--- title: "Using Agentic AI Tools for Anki Flashcard Creation" description: "How Claude Code and similar tools can handle the tedious work of expanding concepts into multiple flashcards, and why Anki Connect is the key to making it work." type: note pubDate: 2026-01-08T00:00:00.000Z topics: ["anki", "spaced-repetition", "agents", "learning", "claude-code", "languages"] canonicalUrl: "https://actuallymaybe.com/blog/agentic-ai-anki-flashcard-creation/" --- > How Claude Code and similar tools can handle the tedious work of expanding concepts into multiple flashcards, and why Anki Connect is the key to making it work. Building on my earlier thoughts about spaced repetition and conceptual learning, I have been experimenting with using Claude Code and other agentic AI tools to help with Anki flashcard creation. The results have been surprisingly useful. ## The Tedium of Good Flashcards One of the hardest parts of maintaining an Anki habit is the upfront work. Taking a single concept and properly exploding it into multiple cards is tedious. You need cloze deletions, reverse cards, related conceptual variations: all the things that actually make spaced repetition effective. Most people, myself included, tend to cut corners here. This is where agentic tools shine. They handle the monotonous expansion work without complaint. ## Mathematics as an Example I have found this particularly useful for mathematics. Take something simple like a trigonometric identity. When you feed it to Claude Code with instructions to follow flashcard best practices, you get back multiple cloze variations of the formula, a derivation card that helps you reconstruct it from first principles, and related identities that reinforce the underlying pattern. The key is that you can ask the tool to explain its reasoning. Why did it choose these particular cloze deletions? What conceptual gaps is it trying to address? This dialogue helps you refine the cards and also teaches you something about effective flashcard design. ## Language Learning and Grammar Drills Another area where this workflow has proven useful is language learning, particularly for grammatical concepts that require drilling. Take Polish noun declension as an example. You learn a word in its base form, but that is only the beginning. In actual use, the ending changes depending on whether the noun is in the dative, genitive, accusative, or another case. To really internalise this, you need to see the word in multiple contexts, not just one card, but many variations showing how the form shifts. Flashcards are not the only way to learn this, of course. You still need to practise speaking, reading, and writing. But once you understand the general concept of how declension works, there is no real benefit to manually creating dozens of drill cards yourself. The tedious part is not the learning: it is the card creation. What you actually want is to see the examples in context, review them, and build the intuition. You want to look at a sentence and think: this pronoun is in this form, so it must be genitive, which means it pairs with *kogo* or *czego*. Agentic tools are well suited to generating these drillable examples. You describe the grammatical pattern, provide a few seed words, and let the tool produce a batch of contextualised cards. The cognitive work stays where it belongs: on the review side, not the production side. ## The Missing Piece: Anki Connect One thing I highly recommend for this workflow is Anki Connect.[^1] It adds a local server to your Anki database, exposing an API that any agentic tool can use to create, modify, and query cards directly. What I did was copy the overview documentation from the Anki Connect website into a markdown file in my project. Claude Code then knew exactly how to interact with my Anki database: creating decks, adding cards with proper formatting, even checking for duplicates before adding new material. Without Anki Connect, you are stuck copying and pasting card content manually, which defeats much of the purpose. With it, the workflow becomes genuinely hands-off. Describe what you want to learn, let the tool generate and refine the cards, and they appear in Anki ready for review. [^1]: See [Anki Connect](https://git.sr.ht/~foosoft/anki-connect) for installation and API documentation. ## Further reading - [Building Explainers Random Thoughts - Drone 101](https://actuallymaybe.com/blog/building-explainers-random-thoughts-drone-101/) — An experiment in interactive explainers: drone control, Fourier, Bret Victor, philosophical prompts, accessibility, and learning with LLMs. - [Thoughts on Conceptual Learning, Anki, Books & Physicality](https://actuallymaybe.com/blog/conceptual-learning-anki-books/) — Exploring why spaced repetition works for languages but fails for abstract, ladder-like concepts, and where solutions might lie - [Notes on Visual Interfaces, AI Tooling, Fast LLMs and Decision Models](https://actuallymaybe.com/blog/notes-visual-interfaces-ai-tooling-fast-llms-decision-models/) — Why agentic work is stuck in text, and what canvases, fast models, branching version graphs, and decision models might add. - [Emerging Field of Epistemic Engineering or Stochastic Management](https://actuallymaybe.com/blog/emerging-field-epistemic-engineering/) — Practices around context, uncertainty, and evals are hardening into a discipline, with belief states at its centre: probabilistic, grounded views of a work process. - [Cheaper Code Should Mean More Experiments](https://actuallymaybe.com/blog/cheaper-code-more-experiments/) — When working with agentic tools, the instinct to solve hard problems inside your main project is usually wrong. Isolate the core problem first, experiment freely, then bring what works back.