dmoshehun-prog/learn-from-materials · dmoshehun-prog

"Which sentences come from the book and which were made up by AI": how a reading skill grew to 1300 stars with one line

What it is A skill package you install in agents like Claude Code and Codex. It turns PDFs, books, and slides into offline study pages, marks every sentence as either from the source or added by AI, and cites PDFs down to the page number.

Why it took off It turned the anxiety everyone has, that AI summaries don't tell you which sentences are original and which are invented, into its selling point. Chinese X accounts that promote GitHub repos reused the same copy every week or two, and each post brought in 60–200 stars.

BHow you could follow it Stop building Chinese PDF-to-study-page tools. Instead, take the "source vs. AI-added, labeled separately" idea to English-language audiences or to materials like contracts, earnings reports, and meeting recordings. Or fix what people complain about, the slowness and the need to paste back into an agent and the lack of flashcards, and then find a promoter for a one-line pitch.

Grade B: the "MIT base + one-line trust pitch + Chinese promoter" recipe for stars is clear and copyable, but the space is already crowded. The pitch isn't unique, real installs are only in the dozens, and whether stars come depends on whether a big promoter writes about you. Circulating in small circles

Data (updated daily) · Repo created 2026-09-08 · 1,334 stars · 7 days +132: GitHub · stars
Data 2026-10-09 → 2026-11-08 (31 days, 30 observations). Points before 10-28 come from deeply / archive backfill, not every day has one

learn-from-materials is a skill package installed in agents like Claude Code and Codex. It turns PDFs, ebooks, and slides into an offline study page with each sentence's source marked. It gained 1,322 stars in 59 days, but nearly all of its features are stitched together from two existing open-source projects, and nearly all the stars came from four or five Chinese X recommendation posts. What's worth learning is the playbook, not the product.

A post from the evening of Sep 14

On 2026 Sep 14 13:30 (UTC), GitHubDaily, one of the biggest GitHub recommendation accounts in the Chinese-speaking world, posted on X. The post opened like this:

I downloaded a 300-page PDF to read it systematically, but I didn't dare fully trust what the AI summarized. I couldn't tell which sentences were in the book and which it had added itself.

It then introduced a repo created just 6 days earlier with only a dozen stars, dmoshehun-prog/learn-from-materials, with its own comment: "What I value most is that it separates 'what's in the material,' 'what the material doesn't cover,' and 'what the model added.' When you read, you know where you stand. This design is more honest than most AI reading tools." The replies were things like "I just have a pile of papers to read, I'll deploy it this weekend" and "With long documents, being able to check against the original is crucial."

Over those two days the repo gained 41 and 66 stars (daily data from trendshift). Every week or two after that, another account posted with nearly the same opening, and the stars went up another step.

The author's GitHub account was registered in Jul 30, has 13 followers, and this is its only real repo. There's no X presence and no personal site. The last code push was in Oct 19, at version v1.1.0.

What it actually does

You hand the agent a material (PDF, EPUB, MOBI, Word, PPT, web page, or Markdown, multiple files are fine). It reads through it, builds a local knowledge base, and generates a single-file HTML study page in six parts: a core framework card with a relationship graph, a content guide, a glossary, action rules, self-test questions, and local notes. There are two modes, "quick overview" and "systematic study," and the systematic mode goes back over the material after finishing to check for chapters it missed.

The only thing it really leans on as a selling point is this: every piece of content is tagged with one of four categories: from the source, not covered by the source, added by the model, or verified externally. Citations go down to the PDF page, the PPT slide, or the EPUB chapter. It doesn't go online by default, and notes and wrong-answer records are stored only in the browser.

It follows the open Agent Skills spec (a SKILL.md plus a few Python scripts), so Claude Code, Codex, Copilot CLI, and Tencent's WorkBuddy can all install it. The README is bilingual in Chinese and English, but the example pages were built in Chinese first.

The foundation belongs to others

NOTICE.md in the repo says it plainly: text extraction, dependency fallback, and knowledge-base structure are adapted from virgiliojr94/book-to-skill (launched in May, now 37k stars, turns technical books into skills agents can query). The study page design is rewritten from crayon-ai/book-to-webpage (launched in June, a Chinese project, "turns books into interactive web pages," also with source citations). Both are MIT-licensed.

The second one is interesting: book-to-webpage does nearly the same thing, is also a Chinese project, and by Nov 6 had only 178 stars. learn-from-materials has more than 7 times that. The difference isn't features. It's whether a big account wrote about it, and whether the pitch can be explained in one line.

How the stars climbed, step by step

Date (UTC)	What happened	stars
9-08 ~ 9-13	Repo created	Total +15
9-14 ~ 9-15	GitHubDaily posts	+41, +66
9-18 ~ 9-20	Source not verified; another Chinese account, "Xu Lun," posted a similar opening, date unverified	+47, +70, +54
9-26 ~ 9-27	"Zhoulan Resources" posted with the same copy; 9-27 reached #22 on trendshift's Python chart, its only chart appearance	+58, +142
10-04	"AI Lei Shu" posted a benchmark they had run themselves	+66
10-12, 10-24 ~ 10-26	Source not found	+48, total +102
10-27 ~ 11-02	No one posted	1–6 per day
11-03 ~ 11-04	"Gk" (@0xGky) posted the same copy again	+68, +39

Data is from trendshift's daily figures. On GitHub it's recorded in Beijing time, so 11-04 was +64, and the one-day difference is a timezone artifact. Over 59 days it totaled +1,257 stars and +134 forks, with only the author as a contributor and no external PRs merged.

The Zhoulan and Gk posts are nearly identical in structure to GitHubDaily's: "hundreds of pages → interactive learning site," "PDFs cited to the page, PPT by slide, EPUB by chapter," "installable in Claude Code, Codex, and Copilot CLI," "AI summaries aren't scary, what's scary is not finding the original." This is a common move among Chinese X GitHub-promoter accounts: a topic that worked once gets reposted by a different account one or two weeks later. Whether these posts were submitted and paid for by the author, or chosen by the account owners, can't be determined.

The author also posted about it: in Sep 23 they wrote an article on the Tencent Cloud developer community, "I built a universal material-learning assistant Skill," which got 243 reads. They also posted on Reddit r/claudeskills. Neither produced a clear star spike. Dozens of skill aggregator and SEO sites auto-indexed it, bringing long-tail traffic but no breakout. There was no media coverage, no English-language influencer reposts, and nothing on Hacker News or Product Hunt.

Lots of stars, few users

On skills.sh it has 44 installs total (11-06). Direct git clone installs don't go through there, so this is a lower bound, but the scale suggests most of the stars were "bookmark first."

What users say? AI磊叔's Oct 4 post is the only real test that ran: a 33-page PDF, study mode, 20 took several minutes. "One page per minute." "A 100-page PDF takes an hour and a half, 4 to 5 hundred pages would take five or six hours. I can't wait that long." He thinks the best feature is "turning knowledge into prompts": click a method, fill in your confusion, and it combines the question with the book's method into a prompt you can copy. His complaints: the tooltip "follows the mouse and never goes away, blocking the content", and "Explain with AI" and the quizzes on the page only give you a prompt you have to paste back into the agent to get anything further, "which breaks the flow of learning." Compared with NotebookLM, it's slower and has no flashcards or mind maps.

A Japanese company called Fyve ran a comparison in Oct 21: with a few pages of Japanese regulations and no skill installed, Claude Code alone produced a study page with page-number citations and quizzes, in about the same time and cost. For short material, the extra value this adds is small.

Who else is doing the same thing

Name	Launched	Scale	How it differs
Google NotebookLM	2023	Mainstream products	Citations, quizzes, flashcards, podcasts, mind maps; fast, nothing to install. But it runs in the cloud, doesn't export an offline web page, and doesn't separate "source text / model additions"
ChatGPT and Claude study modes	2025	Mainstream	Conversational tutoring; doesn't leave behind a structured study page
virgiliojr94/book-to-skill	2026-05	37,055 stars	Its upstream: turns books into skills that agents can query
crayon-ai/book-to-webpage	2026-06	178 stars	Another upstream, similar functionality, nobody promoting it
beltromatti/get-it	2026-05	1,151 stars	Hackathon project, visual understanding, not an agent skill
SYuan03/Skill-Anything	2026-03	345 stars	English-speaking community; command line; outputs quizzes, flashcards, Anki, offline sites; also does verbatim citation verification
Nutlope/pdf-to-interactive-lesson	2025-10	139 stars	Web app; turns PDFs into interactive courses; no source-layering
sebastianhaba/book-to-course-skill	2026-10-03	76 stars	Turns books into introductory courses with animations and quizzes; can be packaged as an APK
ai-azhou/pdf-to-interactive-web	2026-10-22	90 stars	Same direction, 6 weeks later

The star count is 11-06 on GitHub. This space is already crowded, and "marking sources" isn't unique: NotebookLM has citations, Skill-Anything has citation verification. learn-from-materials wins by making that point its most prominent headline, not on technology. I also haven't seen a repo that clearly copies it; none of the 124 forks has grown on its own.

Why it made the charts

The selling point is the anxiety users already voice. Everyone has felt it: "which sentence is from the book, which did the AI make up?" "Exact page numbers for PDFs" is a concrete promise you can screenshot. Every recommendation post repeats this line, and the comments all revolve around "I can check against the original."
Recommendation accounts rotated through it, one wave at a time. 4 documented posts and 4 star spikes line up one-to-one, each bringing 60–200 stars, and the curve flattens between posts.
It caught the moment Agent Skills became a common format. WorkBuddy, Codex, and Copilot CLI all recognize SKILL.md, and Chinese users are looking for skills they can install. book-to-skill reached 37k stars in May, which revived the "turn a book into a skill / web page" topic.
The screenshots look good. The README has 16 liquid-glass-style screenshots. The sample materials are Musk's biography, J.P. Morgan's mid-year outlook, and "Attention Is All You Need", which recommendation accounts used directly as cover images.

If it were you

One-line verdict: worth trying. Building another "book / PDF to study page" is already too crowded. What's worth learning is its recipe: start from an MIT base, add a trust selling point you can explain in one sentence, package it well, and get Chinese recommendation accounts to write about you.

Who it's for: developers who want their open-source project to reach its first thousand stars and who have an agent workflow to show, especially Chinese developers. This playbook worked on Chinese X.

Who it's not for: people who want to build a tool that people actually use every day. 44 installs, and Claude Code alone can produce the same result, which suggests the real usage of this format is very thin.

How to do it:

Stop building Chinese "PDF to study page" tools. Directions you could pivot to: the English-speaking world has no version that marks "source text / AI additions" apart (the closest, Skill-Anything, has only 345 stars); or move the same selling point to other material, like contracts, financial reports, paper reviews, or meeting recordings, where "which line was said, which is an AI summary" matters even more.
Do what it didn't do well: AI磊叔 complained about three things, slowness (one page per minute), follow-up questions and quiz answers having to be pasted back into the agent, and no flashcards or mind maps. Doing any one of these well would be the headline of a new recommendation post.
Record the upstreams in NOTICE.md; MIT base compliance is quick.

How to get users: write the selling point in users' own words, with the anxiety as the first line of the README; use sample screenshots of familiar material (celebrity biographies, investment bank reports, classic papers); and prepare copy that recommendation accounts can repost directly, then send it to accounts like GitHubDaily. Once one gets written up, repost accounts often pick it up a week or two later. For English, the equivalents are r/ClaudeAI, r/claudeskills, and Show HN.

When to move early: interest in the Agent Skills format is still strong, but several new repos in the same direction have already appeared since October. My estimate is that within two or three months, "reading / study skills" will be written about to the point that recommendation accounts stop wanting to cover them.

What would show I'm wrong: if after the next similar repo is covered by recommendation accounts, star spikes clearly shrink (say, a post brings only twenty or thirty stars), that means the topic is already exhausted with those accounts; or if NotebookLM or Claude's own study mode adds "source / model addition" layering, this selling point is gone.

GitHub repo ↗

Research notes: competitor table, acquisition evidence, full research report

Competitors

Name	Where	Launched	Scale	How it differs from this one
Google NotebookLM	Big-company features	2023	Mainstream products	Cloud-based, fast, many formats (podcasts, flashcards, mind maps), with citations; but no offline HTML export and no source / model-addition layering
ChatGPT / Claude study modes	Big-company features	2025	Mainstream	Conversational tutoring; doesn't produce a structured study page you can keep
virgiliojr94/book-to-skill	Open source	2026-05-01	37,055 stars	Its upstream: turns books into agent skills for querying; learn-from-materials reuses its extraction and knowledge-base structure, and adds study pages and source layering on top
crayon-ai/book-to-webpage	Open source	2026-06-16	178 stars	Its other upstream; the study-page design was rewritten from this one. Similar functionality, but it never pushed the source-layering selling point and wasn't picked up by big accounts
SYuan03/Skill-Anything	Open source	2026-03-25	345 stars	CLI format; outputs quizzes, flashcards, Anki, and offline sites; also does verbatim citation verification; English-speaking community
Nutlope/pdf-to-interactive-lesson	Open source	2025-10-28	139 stars	Web app format; no source layering or coverage audit
sebastianhaba/book-to-course-skill	Open source	2026-10-03	76 stars	More of an introductory course (animations, unit tests, APK); a month later
beltromatti/get-it	Open source	2026-05-09	1,151 stars	Hackathon project, leaning toward visual understanding; not an agent skill

Where users come from

Verified X Chinese GitHub recommendation account: GitHubDaily: posted 2026-09-14 13:30 UTC (x.com/GitHub_Daily/status/2099490871851045224), 9-14/15 +41/+66 stars
Verified X repost account: 周览资源: posted with the same template copy 2026-09-26 11:51 UTC, 9-26/27 +58/+142 stars; 9-27 reached #22 on the trendshift Python chart
Verified X review account: AI磊叔: posted a hands-on review 2026-10-04 11:29 UTC (mentions 800 thousand stars), +66 that day
Verified X repost account: Gk (@0xGky): posted the same template 2026-11-03 06:12 UTC, 11-03/04 +68/+39
Inferred X: 徐伦 (@Jolyne_AI): search results include a post with the same opening, which may correspond to +171 between 9-18 and 20; date not verified
Verified Self-published by the author: Tencent Cloud Developer Community, Reddit r/claudeskills: the Tencent Cloud article has 9-23,243 reads; the Reddit post's date and engagement are unknown, and neither matches a clear star spike
Verified Passively indexed by skill aggregators and SEO sites: SkillsLLM, TypingMind, agentbuffs, SkillFoxx, skillpx, 3kjs, fyve.co.jp, and others

Why learn-from-materials (dmoshehun-prog) reached 1300 stars

Channel: GitHub · research day 2026-11-06 · data card: 2026-09-08,1322 stars / 124 forks at repo creation, +125 in the last 7 days

Bottom line

It's an Agent Skill. Once installed in Claude Code, Codex, or WorkBuddy, it turns PDFs, books, and PPTs into offline study web pages, with every sentence marked with its source. It is not a hit. In 59 days its stars came almost entirely from 4–5 Chinese X "GitHub recommendation" account posts, each pushing it up a step, each post bringing 60–200 stars, and the curve flattens whenever the posts stop. All these posts lead with the same selling point: "which sentence is from the book, which did the AI make up?" separated out, with exact PDF page numbers. That selling point is its differentiator and the reason those accounts are willing to write about it. But its foundation is two existing MIT projects it was built on (book-to-skill, 37k stars; book-to-webpage, 178 stars), and actual installs are small (only 44 installs total on skills.sh).

1. What it is

A skill package following the open Agent Skills spec (Python scripts plus SKILL.md), usable by any agent that can read files and run Python: WorkBuddy (Tencent), Codex, Claude Code, Copilot CLI.
Input: PDF / EPUB / MOBI / DOCX / PPTX / HTML / MD / multiple materials. Output three things: a .learnkb/ knowledge base, a single-file offline HTML study page, and Markdown generated from the same source.
The study page has six modules: core framework cards with a relationship graph, a content guide, terms, action rules (method diagrams), dynamic self-tests, and local notes. Two depth levels: "quick overview" and "systematic study"; systematic study runs a coverage audit to check for anything unread.
Selling point protocol: content is labeled in four separate categories: what the source material contains, what it doesn't cover, what the model added, and what was checked against external sources. PDF citations go to the page, PPT to the slide, EPUB to the chapter. Offline by default, doesn't auto-install dependencies.
Bilingual. The README and sample pages were built in Chinese first (GitHubDaily's own words: "the README and pages are both in Chinese").
The author's account was registered 2026-07-30, has 13 followers, and this is the only notable repo under their name. No X, no personal site (GitHub API). The last code push was 10-19; the current version is v1.1.0.
Sources: README, NOTICE.md (https://github.com/dmoshehun-prog/learn-from-materials)

2. Competitors: who else does the same thing

Name	Where	Launched	Scale	How it differs
Google NotebookLM	Big-company features	2023	Mainstream products	Citations, study guides, quizzes, flashcards, podcasts, mind maps; fast, zero install. But it runs in the cloud, doesn't export an offline HTML, and doesn't label "source / model addition" categories. AI磊叔's review says it's slower than NotebookLM and has fewer formats.
ChatGPT study mode / Claude study mode	Big-company features	2025	Mainstream	Conversational tutoring; doesn't produce a structured study page you can keep
virgiliojr94/book-to-skill	Open source	2026-05-01	37,055 stars (our GitHub repo)	One of its upstreams: turns technical books into Claude Code skills that agents can query; learn-from-materials reuses its extraction, dependency fallback, and knowledge-base structure (NOTICE.md)
crayon-ai/book-to-webpage	Open source	2026-06-16	178 stars	Another upstream, a Chinese project that "turns books into interactive web pages," with 6 themes, source labeling, and follow-up questions. The study page design was rewritten from it (NOTICE.md). Functionally almost identical, but only 178 stars
SYuan03/Skill-Anything	Open source	2026-03-25	345 stars	Converts any source into a learning pack (quizzes, flashcards, spaced repetition, Anki, offline sites); v0.4.1 also added verbatim citation verification; CLI format, English-speaking community
Nutlope/pdf-to-interactive-lesson	Open source	2025-10-28	139 stars	Web app; turns PDFs into interactive courses; no source-layering
sebastianhaba/book-to-course-skill	Open source	2026-10-03	76 stars	A Claude skill that turns books into introductory courses (animations, quizzes, unit tests), can be packaged as an APK; launched a month later
ai-azhou/pdf-to-interactive-web	Open source	2026-10-22	90 stars	Same-direction repo, shipped 6 weeks later
beltromatti/get-it	Open source	2026-05-09	1,151 stars	Hackathon project: read, watch, understand. Visual learning
drpwchen/textbook-to-note	Open source	2026-07-19	110 stars	Turns textbooks into a searchable knowledge base with cited notes. No interactive pages
PleasePrompto/notebooklm-skill and others	Open source	2025-10	7,785 stars	Has Claude Code call NotebookLM. Opposite approach (borrows a big-company product)

Scale source: our github repo_profile (2026-11-06); related repo list from trendshift's "You might also like".

Summary: the "book/PDF to interactive learning page" direction is crowded. The real origin is two MIT projects: book-to-skill and book-to-webpage. learn-from-materials was not first, yet its star count is over 7× that of book-to-webpage, the closest in function.

3. Growth: how the stars came

Curve (trendshift counts by UTC day)

9-08 repo created; 9-09 to 9-13 total +15.
9-14 +41, 9-15 +66: GitHubDaily posted at 9-14 13:30 UTC (details below).
9-18 +47, 9-19 +70, 9-20 +54: the Chinese account 徐伦 (@Jolyne_AI) posted with nearly the same opener as GitHubDaily, "downloaded a 300-page PDF…", but the posting date is unverified.
9-26 +58, 9-27 +142 (+28 forks): 周览资源 (@grgerwcwetwet) posted at 9-26 11:51 UTC; on 9-27 it hit #22 on trendshift's Python chart, its only chart appearance.
10-04 +66: AI磊叔 (@AgiRay1015) posted a hands-on review at 10-04 11:29 UTC (back then "800 stars").
10-12 +48, 10-24~26 +102: source not found.
10-27~11-02 nearly stalled (1–6 stars per day).
11-03 +68, 11-04 +39: Gk (@0xGky) posted at 11-03 06:12 UTC. Our database logged it as 11-04 +64 because sampling uses Beijing time, one day off from trendshift.
Over 59 days: +1,257 stars and +134 forks (trendshift); 0 merged PRs, from only 1 contributors.

Source: trendshift.io/repositories/230593. Post times were derived from X post IDs (snowflake) and checked against the Published Time that r.jina.ai returned.

Key posts

GitHubDaily, 2026-09-14 (https://x.com/GitHub_Daily/status/2099490871851045224): a leading Chinese GitHub recommendation account, with a WeChat Official Account of the same name. The post frames the pain point as "the AI summary is useful but you can't trust it; you can't tell which sentences come from the book and which the model filled in," and praises the "separate labels for 'in the material', 'not covered', and 'model-supplied'… more honest than most AI reading tools."
周览资源, 2026-09-26 (https://x.com/grgerwcwetwet/status/2103814710927986998) and Gk, 2026-11-03 (https://x.com/0xGky/status/2117500019003535529): both follow the same structure as GitHubDaily: "hundreds of pages → interactive learning site / PDFs cited to the page, PPT to the slide, EPUB to the chapter / works with Claude Code, Codex, Copilot CLI / AI summaries aren't scary, not finding the source is." They are clearly rewrites of one template. This is the typical playbook of Chinese X "GitHub goods repost" accounts: a post that worked gets reposted from another account one or two weeks later.
AI磊叔, 2026-10-04 (https://x.com/AgiRay1015/status/2106708354102083599): the only real hands-on review. A 33-page PDF ran through the systematic study mode for 20 minutes, "one minute per page." The tooltips glitched. On the learning page, "explain with AI" and "quiz" required pasting the prompt back into the agent to continue. Compared with NotebookLM, it was slower and had fewer formats. Conclusion: "light materials are workable."
Author's own posts: a Tencent Cloud developer community article, "I built a universal material-learning assistant Skill" (2026-09-23,243 reads, developer.cloud.tencent.com/article/2750006); and a Reddit r/claudeskills post, "I built a free agent skill that turns PDFs into offline study pages…" (when v1.0 shipped; Reddit blocked the page, so exact date and engagement are unverified). Neither corresponds to an obvious star spike.
Passive listings: SkillsLLM, TypingMind skills, agentbuffs, Chalk Labs, SkillFoxx, skillpx, vluz.eu (verbatim reposting), 3kjs.com (9-27 Chinese introduction), fyve.co.jp (Japanese hands-on test, 10-21), and dozens of other skill aggregators and SEO sites. These sites provide long-tail exposure, not spikes.

Actual usage is far below the star count

The skills.sh page shows 44 total installs (2026-11-06 as scraped). Clone installs bypass skills.sh, so this is a floor, but the order of magnitude suggests most stars are bookmarks.
Fyve's Japan test (https://fyve.co.jp/claude-code/articles/learn-from-materials-skill-guide): using a few Japanese specification pages, Claude Code produced an equally good learning page with page-cited sources and quizzes without the skill installed, with similar time and cost. For short materials, the added value is limited.

4. Why it took off

A trust selling point that fits in one sentence suits recommendation posts. "AI book summaries can't tell which sentences are from the original and which are made up": everyone has that anxiety. "Cited to the exact page" is a concrete promise people can screenshot. Each recommendation post restates it, and the comments under the AI磊叔 and Gk posts also center on "I can check it against the original."
Repeatedly amplified by Chinese X's GitHub recommendation accounts. 4 documented posts (9-14, 9-26, 10-04, 11-03) match 4 star-spike dates one to one, each adding +60~200; the curve flattens between posts. This is a classic externally-driven content pattern, not organic word of mouth.
Timing: Agent Skills became a cross-agent standard, and Chinese users were looking for skills they could install. Tencent WorkBuddy has SkillHub, and Codex and Copilot CLI both recognize SKILL.md. In 2026 May, book-to-skill reached 37k stars and revived the "turn books into skills/webpages" topic in Chinese circles (for example, @IndieDevHailey wrote about Book-to-Skill in August). learn-from-materials rode that wave as an upgraded version.
Well packaged. The README is bilingual, with 16 liquid-glass-style screenshots. The sample materials are ones readers recognize: a Musk biography, J.P. Morgan's mid-year outlook, and "Attention Is All You Need." The screenshots look good, and recommendation accounts used them directly as images.
Compared with upstream: book-to-webpage is similar in function and also a Chinese project, yet has only 178 stars, because no big account reposted it and it didn't push the "original vs AI-supplied" layering. The gap isn't the features; it's whether a recommendation account wrote about it, and whether the selling point fits in one sentence.

5. Differentiation, and whether that's why it broke out

Differentiation: (1) a source-layering protocol that labels four categories separately (material text, material not covered, model-supplied, external verification), down to the page, slide, or chapter for PDF/PPT/EPUB; (2) a completeness audit, where the systematic study mode runs a coverage audit and reverse spot-checks; (3) a method library, where methods.json accumulates methods across materials and is searchable, and "one-click use of methodology" generates a copyable prompt; (4) an offline single-file HTML, with data stored only locally.
Why it spread: (1) yes. Every recommendation post only talked about sourcing and anti-fabrication. (2) (3) (4) were almost never mentioned. AI磊叔 found "turning knowledge into prompts" most useful, which counts as (3). But the differentiation isn't unique: NotebookLM has citations, and Skill-Anything v0.4.1 also does verbatim citation checks. It wins by making this the most prominent headline, not through a technical moat.

6. How many people have seen it

Circulating in small circles. Chinese X GitHub recommendation accounts (GitHubDaily, 周览资源, AI磊叔, Gk, and possibly 徐伦) posted it over several rounds, and a crowd of skill aggregators auto-indexed it, plus one Japanese hands-on article. No media coverage, no big English accounts reposted it, and no trace on HN or Product Hunt. It's open source and free, so there's no revenue.
Copycats: no clear case of copying found. Later same-direction projects include sebastianhaba/book-to-course-skill (10-03,76 stars) and ai-azhou/pdf-to-interactive-web (10-22,90 stars); vluz.eu reposted it verbatim; it has 124 forks, but none of them grew into anything significant.

7. What to borrow

Repackage an MIT base with a "trust" selling point: combine upstream book-to-skill and book-to-webpage, add source layering and audits, and credit the origins clearly in NOTICE.md. This is faster and more compliant than building from scratch.
Write the selling point as the user's own anxiety, "which sentences are from the book and which did the AI make up," so recommendation accounts can restate it directly.
Use well-known materials for README screenshots (celebrity biographies, investment bank reports, classic papers), giving recommendation accounts ready-made images.
For Chinese open-source projects seeking stars, the single most effective channel is being covered by accounts like GitHubDaily. After one feature, reposts from other accounts often follow one or two weeks later.

8. Uncertain

The sources of the 9-18~20 (+171), 10-12 (+48), and 10-24~26 (+102) peaks are not confirmed; the date of 徐伦's (@Jolyne_AI) post is unverified.
Whether recommendation posts were voluntary submissions, paid placements, or picked by the account owners cannot be determined from the evidence.
Reddit post dates and engagement numbers are unavailable (blocked).
Spread on Xiaohongshu, WeChat Official Accounts, and Bilibili could not be checked (search APIs don't cover them). We only know GitHubDaily has a WeChat Official Account of the same name.
The stargazer breakdown was rate-limited by the GitHub API this time, so we could not split it by day or check for star-farming accounts. Still, forks/stars are about 9.4%, and the star spikes line up with post times, which doesn't look like manufactured stars.

This article was written by AI from public data and the data we collect daily. Numbers are sourced, and inferences are labeled.