A tool that turns computer-analyzed Lichess games into spaced-repetition cards so players drill their own recurring mistakes.
“Mistake Trainer: Spaced Repetition for Lichess Blunders” is an intermediate Games & Simulations AI agent workflow from Any. It connects Claude Code and Other or custom in 5 steps. Setup takes about Not stated and running costs are about Not stated.
Players who review their Lichess games keep repeating the same kinds of errors. The built-in Learn from your mistakes feature only covers one analyzed game at a time, so there is no systematic way to revisit past blunders across a whole game history and reinforce the right move.
Request computer analysis on your Lichess games so the engine has flagged the positions where you went wrong. The tool draws on these analyzed games rather than raw move lists.
Instead of reviewing one game at a time, collect the flagged mistake positions from your entire analyzed history into a single pool of review material.
Turn each mistake position into a card. A sample deck is available if you do not play on Lichess or want to try the tool before connecting your own account.
Work through the deck using spaced repetition so positions you get wrong come back sooner and ones you have mastered fade out. The interface mirrors Lichess so the review feels familiar.
If you have engine analysis from outside Lichess, you can use it to generate cards from all of your games rather than only those analyzed on the site.
A tool that turns computer-analyzed Lichess games into spaced-repetition cards so players drill their own recurring mistakes.
You need Claude Code and Other or custom.
Plan for about Not stated. It is rated intermediate. The guide walks through 5 steps, starting with “Analyze your games”.
Expect about Not stated in running costs (Free range).
Players who review their Lichess games keep repeating the same kinds of errors. The built-in Learn from your mistakes feature only covers one analyzed game at a time, so there is no systematic way to revisit past blunders across a whole game history and reinforce the right move.
Claude Code got an old DirectX game running on modern hardware, decompiled much of it, built a headless parallel runner, and now writes full level guides with screenshots.
Original post ↗Plan, execute and review a cross-cutting refactor in small batches, with Claude Code making the edits and GitHub Copilot reviewing each PR.
Claude Code reads your coverage report, picks the riskiest untested modules and writes unit tests that pass, one small PR at a time.
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