Skill-Anything: turning any source into a study pack with quizzes and flashcards

What Skill-Anything produces, and for whom

Skill-Anything is a command-line tool that takes a source, a PDF, a video, a web page, an audio file, plain text or a code repository, and turns it into an interactive learning package: a twelve-section study guide with quizzes, flashcards and a spaced-repetition schedule. The idea is active recall from material you already have, in one command. The audience is self-learners, students and engineers onboarding to a codebase who want structured study material generated from a book, a talk or a repo rather than made by hand. It is a Python CLI published on PyPI, and it calls a language model to do the extraction and generation, so it is not offline; you bring a model, and it builds the outline, glossary, quiz and learning path around what the source actually contains.

Section-aware map-reduce over long sources

The mechanism that matters is how version 0.3 handles length. Earlier versions silently degraded past roughly 15,000 characters: the generator truncated input, quiz and flashcard generation hit per-call caps before reaching later chapters, and every model call was sequential. Version 0.3 introduces a section-aware, map-reduce pipeline. Each source type is parsed into sections, PDFs using the embedded outline with a page-range fallback, text and Markdown by headings, video and audio by bucketing transcripts into five-minute sections, and a repo producing one section per file. A map call summarizes each section, then a single reduce call synthesizes the global summary, cheat sheet, takeaways and learning path. Quota for quizzes and flashcards is allocated per section by size with a guaranteed minimum, so every chapter gets coverage, and a disk cache keyed on a hash of prompt, model and version means a second run of the same source skips cached calls.

Installing and running it on a source

Skill-Anything installs from PyPI and exposes an sa command:

pip install skill-anything

Then you point it at a source and choose a format. For a long PDF you can raise concurrency for the parallel map stage:

sa pdf book.pdf --format all --concurrency 8

The default concurrency is 6, and a second run of the same source hits the cache and finishes in seconds; a --no-cache flag forces a clean run. Other source verbs mirror this shape, such as sa text for Markdown or notes and sa repo for a local path or a public GitHub URL, which builds an onboarding-oriented study pack from a codebase. It needs Python 3.10 or newer, and because it drives a model, you configure the model through environment variables, including a fast model for the map calls and a smarter one for the reduce.

Model routing and repo-to-skill features

Two capabilities are worth calling out. The first is two-tier model routing: a fast model handles the many per-section map calls while a smarter model handles the single reduce, which keeps the bulk of the work cheap and spends the capable model only where synthesis needs it. The second is the repo-to-skill toolchain added in version 0.2: sa repo turns a codebase into onboarding notes, a glossary, a quiz and a learning path, while sa import-skill pulls an existing SKILL.md package back into a study pack and sa lint validates a skill package before you share or publish it. Together these make the tool more than a study-guide generator; it can also produce and check reusable skill packages. The concurrency, per-section failure isolation and caching are the engineering that makes running it over a whole book or repo practical rather than slow and lossy.

Where it depends on a model, and other limits

The limitations are the kind you should expect from an LLM pipeline. Output quality is bounded by the model you configure, and the tool generates quizzes and summaries from the source, so errors in the source or the model's reading carry through, and generated study material should be checked before you rely on it to learn something correctly. It is not offline: it needs model access and, therefore, whatever that costs. The chapter-coverage guarantee is about breadth, ensuring later chapters are not dropped, not a promise that every question is well written. And the case-study counts the README shows, such as a twelve-chapter book yielding thirty quiz questions and forty flashcards, are illustrative of one run rather than a fixed contract. Treat it as a strong first draft of study material, not a finished, verified course.

Skill-Anything versus hand-built Anki decks

The traditional alternative is building your own spaced-repetition deck by hand, in a tool like Anki. Hand authoring gives you complete control and cards you have thought through, which is itself part of the learning, at the cost of the hours it takes and the tendency to under-cover later chapters when energy runs out. Skill-Anything inverts that: it generates coverage across every section of a long source in one command, with quizzes, flashcards and a spaced-repetition schedule, in exchange for accepting machine-generated cards you should review. The two are complementary rather than exclusive. If the act of writing cards is how you learn, do it by hand; if you need broad, structured coverage of a book or a repo quickly and will edit the result, Skill-Anything produces that far faster than manual authoring.

MIT license and release cadence

Skill-Anything is MIT-licensed, so it is freely reusable in commercial and personal settings with attribution, and it is distributed on PyPI as skill-anything. The last push was on 2026-06-01, with version 0.3.0 released the same day introducing the long-context map-reduce pipeline that is the project's main advance over 0.2. The versioned release notes, the disk cache and the per-section failure isolation point to a tool that has been hardened for real, long inputs rather than short demos. Before adopting, decide which models you will wire into the fast and smart tiers, since that choice sets both the cost and the quality, and start on a source you already understand so you can judge how faithful the generated quiz and notes are before trusting it on new material.

Editorial conclusion

Adopt Skill-Anything if you want to turn books, talks, web pages or repos into structured study packs with quizzes, flashcards and spaced repetition in one command, and you will review the generated material rather than trust it blindly. Do not expect an offline or self-verifying tool: it needs model access, and output quality tracks the model you configure. Install with pip install skill-anything on Python 3.10 or newer, wire up the fast and smart model tiers, and test it first on a source you already know well.

Frequently asked questions

Official sources

1. License: MIT
2. Project website
3. README
4. Releases
5. SYuan03/Skill-Anything on GitHub