SKILL.md
---
name: tag finder
description: assigns hierarchical academic tags using deep analytical reasoning. adapts taxonomy depth to note context and provides transparent reasoning for every classification decision.
---
# ADAPTIVE HIERARCHICAL TAGGING PROTOCOL
## CORE PHILOSOPHY
**Tagging is reasoning, not labeling.**
We're not filing notes into pre-existing boxes—we're discovering where they belong in the landscape of human knowledge. Every tag assignment is:
- **Contextual**: Depends on the note's purpose and surrounding knowledge
- **Debatable**: Multiple valid perspectives may exist
- **Evolving**: Can change as understanding deepens
> "The map is not the territory, but a well-reasoned map helps navigate the territory."
---
## THE GOLDEN RULE: MACRO TO MICRO (Flexible Framework)
**Base Format:** `Academic-Discipline/Sub-discipline/Specific-Topic/[Granular-Detail]`
But remember: **This is a guide, not a prison.**
### Depth Decision Matrix
| Scenario | Recommended Depth | Reasoning |
| --------------------------------- | ---------------------- | --------------------------------------------------------------- |
| **Foundational concept** | 2 levels | `Physics/Thermodynamics` — Established, well-bounded topic |
| **Standard technical topic** | 3 levels | `Computer-Science/Algorithms/Sorting` — Clear disciplinary home |
| **Specialized methodology** | 4 levels | `Biology/Genetics/Genomics/CRISPR` — Requires context chain |
| **Emerging/hybrid concept** | 2-3 levels + multi-tag | Might not fit cleanly; err toward flexibility |
| **Meta-topic (tools, practices)** | Custom structure | May need `Methodology/` or `Tools/` prefix |
**Key Principle:** Depth should illuminate, not obfuscate. If a fifth level adds genuine specificity, use it. If it's just noise, stop at three.
---
## REASONING FRAMEWORK
Before assigning tags, walk through this reasoning process:
### Step 1: IDENTIFY THE NOTE'S EPISTEMIC NATURE
Ask: **What kind of knowledge is this?**
| Knowledge Type | Characteristics | Tag Approach |
| ---------------------------- | ------------------------ | -------------------------------------------------- |
| **Foundational Concept** | Defines basic principles | Root in primary discipline |
| **Applied Technique** | Implements concepts | Include methodology/application layer |
| **Interdisciplinary Bridge** | Connects fields | Multi-tag with clear primary |
| **Tool/Framework** | Enables work | May need `Methodology/` or tool-specific structure |
| **Historical/Contextual** | About the field itself | Consider meta-level tags |
| **Emergent/Cutting-edge** | New, not yet categorized | Be conservative; use broader tags |
**Example:**
```
Note: "Transformer Architecture"
Reasoning:
- Core nature? Technical architecture (applied technique)
- Origin? Research from NLP/Deep Learning
- Current status? Foundational to modern AI
- Decision: 4-level tag to capture evolution from theory to architecture
Tag: Computer-Science/Artificial-Intelligence/Deep-Learning/Transformers
```
### Step 2: MAP DISCIPLINARY LINEAGE
Ask: **What's the intellectual ancestry?**
Trace backwards from specific → general:
1. What specific thing is this? (leaf)
2. What broader category contains it? (branch)
3. What field studies that category? (sub-discipline)
4. What academic domain owns that field? (root)
**Example:**
```
Note: "CRISPR-Cas9 Ethics"
Backward trace:
1. Specific: CRISPR-Cas9 (gene-editing tool)
2. Broader: Gene editing techniques
3. Field: Genomics (within Genetics)
4. Domain: Biology
But wait—ethics layer!
→ This is interdisciplinary
Primary tag: Biology/Genetics/Genomics/CRISPR
Secondary tag: Philosophy/Ethics/Applied-Ethics/Bioethics
Reasoning: The note studies CRISPR through ethical lens, so Biology is primary (the object of study) and Ethics is secondary (the analytical framework).
```
### Step 3: EVALUATE INTERDISCIPLINARY COMPLEXITY
Ask: **Does this concept live in multiple worlds?**
| Indicator | Action |
| ----------------------------------------------------- | ---------------------------------------------------- |
| Concept originated in Field A but now used in Field B | Primary: Origin field / Secondary: Application field |
| Equal contribution from multiple fields | Multiple co-equal tags |
| Field A studying Field B | Primary: Field A / Reference Field B in sub-levels |
| Meta-analysis across fields | Consider `Methodology/Interdisciplinary-Studies` |
**Example:**
```
Note: "Neural Networks for Drug Discovery"
Analysis:
- Neural Networks: CS/AI technique
- Drug Discovery: Biology/Pharmacology goal
Interdisciplinary type: Tool from Field A applied to Field B
Tags:
- Computer-Science/Artificial-Intelligence/Machine-Learning/Neural-Networks
- Biology/Pharmacology/Drug-Discovery
Reasoning: Primary tag reflects the technical method; secondary reflects application domain. If note focuses more on biological insights than ML technique, reverse the priority.
```
### Step 4: ASSESS TAXONOMY MATURITY
Ask: **How established is this concept?**
| Maturity Level | Tag Strategy |
| ------------------------------------------------- | --------------------------------------------- |
| **Canonical** (in textbooks for 20+ years) | Use standard academic hierarchy |
| **Established** (widespread in journals/practice) | Follow field conventions |
| **Emerging** (active research, no consensus) | Use broader tags, avoid premature specificity |
| **Speculative** (blog posts, tweets, hype) | Tag the underlying established concepts |
**Example:**
```
Note: "GPT-4 Prompt Injection Attacks"
Maturity assessment:
- GPT-4: Very new (2023)
- Prompt Engineering: Emerging (2020s)
- Security vulnerabilities: Established
Decision: Tag using established concepts, not bleeding-edge labels
Conservative tag:
Computer-Science/Artificial-Intelligence/Natural-Language-Processing/Security
Alternative (if focusing on prompt engineering):
Computer-Science/Artificial-Intelligence/Prompt-Engineering
Reasoning: "Prompt injection" is too new and unstable as terminology. Anchor in established security or NLP concepts, then add emergent layer if needed.
```
---
## ADAPTIVE PATTERNS
### Pattern 1: THE UMBRELLA TERM PROBLEM
**Scenario:** Note discusses a broad concept that could be tagged at multiple specificity levels.
**Example:** "Introduction to Machine Learning"
**Options:**
```yaml
# Option A: Broad (appropriate for survey/intro)
tags:
- Computer-Science/Artificial-Intelligence/Machine-Learning
# Option B: Specific (if focusing on sub-areas)
tags:
- Computer-Science/Artificial-Intelligence/Machine-Learning/Supervised-Learning
- Computer-Science/Artificial-Intelligence/Machine-Learning/Unsupervised-Learning
# Option C: Meta-level (if about ML as a field)
tags:
- Computer-Science/Artificial-Intelligence/Machine-Learning
- Methodology/Research-Methods
```
**Decision framework:**
- Introductory/survey content → Broader tag
- Deep dive into specific technique → More specific tag
- Epistemological/historical → Add meta-tag
### Pattern 2: THE TOOL vs. CONCEPT DILEMMA
**Scenario:** Note is about a tool that implements concepts.
**Example:** "TensorFlow Tutorial"
**Reasoning:**
```
Is this about:
A) The software tool itself? → Computer-Science/Tools/Machine-Learning-Frameworks
B) ML concepts via TensorFlow? → Computer-Science/Machine-Learning/[specific-topic]
C) Software engineering? → Computer-Science/Software-Engineering/Libraries
Decision: Depends on note's focus
- If explaining how to install/use TensorFlow → Tools tag
- If using TensorFlow to teach neural networks → Neural-Networks tag
- If comparing frameworks → Software-Engineering tag
```
### Pattern 3: THE HISTORICAL vs. TECHNICAL SPLIT
**Scenario:** Note discusses the history or sociology of a technical field.
**Example:** "The AI Winter of the 1980s"
**Options:**
```yaml
# Pure historical approach
tags:
- History/History-of-Science/Computer-Science
- Computer-Science/Artificial-Intelligence
# Science-and-society approach
tags:
- Sociology/Science-and-Technology-Studies
- Computer-Science/Artificial-Intelligence
# Field-internal approach
tags:
- Computer-Science/Artificial-Intelligence
- Methodology/Research-History
```
**No single right answer**—choose based on the note's analytical lens.
---
## SPECIAL CASES & EDGE CASES
### Case 1: Personal Knowledge Management Notes
**Example:** "My System for Reading Papers"
**Challenge:** Not strictly academic content, but about academic practice.
**Solution:**
```yaml
tags:
- Methodology/Knowledge-Management/Reading-Systems
- Methodology/Research-Methods/Literature-Review
```
**Reasoning:** Create a `Methodology/` root for meta-practices. This is a legitimate academic concern (studied in library science, cognitive science, education).
### Case 2: Colloquial Terms for Technical Concepts
**Example:** Note titled "AI Hallucinations"
**Challenge:** "Hallucination" is colloquial jargon for "generation errors" or "factual inconsistencies."
**Solution:**
```yaml
tags:
- Computer-Science/Artificial-Intelligence/Natural-Language-Processing/Model-Evaluation
- Computer-Science/Artificial-Intelligence/Machine-Learning/Reliability
```
**Reasoning:** Tag the underlying technical concept, not the slang. Could add informal alias in note metadata.
### Case 3: Compound Concepts
**Example:** "Quantum Machine Learning"
**Analysis:**
```
This is genuinely interdisciplinary—not just ML applied to quantum problems,
but using quantum computing principles for ML algorithms.
Options:
A) Emphasize quantum: Physics/Quantum-Computing/Applications/Machine-Learning
B) Emphasize ML: Computer-Science/Machine-Learning/Quantum-Methods
C) Both: Use multiple tags
```
**Recommendation:** Use both tags with clear reasoning about which is primary based on note content.
### Case 4: Framework/Theory/Model Naming
**Example:** "Actor-Critic Algorithms"
**Decision tree:**
```
Is this:
- A specific algorithm? → Include algorithm name in tag path
- A class of algorithms? → Stop at category level
- A theoretical framework? → May need Theory/Framework sub-level
Result: Computer-Science/Artificial-Intelligence/Reinforcement-Learning/Actor-Critic
Reasoning: Actor-Critic is a recognized sub-category of RL algorithms,
not just a single implementation.
```
---
## OUTPUT PROTOCOL
When assigning tags, provide:
### 1. RECOMMENDED TAG(S)
```yaml
tags:
- [Primary tag with full path]
- [Secondary tag if applicable]
```
### 2. REASONING CHAIN
Explain your thought process:
- What is the core concept?
- What discipline does it originate from?
- What's the appropriate depth for this specific note?
- Are there interdisciplinary considerations?
- Why did you choose this structure over alternatives?
### 3. ALTERNATIVES & TRADEOFFS
If multiple valid approaches exist, present them:
```
Alternative A: [tag] — Better if [condition]
Alternative B: [tag] — Better if [condition]
```
### 4. CONFIDENCE LEVEL
Be transparent about certainty:
- **High confidence**: Canonical, textbook concept
- **Medium confidence**: Reasonable interpretation, defensible
- **Low confidence**: Emerging topic, multiple valid framings
- **Uncertain**: Request clarification about note's focus
---
## EXAMPLE: FULL REASONING PROCESS
**User asks:** "Help me tag my note about 'Attention Mechanisms in Transformers'"
**Response:**
```yaml
tags:
- Computer-Science/Artificial-Intelligence/Deep-Learning/Attention-Mechanisms
```
### Reasoning Chain:
**1. Core concept identification:**
- This is about a specific technical mechanism (attention) within a specific architecture (Transformers)
- Attention is the defining innovation of Transformers, so both are important
**2. Disciplinary lineage:**
- Root: Computer Science (clear university department)
- Branch: Artificial Intelligence (field of study)
- Sub-branch: Deep Learning (paradigm within AI/ML)
- Leaf: Attention Mechanisms (specific technique)
**3. Depth justification:**
- 4 levels is appropriate here because:
- Level 1-2: Establishes academic grounding
- Level 3: Specifies the ML paradigm (distinguishes from symbolic AI, classical ML)
- Level 4: Names the specific mechanism being studied
- Could go to 5 levels with `/Transformers/Attention`, but that risks over-specification since attention is broader than just Transformers
**4. Interdisciplinary check:**
- This is purely technical CS/AI content
- No secondary tags needed unless note discusses applications (e.g., linguistics, vision)
**5. Alternative considerations:**
**Alternative A:** `Computer-Science/Artificial-Intelligence/Natural-Language-Processing/Transformers`
- **When to use:** If note focuses on Transformers as NLP architecture rather than attention mechanism itself
- **Tradeoff:** More specific to domain (NLP), less focused on the core innovation
**Alternative B:** `Computer-Science/Artificial-Intelligence/Machine-Learning/Neural-Networks/Attention`
- **When to use:** If treating attention as a general neural network component
- **Tradeoff:** Less specific about deep learning context
**Confidence level:** High
- Attention mechanisms are well-established (2017+)
- Clear academic consensus on taxonomy
- Standard terminology in literature
**Would recommend Alternative A instead if:**
- Note emphasizes BERT, GPT, or other specific Transformer models
- Focus is on NLP applications rather than mechanism itself
- Note discusses linguistic capabilities enabled by attention
---
## ANTI-PATTERNS (Revisited with Nuance)
| Pattern | Why It Seems Wrong | When It Might Be Right | Resolution |
| ------------------------------- | ------------------- | ------------------------------------ | ---------------------------------------------- |
| Multiple tags at same depth | Seems redundant | Genuinely interdisciplinary topics | Use both if note bridges fields equally |
| Very deep hierarchy (6+ levels) | Over-specification | Highly specialized research notes | Acceptable if each level adds real specificity |
| Shallow tag for complex topic | Under-specification | Broad survey or intro content | Appropriate for overview notes |
| Custom top-level category | Breaks convention | Meta-topics, tools, personal systems | Use `Methodology/` or `Tools/` roots |
---
## SELF-REFLECTION PROMPTS
Before finalizing tags, ask yourself:
1. **Clarity test:** Could someone unfamiliar with the note understand what it's about from the tags alone?
2. **Retrieval test:** If I wanted to find this note in 6 months, what would I search for?
3. **Consistency test:** Have I tagged similar notes differently? If so, is there good reason?
4. **Granularity test:** Am I at the right zoom level, or too zoomed in/out?
5. **Future-proof test:** Will this tag structure still make sense if the field evolves?
---
## MAJOR ACADEMIC DISCIPLINES (Living Reference)
This list guides but doesn't constrain. If a concept doesn't fit cleanly, that's data—not failure.
| Discipline | Common Sub-fields | Notes |
| -------------------- | ---------------------------------------------------- | --------------------------------------------------- |
| **Computer-Science** | AI, Algorithms, Systems, HCI, Security, Networks | Often interdisciplinary with Math, Engineering |
| **Mathematics** | Algebra, Analysis, Statistics, Topology, Logic | Pure vs Applied distinction matters |
| **Physics** | Mechanics, Thermodynamics, Quantum, Electromagnetism | Historical vs modern physics differ in organization |
| **Biology** | Genetics, Ecology, Neuroscience, Evolutionary | Molecular vs organismal levels |
| **Chemistry** | Organic, Inorganic, Biochemistry, Physical | Overlaps heavily with Biology, Physics |
| **Psychology** | Cognitive, Clinical, Social, Developmental | Empirical science vs applied practice |
| **Economics** | Micro, Macro, Behavioral, Econometrics | Positive vs normative economics |
| **Philosophy** | Ethics, Epistemology, Metaphysics, Logic | Can be meta-tag for any field |
| **History** | Ancient, Medieval, Modern, Regional | Also: History of Science, Economic History, etc. |
| **Engineering** | Electrical, Mechanical, Civil, Software | Applied sciences with disciplinary roots |
| **Business** | Marketing, Finance, Management, Strategy | Applied social science |
| **Linguistics** | Syntax, Semantics, Phonology, Computational | Bridging humanities and CS |
| **Sociology** | Social-Theory, Methods, Specialized-Fields | Often studies other disciplines |
| **Methodology** | Research-Methods, Knowledge-Management, Statistics | Meta-level, applies across fields |
---
## FINAL PRINCIPLE: EMBRACE UNCERTAINTY
**Perfect tags don't exist.** Good tags:
- Reflect current understanding
- Facilitate retrieval
- Respect disciplinary conventions
- Remain open to revision
When in doubt:
1. Choose the most defensible option
2. Explain your reasoning
3. Flag uncertainty
4. Suggest when to revisit
**The goal is useful navigation, not absolute truth.**
---
## References
- [Obsidian Tags Documentation](https://help.obsidian.md/tags)
- [Obsidian Properties and Frontmatter](https://help.obsidian.md/properties)
- [Library of Congress Classification](https://www.loc.gov/catdir/cpso/lcco/)
- [ACM Computing Classification System](https://dl.acm.org/ccs)