Analyze a book (EPUB/PDF) and generate detailed chapter-by-chapter notes with extracted key concepts. Use when given a book file path to process.
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Spaced repetition and retrieval practice engine for the vault. Use when the user wants a review session, recall practice, to test what they remember, or to resurface notes. Triggers on "review session", "what should I review", "recall practice", "resurface notes", "spaced repetition", "quiz me", "what do I know about", "inbox triage", "surprise connection".
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
<Use_When>
<Do_Not_Use_When>
<Execution_Policy>
Candidate selection using MCP:
search_notes(query="processing_status: processed OR processing_status: evergreen", limit=50)
search_notes(query="type: term OR type: note", limit=50)
Or fall back to Grep:
Grep(pattern="processing_status: (processed|evergreen)", path="notes/", glob="*.md")
Grep(pattern="^type: (term|note)$", path="notes/", glob="*.md")
Filter and rank candidates:
updated_date older than 14 days — compute from today (2026-03-20)[ ](#anki-card) anchor (marked for spaced repetition)processing_status: processed over evergreen
(evergreen notes are maintained; processed ones need re-testing)notes/ml/, notes/psychology/, notes/startup/, etc.) and ensure the
final selection spans at least 3 different foldersRead the frontmatter to confirm updated_date and type:
get_frontmatter(path="notes/PATH/filename.md")
Find the oldest unprocessed source notes:
search_notes(query="processing_status: inbox", limit=20)
Or:
Grep(pattern="processing_status: inbox", path="notes/", glob="*.md")
Filter to types: paper, post, book, lecture, course.
Sort by created_date ascending — oldest first.
Pick 1–2.
Goal: find two notes from DIFFERENT domain folders that share a concept keyword but have NO wikilink between them.
Algorithm:
Grep(pattern="KEYWORD", path="notes/OTHER_DOMAIN/", glob="*.md")
Grep(pattern="\\[\\[.*TITLE.*\\]\\]", path="notes/SOURCE_NOTE.md")
Non-obvious pairings to prioritize (cross-domain > within-domain):
Before starting, show the session plan:
Review session — [N] cards queued
Retrieval practice ([count]):
1. [Note title] — [domain]
2. [Note title] — [domain]
...
Inbox triage ([count]):
• [Note title] ([type], [age] days old)
...
Surprise connection: [teaser — e.g., "a bridge between ML and psychology"]
Ready? I'll show you one card at a time. Type anything to start, or "skip [N]"
to skip a card.
Turn 1 — Prompt recall:
─────────────────────────────────────
Card [N/total] · [domain folder]
[[ (Type) Note Title ]]
What do you remember about this concept?
(Type your answer, or "skip" to reveal)
─────────────────────────────────────
Wait for user response.
Turn 2 — Reveal: Read the full note content:
read_note(path="notes/PATH/filename.md")
Or: Read(file_path="/absolute/path/to/note.md")
Show the note content (truncate at 400 words if very long — show key sections).
Then ask:
How did you do? Is this note still accurate?
[u] Update note [n] Next card [e] Evergreen (promote status)
If user wants to update: help them edit inline, then update updated_date
in frontmatter using update_frontmatter.
If user types "e" or "evergreen": update processing_status to evergreen.
─────────────────────────────────────
Inbox triage · [type] · [age] days old
[[ (Type) Note Title ]]
Read the note, show the first 5–7 bullets (or Abstract section for papers).
Then ask:
Worth processing deeply, or archive?
[p] Process (I'll run /process next) [a] Archive [s] Skip for now
p → note it as a recommendation; optionally update processing_status: processinga → update processing_status: archiveds → leave unchanged─────────────────────────────────────
Surprise connection
These two notes share the concept "[KEYWORD]" but don't link to each other:
[[ (Type) Note A ]] (domain: X)
[[ (Type) Note B ]] (domain: Y)
[Brief excerpt from each showing the shared concept]
Do you see a meaningful connection? What would the link say?
─────────────────────────────────────
Wait for user response. If they see a connection:
[[ (Type) Note B ]] into Note A's Related links sectionupdated_date on both notes─────────────────────────────────────
Session complete
Reviewed: [N] cards
Updated: [N] notes
Promoted to evergreen: [list]
Archived: [list]
Queued for /process: [list]
New connections made: [count]
Oldest unreviewed term: [title] ([N] days)
─────────────────────────────────────
Optionally suggest: "Run /process on [oldest inbox item] next."
</Steps> <Examples> <Good> User: "review session" 1. Build queue: 4 term notes (ml/, psychology/, startup/, finance/), 2 inbox papers, 1 surprise 2. Show overview → user starts 3. Card 1: "[[ (Term) Attention Mechanism ]]" — user recalls, sees note, updates a stale bullet 4. Card 2: "[[ (Term) Loss Aversion ]]" — user blanks, reads note, promotes to evergreen 5. Inbox: "(Paper) Chinchilla Scaling Laws" — 60 days old → user says archive 6. Surprise: "(Term) Gradient Descent" ↔ "(Term) Operant Conditioning" — shared keyword "reward signal" → user says "oh, both use incremental feedback to converge on a target behavior" → adds link 7. Summary: 6 reviewed, 2 updated, 1 evergreen, 1 archived, 1 connection made </Good> <Good> User: "quiz me on 3 cards" → Session with 3 retrieval cards (not 5), 1 inbox item, 1 surprise connection </Good> <Bad> Showing note content immediately without prompting recall first. → This destroys the retrieval practice effect. ALWAYS show title first, wait for response. </Bad> <Bad> Picking 4 cards all from notes/ml/. → Violates interleaving. Domains must be mixed. If ml/ only has enough candidates, pull from any other folder before repeating a domain. </Bad> </Examples><Escalation_And_Stop_Conditions>
$ARGUMENTS
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Analyze a book (EPUB/PDF) and generate detailed chapter-by-chapter notes with extracted key concepts. Use when given a book file path to process.
中文简介正在准备中。当前显示原文说明。
Process an online course into detailed vault notes — one per lecture, with slides and transcripts synthesized. Use this skill whenever the user shares a course URL, syllabus page, or lecture playlist and wants structured notes from it. Also triggers on "process this course", "take notes on these lectures", "course notes", any university course page, or when someone pastes a link to a course with multiple lectures/sessions. Even if the user just says "check out this course" or drops a .edu link with a schedule — use this skill.
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Deep, iterative, multi-source research that explores a topic from multiple perspectives, detects contradictions, and produces a comprehensive vault note with fact-level citations and a confidence map. Use this skill whenever the user says /deep-research, "deep research X", "research X thoroughly", "investigate X deeply", "I need to really understand X", or wants research that goes beyond a quick web search. Also use when the user wants to understand a complex, contested, or multi-faceted topic, compare competing approaches with evidence, or build a comprehensive mental model they can trust for decisions. This is heavier than /research — it searches web, academic papers, Reddit, and the vault, then loops through exploration and critique rounds until the evidence base is solid. When in doubt between /research and /deep-research, prefer this one if the topic is complex, contested, or the user wants depth over speed.
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Initialize an Obsidian vault with the agent-ready Zettelkasten system — creates directory structure, templates, CLAUDE.md, MCP config, and hooks
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Extract transcript and key slides from a local video file, then create a vault-formatted lecture note. Use this skill whenever a user provides a local video file (.mp4, .mov, .mkv, .avi, .webm) and wants notes from it. Triggers on "lecture", "take notes", "I have a recording of", "class video", "transcribe this presentation", "process this video for notes", or /lecture. Works with any language — auto-detects transcript language.
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Use when searching for academic papers related to a topic, finding papers similar to one already in the vault, or discovering research gaps. Triggers on "find papers", "related papers", "paper search", "literature", "what papers should I read about X".
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