Are you getting worse at coding without AI? Atrophy tells you - with a number.
Atrophy is a command-line app that regularly hands you a small coding exercise to solve without any AI help - no Copilot, no chat, just you and your editor. It grades your solution automatically, keeps a skill rating for you (like a chess Elo), and charts how that rating moves over the weeks. If AI assistance is quietly eroding your ability to code unaided, the chart shows you - before an interview, an outage, or a day without wifi does.
How it works
atrophy baseline- once, ~25 minutes. Solve one exercise for each of five skills, AI off. This sets your starting ratings.atrophy drill- 5-10 minutes, two or three times a week. One exercise, automatically picked from the skill you've neglected longest. Pass and your rating rises; fail and it falls.atrophy serve- your dashboard. One curve per skill, plus the chart this tool exists for (more below).- Once a month:
atrophy drill --ai-on- take one drill with your AI tools. Those scores are tracked separately, so the dashboard can show the gap between you-with-AI and you-alone.
What a drill looks like
$ atrophy drill
Binary search misses the edges [debugging · python · tier 2]
────────────────────────────────────────────────────────────
binary_search(items, target) should return the index of target in the
sorted list items, or -1 if absent. It mysteriously fails for some
values that are clearly in the list. Find and fix the bug.
────────────────────────────────────────────────────────────
Edit: /tmp/atrophy-k3XoP1/solution.py
AI off. Soft limit 7 min - timer started.
[Enter] submit · [q] abandon >
✓ 6/6 tests passed in 214s
Score 1.00 · debugging rating 1222 → 1241 (+19)
The exercise opens in your own editor ($EDITOR). Grading runs your code
against hidden tests in a sandboxed subprocess. There's a soft time limit -
going over shrinks your score gradually, nothing explodes. If tests fail you
can keep fixing and resubmit; the clock just keeps running.
Not every skill is "write code against tests" - see the table below.
The five skills
| Skill | The drill | Graded by |
|---|---|---|
| Syntax recall | Write a small function from a spec | Hidden tests |
| Debugging | Working-looking code has one planted bug - find and fix it | Hidden tests |
| Code reading | Read a snippet, type exactly what it prints | Compared to the snippet's real output |
| API memory | Fill in the blanked-out stdlib call | Answer match |
| Decomposition | Outline a design (rate limiter, folder sync…) in bullets | You score yourself against a revealed rubric |
Exercises come in Python and JavaScript across three difficulty tiers - a hand-written static bank plus generator families that render endless fresh variants (randomized data, names, and twists; same seed always reproduces the same exercise). Difficulty targets you: each drill picks the tier where your predicted success is closest to ~65%, the point where a rep carries the most information. Comfortable wins teach the rating nothing.
The dashboard
atrophy serve # http://127.0.0.1:4646Try the live demo → (synthetic data)
How to read it:
- The line is your skill rating. It only moves when you actually take a drill - no evidence, no movement.
- The shaded band around the line is confidence. Skip practicing for a few weeks and the band visibly widens: the tool isn't claiming you got worse, it's admitting it no longer knows you're still good. One drill snaps it tight again.
- "Unaided vs AI-assisted" plots every drill score in two colors: your solo reps in blue, your monthly with-AI reps in green. If the blue line sinks while the green line stays perfect, that growing gap is your dependence, measured. This chart is the reason the tool exists.
Why take this seriously
The pattern is documented across professions, and it comes with no internal warning signal - people consistently feel fine while measurably declining:
- Doctors' unaided polyp-detection rates fell 28% → 22% within months of routine AI assistance (The Lancet G&H, 2025)
- Students with GPT-4 scored 17% worse than peers once it was taken away (PNAS, 2025)
- Experienced developers using AI were 19% slower - while believing they were 20% faster (METR RCT, 2025)
- Engineers who used AI to write code scored 17% lower on understanding that same code - debugging suffered most (Anthropic, 2026)
Full citations and an honest discussion of what this tool can and can't measure: docs/research.md.
Install
Requires Node.js ≥ 22, plus Python 3 on PATH if you want the Python exercises.
npm install -g atrophy atrophy baseline
Command reference
| Command | What it does |
|---|---|
atrophy baseline |
First session: one drill per skill (~25 min) |
atrophy drill |
One drill on your most-neglected skill |
atrophy drill --axis debugging |
Drill a specific skill (syntax-recall, debugging, code-reading, api-memory, decomposition) |
atrophy drill --lang python |
Only Python (or javascript) exercises |
atrophy drill --ai-on |
Monthly comparison rep with AI allowed |
atrophy publish --handle you |
Opt in to the public leaderboard; afterwards every drill syncs automatically (--stop opts out) |
atrophy stats |
Ratings table and week streak in the terminal |
atrophy serve |
Dashboard at 127.0.0.1:4646 |
atrophy export -o out.json |
Dump all your data as JSON |
Your data
One SQLite file at ~/.atrophy/atrophy.db, owned by you. No account, no
sync, no telemetry, nothing leaves your machine. ATROPHY_DB overrides the
location if you want it in a dotfiles repo or synced folder.
Honest limitations
- Ten-minute drills are a proxy for real-world skill, not a clinical measurement - treat trends, not absolute numbers, as the signal.
- Drilling makes you better at drills. That's fine - the drill is the maintenance - but it's another reason the interesting number is the unaided-vs-AI gap, not your raw rating.
- "AI off" is an honor system, actively assisted: starting an unaided drill while a known AI assistant is running (Copilot, Cursor, Claude, Windsurf, Codeium, Tabnine, Ollama, LM Studio, ChatGPT, Aider) prints a warning that names it. Warned, never blocked - you'd only be cheating your own chart.
Contributing & development
git clone https://github.com/ashutosh-rath02/atrophy.git cd atrophy && npm install npm run dev -- drill # CLI from source npm test # 70 tests, incl. real grading subprocesses
New exercises are the most welcome contribution: one JSON file under
bank/exercises/<skill>/, validated by bank/schema.ts. CI proves every
planted bug actually fails a test and every code-reading snippet runs
deterministically, so a broken exercise can't merge.
Roadmap: LLM-judged decomposition drills, more languages, spaced-repetition scheduling (FSRS), per-axis leaderboards.
License
MIT © 2026 Ashutosh Rath