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, OpenCode, or any AI coding agent.

Quick start

pip install applyr                        # Install
applyr init                               # Set up config, database, templates
# Edit ~/.applyr/cv-master.md             # Fill in your professional profile
applyr setup-agent --agent claude         # Connect your AI agent

Then paste a job offer into your AI agent — it handles the rest.

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.

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AI Development Benchmark

applyr was designed to work for AI coding agents — it made sense to build it with them, as a pair programming partner. A human engineer defined the domain model and architecture; AI accelerated implementation, always behind human review.

How we worked together

Human-owned	AI implemented, always human-reviewed
Product design & data model (28-column schema)	Python logic generation
Atomic QoL commands design	CLI command scaffolding
ATS CV template structure	Refactoring, test scaffolding
Config (TOML) design	Auxiliary docs, type checking
Code review & final acceptance	Documentation, auxiliary scripts

Workflow: Idea → Spec → AI implementation → Human review → Test → Refine → Merge

The 200+ jobs this tool manages were tracked by an AI agent; the code beneath them was built with the same human-in-the-loop discipline.

AI Development Principles

• AI never made product decisions.
• Every implementation started from a written specification.
• Documentation was treated as executable context for AI.
• All generated code required human review.
• Architecture was preserved over implementation speed.

Supporting metrics

Metric	Value
AI sessions	12 logged (11 on predecessor + applyr)
Measured development time	~2 h tracked; earlier work pre-dates session logs
Primary model	Claude Opus 4.6
Secondary	DeepSeek V4 Flash (OpenCode)

Measured with ClaudeStat. Approximate values; early work was built before exhaustive session logging.

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Install

pip install applyr

Or clone and install locally:

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

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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

Run setup-agent in your project directory:

applyr setup-agent --agent claude     # Claude Code → CLAUDE.md
applyr setup-agent --agent cursor     # Cursor → .cursorrules
applyr setup-agent --agent opencode   # OpenCode → .opencode/instructions.md
applyr setup-agent --agent generic    # Any agent → AGENTS.md

If your project already has an agent config file, setup-agent auto-detects it:

applyr setup-agent                    # Auto-detects and appends 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.

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Usage

Register an offer

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

1. Read your cv-master.md
2. Score each topic (tech stack, experience, education, etc.)
3. 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

Compare, plan, and analyze salaries

applyr compare 1 3 4           # Side-by-side comparison
applyr plan                    # Prioritized learning plan from skill gaps
applyr salary                  # Salary stats by seniority + category
applyr salary --seniority mid  # Filter by seniority level

Example output — applyr compare:

Field         #1                    #3                    #4
----------------------------------------------------------------------
Company       Acme Corp             DataCo                CloudNet
Title         AI Engineer           Junior Python Dev     Backend Engineer
Score         78%                   92%                   65%
Status        Applied               Applied               In Process
Seniority     mid                   junior                mid
Work Mode     remote                onsite                remote
Salary        35000-45000/ann       22000-28000/ann       38000-48000/ann
Tech Stack    Python, LangChain     Python, Django        Go, Kubernetes

Example output — applyr salary:

--- Salary Insights ---

  Seniority       Count       Min       Max       Avg    Median  Period
  ——————————————  —————  ————————  ————————  ————————  ————————  ——————
  junior              1    22,000    28,000    25,000    25,000  annual
  mid                 2    35,000    48,000    41,500    41,500  annual
  senior              1    40,000    55,000    47,500    47,500  annual
  trainee             1    18,000    22,000    20,000    20,000  annual

Example output — applyr plan:

--- Learning Plan ---

  #     Skill                   Seen  Avg Gap  Priority
  ————  ——————————————————————  ————  ———————  ————————
  1     Experience                 4x      25%  CRITICAL
  2     Tech Stack                 3x      17%  HIGH
  3     English                    4x      10%  MEDIUM

  Focus on CRITICAL and HIGH items first.

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.

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All commands

Command	Description
applyr init	Set up ~/.applyr/ (config, database, agent instructions)
applyr setup-agent [--agent NAME]	Configure AI agent (claude, cursor, opencode, generic)
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 compare <id1> <id2> [...]	Compare offers side by side
applyr plan [--limit N]	Prioritized learning plan from skill gaps
applyr salary [--seniority S] [--category C]	Salary insights by seniority/category
applyr export [--format csv|json|md]	Export all data
applyr cv generate <id>	Generate ATS-safe HTML CV skeleton
applyr cv review <file.html>	Generate recruiter review prompt (ATS score + feedback)
applyr cv pdf <file.html> [--output f.pdf]	HTML to PDF via Chrome
applyr doctor	Check configuration and database health
applyr version	Show version
applyr help	Show help

Aliases

Alias	Command
ls	list
st	stats
fu	followups
cmp	compare
sal	salary

Global flags

Flag	Description
--json	Output structured JSON (available on all data commands)
--no-color	Disable colored output (also respects NO_COLOR env var)

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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	trainee, entry_level, 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

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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.

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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

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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"

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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

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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)..."

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License

MIT