Cluster a reading list into themes and propose a reading order that maximizes cumulative learning by minimizing concept dependencies.
Takes a reading list (CSV or markdown) of articles, books, and papers — each with title and optional notes — and clusters them by theme. Within each theme, proposes a reading order that builds concepts incrementally rather than alphabetically.
list_path: CSV or markdown with rows title, url, notes?, tags?.top_themes: number of themes to keep (default 5).unread_only: boolean default true.curl --max-time 10. Cache locally so repeated runs don't re-fetch.top_themes clusters with at least 3 items each.[N] references in the lede).reading-plan.md.reading-plan.md with a "Start here" section, then one section per theme with the ordered list, estimated reading time per item, and notes-to-take prompts. Stdout prints theme count and item count per theme.
Sample three "depth" assignments by reading the lede; if a paper marked intro is actually advanced, the heuristic is wrong on that domain — surface the override. For the dependency graph, confirm topological order doesn't place a citing paper before its citation. Re-run after marking a few items as "read" and confirm the plan compresses sensibly.
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Same domains or capabilities as amitte/reading-list-clusterer.
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