applyr

CLI job application tracker designed for AI coding agents.

Track applications, measure your conversion funnel, spot skill gaps, and generate ATS-optimized CVs — all from your terminal. Built to work with Claude Code, Cursor, Aider, or any AI coding agent.

Why applyr?

I built this while applying to 200+ jobs. Most job trackers are web apps that don't talk to your AI tools. applyr is different:

CLI-first — runs in your terminal, pipes into anything
AI-agent native — your coding agent analyzes offers, scores compatibility, and generates CVs
ATS-safe CVs — locked CSS template that passes Applicant Tracking Systems. Your agent fills content, never touches the structure
Zero dependencies — Python 3.12+ stdlib only. No frameworks, no API keys, no subscriptions
Local and private — your data stays in a SQLite file on your machine

How it works

1. You paste a job offer into your AI agent
2. The agent reads your cv-master.md (your complete professional profile)
3. The agent evaluates your compatibility per topic (tech stack, experience, etc.)
4. The agent runs `applyr add` to register the offer with all data
5. If you want to apply, the agent generates an ATS-safe CV tailored to the offer
6. You track everything: pipeline, stats, follow-ups, skill gaps, trends

applyr is the storage and structure layer. Your AI agent is the brain that analyzes and decides.

Install

pip install applyr

Or clone and install locally:

git clone https://github.com/DeibyGS/applyr.git
cd applyr
pip install .

Setup (3 steps)

Step 1 — Initialize

applyr init

This creates ~/.applyr/ with:

applyr.toml — configuration (scoring weights, thresholds, paths)
jobs.db — SQLite database (empty, ready to use)
cv-master.md — template for your professional profile
AGENT_INSTRUCTIONS.md — step-by-step guide for your AI agent
cv/ — directory for generated CVs

Step 2 — Fill your CV master

Open ~/.applyr/cv-master.md and fill it with your complete professional profile: contact info, experience, projects, skills, education, certifications, languages. This is the source of truth — the AI agent reads this file to evaluate offers and generate CVs. Never leave it empty.

Step 3 — Connect your AI agent

Copy the agent instructions into your AI tool's config:

# Claude Code
cat ~/.applyr/AGENT_INSTRUCTIONS.md >> ~/.claude/CLAUDE.md

# Cursor
cat ~/.applyr/AGENT_INSTRUCTIONS.md >> .cursorrules

# Aider / OpenCode / others
# Add the content to whatever file your agent reads for instructions

This tells your AI agent:

How to read your cv-master.md and evaluate offers
How to build the JSON for applyr add with all valid field values
Which command to run for each user question
Rules: never invent content, be honest with scores, leave unknown fields empty
ATS rules for CV generation (single column, standard fonts, visible URLs, etc.)

That's it. You're ready.

Usage

Register an offer

Paste a job posting into your AI agent and say "analyze this offer". The agent will:

Read your cv-master.md
Score each topic (tech stack, experience, education, etc.)
Run applyr add with all the data:

applyr add '{"title": "AI Engineer", "company": "Acme Corp", "work_mode": "remote", "location": "Madrid", "salary_min": 30000, "salary_max": 40000, "seniority_level": "junior", "role_category": "ai", "tech_stack": "Python, LangChain, AWS", "canal": "linkedin_easy", "status": "applied", "topics": {"tech_stack": {"score": 85, "detail": "Python strong"}, "education": {"score": 70, "detail": "DAM completed"}, "english": {"score": 60, "detail": "B1"}, "experience": {"score": 40, "detail": "6mo internship"}, "projects": {"score": 90, "detail": "3 production projects"}, "cultural_fit": {"score": 80, "detail": "Good fit"}}}'

Output:

Offer added successfully.
  ID          : 1
  Title       : AI Engineer
  Company     : Acme Corp
  Compat.     : 74%
  Status      : Applied
  Follow-up   : 2026-08-16
  Skill gaps  : English, Experience

All fields are optional except title. The agent fills what it can from the posting.

View your offers

applyr list                    # All offers (last 50)
applyr list --status applied   # Filter by status
applyr show 1                  # Full detail of offer #1
applyr pipeline                # Grouped by status

Track your progress

applyr stats                   # Conversion funnel + metrics
applyr gaps                    # Skills you need to improve
applyr followups               # Overdue and upcoming follow-ups
applyr trends                  # Applications per week + growth rate
applyr summary --json          # Weekly summary as structured JSON

Update and manage

applyr update 1 waiting --notes "Interview scheduled for Monday"
applyr update 1 rejected --notes "They needed 3+ years experience"
applyr search Python           # Search by company/title/tech/notes
applyr delete 5                # Remove an offer
applyr export --format json    # Export everything

Generate ATS-safe CVs

applyr cv generate 1           # Creates HTML skeleton for offer #1

This generates an HTML file with:

Locked ATS-safe CSS — single column, standard fonts, no flex/grid/tables
Offer context — company, title, tech stack, scores embedded as comments
Placeholders — for the AI agent to fill from your cv-master.md

The agent then fills the placeholders and you convert to PDF:

applyr cv pdf ~/.applyr/cv/cv-acme-ai-engineer.html

The PDF is generated with Chrome headless, no headers or footers.

All commands

Command	Description
`applyr init`	Set up ~/.applyr/ (config, database, agent instructions)
`applyr add '<json>'`	Register a new job offer
`applyr list [--status S] [--sort F]`	List offers (default: last 50)
`applyr pipeline [--min-score N]`	View offers grouped by status
`applyr show <id>`	Show full offer details with topic scores
`applyr update <id> <status> [--notes ""]`	Update offer status
`applyr delete <id>`	Delete an offer
`applyr search <keyword> [--status S]`	Search by company/title/notes/tech
`applyr stats`	Conversion funnel, channels, salary, work mode
`applyr gaps [--limit N]`	Skill gap analysis by frequency
`applyr followups`	Pending/overdue follow-ups with contact info
`applyr trends [--period week\|month]`	Application trends over time
`applyr summary [--json]`	Weekly summary (JSON for LLM consumption)
`applyr export [--format csv\|json]`	Export all data
`applyr cv generate <id>`	Generate ATS-safe HTML CV skeleton
`applyr cv pdf <file.html> [--output f.pdf]`	HTML to PDF via Chrome
`applyr version`	Show version
`applyr help`	Show help

Offer fields reference

Field	Type	Valid values	Required
`title`	string	Any	Yes
`company`	string	Any	No
`summary`	string	Any	No
`date_received`	string	`YYYY-MM-DD`	No
`date_applied`	string	`YYYY-MM-DD`	No
`status`	string	`pending`, `applied`, `waiting`, `in_process`, `rejected`, `discarded`, `offer`	No
`canal`	string	`linkedin_easy`, `linkedin_direct`, `email`, `portal`, `referral`, `other`	No
`work_mode`	string	`remote`, `hybrid`, `onsite`	No
`location`	string	Any	No
`salary_min`	integer	Annual EUR	No
`salary_max`	integer	Annual EUR	No
`salary_period`	string	`annual`, `monthly`	No
`seniority_level`	string	`junior`, `mid`, `senior`, `lead`, `director`	No
`role_category`	string	`backend`, `frontend`, `fullstack`, `ai`, `devops`, `data`, `mobile`, `qa`, `other`	No
`tech_stack`	string	Comma-separated	No
`cover_letter`	integer	`0` or `1`	No
`cover_letter_file`	string	File path	No
`contact_name`	string	Any	No
`contact_role`	string	Any	No
`job_url`	string	URL	No
`rejection_reason`	string	Any	No
`notes`	string	Any	No
`topics`	object	See Scoring section	No

Scoring

When you provide topics in applyr add, the compatibility score is auto-calculated using weighted averages:

Topic	Default Weight	What to evaluate
`tech_stack`	30%	How much of the required tech does the user know?
`education`	15%	Does the education match what they ask?
`english`	10%	Does the language level meet the requirement?
`experience`	15%	Years, seniority, and industry match?
`projects`	20%	Are the user's projects relevant to this role?
`cultural_fit`	10%	Work mode, company culture, location match?

Each topic score goes from 0 to 100. The weighted average becomes the compatibility percentage.

Default threshold to recommend applying: 65% (configurable).

Customize weights, topic names, and threshold in ~/.applyr/applyr.toml.

Status flow

pending ──> applied ──> waiting ──> in_process ──> offer
               |            |           |
               v            v           v
           discarded    rejected    rejected

pending — offer registered, not yet applied
applied — application sent (auto-schedules follow-up)
waiting — waiting for company response
in_process — interview stage
offer — offer received
discarded — decided not to apply
rejected — company rejected your application

Configuration

Edit ~/.applyr/applyr.toml:

[general]
threshold = 65          # Min compatibility % to recommend applying
followup_days = 10      # Days before follow-up reminder

[weights]
# Relative importance of each topic (auto-normalized, no need to sum to 1.0)
tech_stack = 30
education = 15
experience = 15
projects = 20
english = 10
cultural_fit = 10

[cv]
# cv_master = "~/.applyr/cv-master.md"
# output_dir = "~/.applyr/cv"

Data storage

All data is stored locally in ~/.applyr/jobs.db (SQLite). Nothing leaves your machine.

Export anytime:

applyr export --format json --file my-applications.json
applyr export --format csv --file my-applications.csv

Example conversation with your AI agent

You:   "Analyze this job posting for AI Engineer at Acme"
Agent: Reads the posting + your cv-master.md
       Evaluates compatibility per topic
       Runs: applyr add '<json with all fields>'
       → "Registered as #42 — 78% match. Gaps: English, Experience"

You:   "Apply to it"
Agent: Runs: applyr update 42 applied --canal linkedin_easy
       Runs: applyr cv generate 42
       Fills placeholders from cv-master.md
       Runs: applyr cv pdf ~/.applyr/cv/cv-acme-ai-engineer.html
       → "CV generated. PDF ready at ~/.applyr/cv/cv-acme-ai-engineer.pdf"

You:   "What skills should I focus on improving?"
Agent: Runs: applyr gaps
       → "Experience appears in 15 offers (avg gap 20%). English in 12 offers."

You:   "How am I doing this month?"
Agent: Runs: applyr summary --json
       → Structured JSON with applications sent, response rate, trends

You:   "Any follow-ups due?"
Agent: Runs: applyr followups
       → "3 overdue: Acme (#42, 5 days ago), Beta (#38, 3 days ago)..."

License

MIT

Name		Name	Last commit message	Last commit date
Latest commit History 17 Commits
.github/workflows		.github/workflows
applyr		applyr
templates		templates
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
applyr.toml.example		applyr.toml.example
pyproject.toml		pyproject.toml

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applyr

Why applyr?

How it works

Install

Setup (3 steps)

Step 1 — Initialize

Step 2 — Fill your CV master

Step 3 — Connect your AI agent

Usage

Register an offer

View your offers

Track your progress

Update and manage

Generate ATS-safe CVs

All commands

Offer fields reference

Scoring

Status flow

Configuration

Data storage

Example conversation with your AI agent

License

About

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Repository files navigation

applyr

Why applyr?

How it works

Install

Setup (3 steps)

Step 1 — Initialize

Step 2 — Fill your CV master

Step 3 — Connect your AI agent

Usage

Register an offer

View your offers

Track your progress

Update and manage

Generate ATS-safe CVs

All commands

Offer fields reference

Scoring

Status flow

Configuration

Data storage

Example conversation with your AI agent

License

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