows, in PowerShell instead of the line above:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

# 2. Install Maestro, then start it.
uv tool install --python 3.12 maestro-career-studio
maestro start

maestro start runs Maestro in the background and opens http://localhost:3000. Closing the terminal does not stop it. The first start downloads the small scoring model (about 100 MB, once) from Hugging Face, so allow a minute or two. If the terminal says maestro is not found, run uv tool update-shell and open a new terminal.

Best effort, not tested: Windows on ARM has no PDF-engine (typst) wheel, so install an x64 Python under emulation: uv tool install --python cpython-3.12-windows-x86_64-none maestro-career-studio. Alpine and other musl Linux are not supported by the PDF engine's wheels; use a glibc distro or Docker.

Then:

1. Add your AI key in Settings › Basics. One key from OpenAI, Gemini or Claude is enough. Without a key, scoring, PDFs and tracking still work (what a key costs and does).
2. Import your resumes, save a job, and tailor (step by step).
3. Optional: maestro connect adds Maestro to Claude Desktop, Claude Code, Codex, Cursor or VS Code, and the Companion is a Chrome side panel for saving and filling job pages.

| Command | What it does | |---|---| | maestro start | Start Maestro and open the app (--no-browser, --skip-model) | | maestro stop | Stop it | | maestro status | Whether it is running, its version and whether a newer one exists (--offline skips that check, --json for scripts; exit code 3 means stopped) | | maestro update | Save a snapshot of your data, update, restart (-y skips the question) | | maestro connect [app] | Add Maestro to Claude Desktop, Claude Code, Codex, Cursor or VS Code | | maestro import-docker CHECKOUT | Move your data from a Docker install into this one, once (--dry-run shows the plan; steps) |

The full command table also covers logs, open, restart, autostart, backup, uninstalling and TeX for LaTeX templates. What the command contacts, and when: PRIVACY.md.

Young, but rehearsed. The app runs daily on my machine. A fresh Docker install (clone, start, first tailored resume) has been checked end to end on a second machine, and CI installs and starts the uv package on macOS, Linux and Windows for every change. It has had little testing on machines that aren't mine. If it fails on yours, please open an issue with the log. That report is one of the most valuable contributions there is.

Alternative: run with Docker (self-hosters)

Docker stays the route for contributors and for servers. It needs Docker, Git and about a 1 GB download (roughly 3–4 GB unpacked); the prerequisites have the detail, and on Windows you run it under WSL (steps).

# 1. Download the project and create your settings file
git clone https://github.com/seinun-ai/maestro-career-studio.git
cd maestro-career-studio
cp .env.example .env

# 2. Start the app (downloads about 1 GB the first time)
docker compose up -d

Then open http://localhost:3000 and add your AI key in Settings › Basics. The first start sets up your database and adds a demo resume so you have something to look at. The rest of the Docker route is in the setup reference.

Docker and uv are separate copies. They keep separate data, and both want ports 8001 and 3000, so run one at a time. Moving from Docker to uv? Stop Docker, install Maestro as above, then run maestro import-docker CHECKOUT once (steps). It reads your Docker folder and changes nothing in it.

Working with AI assistants

Driving it from Claude, Codex, or ChatGPT (MCP)

MCP is the connector that lets an AI assistant use the app for you: read a posting, score it, work the gaps, make the PDF, all in conversation, on your computer against your data. All 99 too