applyr

CLI job application tracker designed for AI coding agents.

Track applications, measure your funnel, spot skill gaps, and generate tailored 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 reads/writes offers, generates CVs, and gives you insights
• Zero dependencies — Python 3.10+ stdlib only. No frameworks, no API keys
• Local & private — your data stays in a SQLite file on your machine

Install

pip install applyr

Or clone and install locally:

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

Quick Start

# 1. Initialize (creates ~/.applyr/ with config, database, and agent instructions)
applyr init

# 2. Edit your CV master (source of truth for all CVs)
#    Open ~/.applyr/cv-master.md in your editor

# 3. Copy agent instructions into your AI tool's config
#    cp ~/.applyr/AGENT_INSTRUCTIONS.md into CLAUDE.md, .cursorrules, etc.

# 4. Add your first offer
applyr add '{"title": "Backend Developer", "company": "Acme", "work_mode": "remote", "salary_min": 35000, "salary_max": 45000, "seniority_level": "junior", "tech_stack": "Python, FastAPI, PostgreSQL"}'

# 4. Check your pipeline
applyr pipeline

# 5. See your stats
applyr stats

Commands

Offer Statuses

pending > applied > waiting > in_process > offer

Side tracks: discarded, rejected

Offer Fields

All fields are optional except title. Your AI agent fills in what it can from the job posting:

{
  "title": "AI Engineer",
  "company": "Acme Corp",
  "summary": "Building LLM-powered features...",
  "date_received": "2026-08-06",
  "date_applied": "2026-08-06",
  "compatibility_pct": 78,
  "status": "applied",
  "canal": "linkedin_easy",
  "work_mode": "remote",
  "location": "Madrid",
  "salary_min": 30000,
  "salary_max": 40000,
  "seniority_level": "junior",
  "role_category": "ai",
  "tech_stack": "Python, LangChain, AWS",
  "cover_letter": 1,
  "contact_name": "Ana Garcia",
  "contact_role": "Recruiter",
  "job_url": "https://...",
  "notes": "Referred by John",
  "topics": {
    "tech_stack": {"score": 85, "detail": "Python strong, LangChain learning"},
    "education": {"score": 70, "detail": "DAM completed"},
    "english": {"score": 60, "detail": "B1 level"},
    "experience": {"score": 40, "detail": "6 months internship"},
    "projects": {"score": 90, "detail": "3 production projects"},
    "cultural_fit": {"score": 80, "detail": "Startup culture match"}
  }
}

Scoring

Compatibility is auto-calculated from topic scores using configurable weights:

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

Using with AI Agents

applyr is designed to be used through your AI coding agent. Here's the workflow:

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>'
       Output: "Registered as #42 — 78% match. Gaps: English, Experience"

You:   "What should I focus on improving?"
Agent: Runs: applyr gaps
       Output: "English appears in 15 offers. Consider getting B2 cert."

You:   "Generate a CV for offer #42"
Agent: Runs: applyr cv generate 42
       Reads cv-master.md + offer details
       Creates tailored HTML CV
       Runs: applyr cv pdf cv-acme.html

You:   "Weekly summary"
Agent: Runs: applyr summary --json
       Output: structured JSON with metrics, trends, and recommendations

Agent Instructions

applyr init creates ~/.applyr/AGENT_INSTRUCTIONS.md — a complete step-by-step guide for any AI agent (Claude Code, Cursor, Aider, OpenCode, etc.). It covers:

• How to read cv-master.md and evaluate offers
• How to build the JSON for applyr add with all valid values
• Rules: never invent content, be honest with scores, leave unknown fields empty
• Which command to run for each type of user question

Setup: Copy the instructions into your agent's config file:

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

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

# Or just include the path in your project's agent config

Configuration

Edit ~/.applyr/applyr.toml:

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

[weights]
tech_stack = 0.30
education = 0.15
english = 0.10
experience = 0.15
projects = 0.20
cultural_fit = 0.10

[topics]
tech_stack = "Tech Stack"
education = "Education"
english = "English"
experience = "Experience"
projects = "Own Projects"
cultural_fit = "Cultural Fit"

[cv]
# chrome_path = "/Applications/Google Chrome.app/Contents/MacOS/Google Chrome"
# cv_master = "~/.applyr/cv-master.md"
# output_dir = "~/.applyr/cv"

Data Storage

All data is stored locally in ~/.applyr/jobs.db (SQLite). Export anytime:

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

License

MIT