tag finder skill

---
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)