Skill 详情
study
General guided learning, tutoring, quizzing, and study-queue workflow.
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SKILL.md
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--- name: study description: "A personal, cross-project learning queue and tutor. Capture topics you want to understand deeply into a dumb home-dir backlog (~/.study/topics.md) from anywhere, then on demand run a guided, canvas-driven, recall-checked deep dive on any one of them. Topics can be grounded in your real code, in a sandbox the agent scaffolds, or be purely conceptual (\"investing\", \"solving a rubik's cube\"). Use whenever the user wants to learn or understand something deeply, park a rabbit hole for later, or be taught/quizzed — \"add X to my study list\", \"I want to understand Y\", \"teach me Z\", \"quiz me on…\", \"let's do a deep dive\", \"what's on my study queue\", \"explain how our <thing> actually works\" — and as the place other skills (e.g. better-planning-sync, better-planning-comprehend) send knowledge gaps. Works standalone; uses the canvas skill for interactive lessons when installed." --- # Study Relying on agents to write code (and to answer everything) quietly erodes understanding: the rabbit holes you'd once have chased — "wait, how *does* RRULE expansion work?" — get skipped to stay on task, and the gap compounds until you're a spectator to your own systems. `study` is the counter-habit. It splits learning into two cheap halves: **capture now** (a frictionless backlog of things worth understanding) and **learn later** (a guided, tested deep dive when you actually have the time). It's general — code is its strength, not its limit; "investing" and "the Krebs cycle" belong on the same queue. ## The home: a dumb queue, the filesystem as state Everything lives under `~/.study/` (full protocol in `references/study-layout.md` — read it before touching the files): - **`~/.study/topics.md` is pure backlog** — one topic per line, nothing else. A bare topic, or a topic plus free-form context if the author felt like typing it. **No status tags, no dates, no machine fields, no parsing.** It stays short and human; anything (including you, by hand) can append a line. - **A topic's state *is* its directory.** No `~/.study/<slug>/` ⇒ not started (just a queue line). Directory exists ⇒ picked up. Done ⇒ recorded *inside* the dir (the learning record marks it). Read state by looking at the filesystem, never by parsing the queue. - **Picking a topic up graduates its line out** of `topics.md` into a fresh `~/.study/<slug>/` workspace that seeds the dive. The queue shrinks as you act; a topic is never in two places. ## The four verbs The user's intent maps to one of these — do the one they asked for; don't force a dive when they only wanted to capture. ### capture — append a line "Add X to my study list", or a topic surfaced mid-conversation worth parking. Append one line to `~/.study/topics.md` (create the file on first use). Keep the human's words; attach free-form context only if it's cheap and useful (e.g. the repo + path the question came from), never machine tags. Confirm in a sentence; do **not** start teaching. This is the GTD capture — get it out of the head and move on. ### browse — show the queue, help pick "What's on my study queue?" Show `topics.md` (and, if asked, the started/finished dirs from `ls ~/.study/*/`). Help choose what to learn now — by what's quick, what's blocking real work, or what the user's in the mood for. Recommend one, but it's their call. ### learn — scaffold and run the dive The user picks a topic (from the queue or fresh). Derive a slug, scaffold `~/.study/<slug>/`, move the topic line + its context out of `topics.md` into the workspace seed, then run the dive (next section). If a workspace already exists for the slug, resume from its learning record instead of starting over. ### record — capture what stuck, mark done On the way out of a dive (or when the user says they're done), write/append the learning record: what clicked, what's still fuzzy, the next thing to chase. Marking it done lives **in the dir**, not the queue. The record is both the retention aid在 GitHub 阅读完整来源 (打开外部页面)