CV & Application Generator

What it does: You send a job posting link — the system reads it, picks the right CV, writes a personalised cover letter, and delivers two ready-to-send PDFs.

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First-Time Setup

Step 1 — Install dependencies (run once)

pip install -r requirements.txt

Step 2 — Create your personal profile

cp master_cv.example.json master_cv.json

Edit master_cv.json with your real name, contact info, work history, and skills. This file is private and gitignored — it never gets committed.

Step 3 — Add your API keys

cp .env.example .env

Open .env and fill in:

• CV_OWNER_SLUG — your name slug used in all output filenames (e.g. Firstname_LASTNAME)
• OPENAI_API_KEY — from platform.openai.com
• TELEGRAM_BOT_TOKEN and TELEGRAM_CHAT_ID — only needed for the Telegram bot (see docs/bot-setup.md)
• LINKEDIN_CLIENT_ID / LINKEDIN_CLIENT_SECRET — only needed for LinkedIn posting (see LinkedIn Content section below)

Step 4 — Verify everything is working

uv run main.py test

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Generating an Application

Quickstart (minimal)

If you want to try the tool quickly, run these commands from the repository root:

# 1) Install Python deps

pip install -r requirements.txt

# 2) Copy env and set your OpenAI key

cp .env.example .env
# Edit .env and set OPENAI_API_KEY (and TELEGRAM_* if using the bot)

# 3) Create the internal profile (accepts .tex, .pdf, .jpg/.png)
Examples:

uv run main.py init-profile --cv templates/lato/CV_AI_Data_Lato.tex
uv run main.py init-profile --cv path/to/my_cv.pdf
uv run main.py init-profile --cv path/to/photo_of_cv.jpg

# 4) Generate an application from a job URL (optional: pass --cv to override)

uv run main.py apply https://company.com/jobs/12345
uv run main.py apply https://company.com/jobs/12345 --cv path/to/my_cv.pdf

# 5) Or start the Telegram bot and use /apply from your phone

uv run main.py bot

# 6) Quick health check

uv run main.py test

Notes:

• The --cv flag for init-profile and apply accepts .tex, .pdf, and common image formats (.jpg, .jpeg, .png, .webp).
• PDF extraction and image OCR require additional system/software:
  • pdfminer.six, pytesseract, and Pillow are Python dependencies (in requirements.txt).
  • tesseract OCR engine must be installed on your system for OCR to work. On macOS: brew install tesseract.
• If you only use a LaTeX .tex source, you do NOT need tesseract or the OCR Python packages — the .tex path works without extra system deps.
• The --template flag for apply supports altacv (default) and lato.

Option A — Telegram (easiest)

Start the bot:

uv run main.py bot

Then in Telegram, send:

/apply https://company.com/jobs/your-job-link

The bot will:

1. Read the job posting
2. Generate a tailored CV + cover letter (takes ~30 seconds)
3. Send you both PDFs to review
4. Ask Approve or Reject
   • Approve → saves both files to your private archive channel
   • Reject → nothing is saved

Option B — Terminal

uv run main.py apply https://company.com/jobs/your-job-link

The two PDFs are saved in the Applied/ folder.

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Where Are My Files?

What	Where
Generated applications (CV + cover letter per job)	Applied/YYYY-MM-DD_Company_Role/
Spontaneous applications (no company)	Applied/YYYY-MM-DD_Spontannee_Role_lang/

Output Filenames: All generated CVs and cover letters follow the standardized naming pattern:

{CV_OWNER_SLUG}-{DocumentType}_{Role}_{Language}.{ext}

The slug is set via CV_OWNER_SLUG in your .env file. Examples:

• Firstname_LASTNAME-CV_IT_fr.pdf — IT infrastructure CV (French)
• Firstname_LASTNAME-LettreMotivation_AI_fr.pdf — AI cover letter (French)
• Firstname_LASTNAME-CV_PhD_en.pdf — PhD application CV (English)

This naming convention makes it easy to identify document type and role at a glance.

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Updating Your CVs

Change your contact info (name, email, phone, location)

Edit one file only:

templates/shared/personal_data.tex

All CVs pull from this file — you never need to update the same detail in multiple places.

Change CV content (work experience, skills, etc.)

Templates are organized by style family:

templates/altacv/ — AltaCV style (xelatex), used for spontaneous applications:

File	Use for
CV_AI_MLOps_fr.tex	AI / MLOps roles (French)
CV_AI_MLOps_en.tex	AI / MLOps roles (English)
CV_DevOps_Alternance_fr.tex	DevOps alternance (French)
CV_Polyvalent_fr.tex	Polyvalent / interim agency (French)

templates/lato/ — Lato/article style (pdflatex):

File	Use for
CV_AI_Data_Lato.tex	AI / Data Science / Python roles (English)
CV_IT_Infra_Lato.tex	IT Support / Network roles (French)
CV_PhD_Research_en.tex	PhD / Research applications (English)

templates/classic/ — ModernCV banking style (pdflatex), 16 role variants.

After editing a lato or classic template, rebuild the PDF:

./compile.sh ai    # CV_AI_Data_Lato
./compile.sh it    # CV_IT_Infra_Lato
./compile.sh phd   # CV_PhD_Research_en
./compile.sh all   # rebuild all CV_*.tex across all template folders

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Spontaneous Applications

Generate a pre-written CV without LLM — no job URL needed:

uv run main.py spontaneous ai                    # AI / MLOps (French)
uv run main.py spontaneous ai-en                 # AI / MLOps (English)
uv run main.py spontaneous mlops                 # MLOps (French)
uv run main.py spontaneous mlops-en              # MLOps (English)
uv run main.py spontaneous devops                # DevOps (French)
uv run main.py spontaneous devops-alternance     # DevOps alternance (French)
uv run main.py spontaneous phd                   # PhD / Research (English)
uv run main.py spontaneous polyvalent            # Polyvalent / interim (French)

# Add --city to select Montpellier vs Grenoble automatically:
uv run main.py spontaneous ai --city montpellier

Output goes to Applied/YYYY-MM-DD_Spontannee_{role}_{lang}/.

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Updating Your Cover Letter

Open the relevant template in cover_letters/:

File	Language
Cover_Letter_Template_Fr.tex	French
Cover_Letter_Template_En.tex	English

Only edit the stock paragraphs (the text that describes your experience). The four personalisation variables at the top (\CompanyName, \PositionTitle, etc.) are filled automatically for each application — do not touch them.

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LinkedIn Content

This project includes a full pipeline for creating and publishing LinkedIn data-analysis carousel posts.

Folder structure

linkedin/
├── 01_IT_job_market/     # Published: French IT job market (15 cities)
│   ├── carousel.md       # Source Markdown for the carousel
│   ├── carousel.pdf      # Generated PDF (gitignored)
│   └── post.txt          # Post title + text (TITRE DU DOCUMENT on line 1, text after ---)
├── 02_data_science/      # Published: Data jobs in France — offers vs candidates
│   ├── carousel.md
│   ├── carousel.pdf
│   └── post.txt
└── idees_posts.md        # Post backlog and ideas (private — gitignored)

Step 1 — Generate the carousel PDF

# From the repo root:
amir pdf --theme carousel linkedin/02_data_science/carousel.md -o linkedin/02_data_science/carousel.pdf

Requires the amir-cli tool with the carousel theme installed. Install the theme once: bash docs/install-carousel-theme.sh

Step 2 — Authenticate with LinkedIn (one-time)

1. Go to developer.linkedin.com → Create App
2. Add products: "Share on LinkedIn" + "Sign In with LinkedIn using OpenID Connect"
3. Set redirect URI to http://localhost:8765/callback
4. Copy Client ID and Secret into .env (LINKEDIN_CLIENT_ID, LINKEDIN_CLIENT_SECRET)
5. Run the OAuth flow:

python scripts/linkedin_post.py --auth

A browser window opens. Approve. Token saved to .linkedin_token.json (gitignored).

Step 3 — Preview before posting

python scripts/linkedin_post.py --post linkedin/02_data_science --dry-run

Prints the title, first 120 chars of text, and PDF path — no API calls made.

Step 4 — Post

python scripts/linkedin_post.py --post linkedin/02_data_science

Uploads the PDF and publishes the post. Prints the post URL when done.

Posting frequency rules

• Maximum 2 posts per week, minimum 3 days between carousel/analytical posts
• Best days: Monday/Tuesday or Wednesday
• Avoid Friday afternoon, Saturday, Sunday

Before posting, always check the date of the last post:

# Check last post date
import json, requests
from datetime import datetime, timezone
from pathlib import Path

token_data = json.loads(Path(".linkedin_token.json").read_text())
token = token_data["access_token"]
owner_urn = f"urn:li:person:{token_data['sub']}"
headers = {"Authorization": f"Bearer {token}", "LinkedIn-Version": "202604",
           "X-Restli-Protocol-Version": "2.0.0"}
r = requests.get(
    f"https://api.linkedin.com/rest/posts?author={owner_urn}&q=author&count=5&sortBy=LAST_MODIFIED",
    headers=headers, timeout=10)
ts = r.json()["elements"][0].get("publishedAt")
last = datetime.fromtimestamp(ts/1000, tz=timezone.utc)
print(f"Last post: {last.strftime('%A %d %B %Y')}")

API notes

• LinkedIn-Version header: Calendar versioning (YYYYMM). Current working version: 202604 (update if you see a 426 NONEXISTENT_VERSION error)
• Document post payload: content.media requires "id": document_urn (not "document":)

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Data Scraping (France Travail)

Scrape job-market stats for data roles

node scripts/data_jobs_scraper.mjs

Reads ROME codes from docs/it_rome_codes.json, visits candidat.francetravail.fr/metierscope/fiche-metier/{CODE}/ for each role, extracts national offer count and candidate count, saves to docs/data_jobs_stats.json.

Key ROME codes for data jobs:

Code	Métier
M1405	Data Scientist
M1419	Data Analyst
M1811	Data Engineer
M1889	Ingénieur IA
M1872	Consultant BI

Note: France Travail uses non-breaking spaces as thousands separators ("3 830"). The scraper strips all non-digit characters before parsing numbers.

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Technical Documentation

For developers, AI agents, or anyone who wants to understand the internals:

Topic	File
How the pipeline works (scraping, LLM, compilation)	docs/architecture.md
LaTeX macros, template structure, adding new styles	docs/latex-templates.md
Telegram bot setup, AUTO_APPLY, source files	docs/bot-setup.md
Git workflow, commit conventions, tracked files	docs/git-workflow.md

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Contact

Contact details are in templates/shared/personal_data.tex (private, not committed).