Process

This skill is INTERACTIVE by design. The desirable-difficulty questions in Stage 3 are not optional ceremony — the friction IS the learning. The user's answers shape what gets extracted and how.

Stage 1: READ THE SOURCE NOTE

Ask the user which note to process if it's not clear from context. Then read it.

Verify the note type is a source type: paper, post, book, lecture, course, or a Clipping. If the note is a Term, Note, or Thought, stop and explain why this workflow doesn't apply.

Read the frontmatter and body:

mcp__obsidian-vault__read_note(path="path/to/note.md")

Or if MCP is unavailable:

Read("/absolute/path/to/note.md")

Stage 2: MARK AS PROCESSING

Update frontmatter to signal active work:

mcp__obsidian-vault__update_frontmatter(
  path="path/to/note.md",
  updates={"processing_status": "processing", "updated_date": "YYYY-MM-DD"}
)

Stage 3: ASK DESIRABLE-DIFFICULTY QUESTIONS

Do NOT skip this stage. Present these three questions and wait for the user's answers before proceeding:

Q1. In one sentence — what is the single most important idea in this note? (Don't look at the note. Reconstruct it from memory. That struggle is the point.)

Q2. How does this connect to something you already know? Name a specific vault note, or a concept from a completely different domain.

Q3. What surprised you, or pushed back against something you already believed?

Wait for the user's responses. Their answers determine:

• Which concepts are worth extracting (Q1 focus)
• Which wikilinks to create (Q2 connections)
• Where the note's real epistemic value lives (Q3 surprises)

If the user skips a question or gives a short answer, acknowledge it and move on — don't block the workflow.

Stage 4: VAULT CONCEPT SCAN

Search the vault thoroughly for concepts mentioned in the note. Use multiple strategies:

By keyword (MCP preferred):

mcp__obsidian-vault__search_notes(query="CONCEPT NAME", limit=10)

By keyword (Grep fallback):

Grep(pattern="concept name", path="notes/", glob="*.md", output_mode="files_with_matches", head_limit=15)

By type — check all Term notes in relevant domain:

Grep(pattern="type: term", path="notes/ml/", glob="*.md", output_mode="files_with_matches")

For each key concept extracted from the note (aim for 5–12 concepts), classify it:

Search ACROSS domain folders — a psychology concept may already exist under a different framing in notes/startup/ or notes/design/. Cross-domain matches are the most valuable finds.

Stage 5: PRESENT EXTRACTION PLAN

Present the plan clearly before creating anything:

## Existing concepts — add [[wikilinks]]
- [[(Term) Attention Mechanism]] — mention it in the Architecture section
- [[(Paper) Attention Is All You Need]] — link in Related links

## New concepts to extract
### 1. (Term) Flash Attention
- Type: Term
- Folder: notes/ml/
- Core idea: memory-efficient attention that avoids materializing the full N×N matrix
- Why it matters: enables training on longer sequences with same GPU budget
- Tags: #ml #attention #efficiency #MM-YYYY

### 2. (Note) IO-Aware Algorithm Design
- Type: Note (too broad for a Term)
- Folder: notes/computer science/
- Core idea: design algorithms around memory bandwidth, not just FLOPs
- Cross-domain angle: same principle appears in database query planning
- Tags: #systems #algorithms #MM-YYYY

## Questions this note raises
- Why doesn't standard PyTorch implement this by default?
- → Could become a (Question) note

Ask the user: "Which concepts should I create? (say 'all', list numbers, or adjust the plan)"

Stage 6: CREATE APPROVED NOTES

For each approved concept, create the note. Use this exact structure:

For Term notes:

---
id: YYYYMMDDHHMMSS
type: term
processing_status: processed
created_date: YYYY-MM-DD
updated_date: YYYY-MM-DD
---

# Concept Name [ ](#anki-card)

- **🏷️Tags** : #term #all-anki #topic-tag #MM-YYYY

[One sentence that captures the core intuition — lead with WHY it matters, not just what it is]

- [Bullet 1: concrete mechanism or definition]
- [Bullet 2: why it matters, what problem it solves]
- [Bullet 3: concrete example — make it visualizable]
- [Bullet 4: cross-domain connection or counterintuitive angle]

## Related
- Source: [[(Type) Source Note Title]]
- [[(Term) Related Concept]]

For Note entries (richer, multi-section):

---
id: YYYYMMDDHHMMSS
type: note
processing_status: processed
created_date: YYYY-MM-DD
updated_date: YYYY-MM-DD
---

# Concept Name

- **🏷️Tags** : #note #topic-tag #MM-YYYY

[ ](#anki-card)

[Lead insight — the one thing someone should remember]

## [Section 1]
- [bullets]

## [Section 2]
- [bullets]

## Related links
- Source: [[(Type) Source Note Title]]
- [[(Term) Related Concept]]

Generate the timestamp ID for each note:

date +%Y%m%d%H%M%S

Place notes in the appropriate subfolder under notes/. Match the domain:

• ML, AI → notes/ml/
• Papers → notes/paper/ (or stay in the source's folder)
• Psychology, cognition → notes/psychology/
• Business, startups → notes/startup/
• CS, algorithms → notes/computer science/
• Original ideas → notes/thoughts/

Quality gate before creating each note:

• Does the first bullet lead with insight, not context?
• Is there at least one concrete example?
• Is there a cross-domain link (explicit or implicit)?
• Could you cut 20% and lose nothing? If yes, cut it.

Stage 7: UPDATE THE SOURCE NOTE

After creating new notes, update the source note:

1. Add [[wikilinks]] inline where concepts are first mentioned — use the full (Type) Title prefix that matches the created filename exactly:

   • [[(Term) Flash Attention]] not [[Flash Attention]]
   • [[(Note) IO-Aware Algorithm Design]] not [[IO-Aware]]

2. Update frontmatter:

mcp__obsidian-vault__update_frontmatter(
  path="path/to/source-note.md",
  updates={"processing_status": "processed", "updated_date": "YYYY-MM-DD"}
)

3. Confirm with a brief summary:

Done. Created 3 notes, updated 2 wikilinks.

New notes:
- notes/ml/(Term) Flash Attention.md
- notes/computer science/(Note) IO-Aware Algorithm Design.md
- notes/ml/(Term) Tiling (GPU).md

Added wikilinks to source note for 5 concepts (2 existing, 3 new).

Cross-domain connection worth exploring:
- [[(Note) IO-Aware Algorithm Design]] ↔ database query planning (no vault note yet)

$ARGUMENTS