article-tag-enhancer

name	article-tag-enhancer
description	Analyze markdown article content and add missing relevant tags to frontmatter. Uses kebab-case format for multi-word tags (e.g., python-venv). Only adds tags that are clearly relevant and not already present. Automatically invokes article-post-edit-actions skill after completion.

Article Tag Enhancer

Analyze article content and add missing relevant tags to improve discoverability and categorization.

When to Use

• User asks to add tags to an article
• User wants to improve article discoverability
• Article has few or generic tags that don't cover the content well
• Preparing an article for publication with proper tagging

Inputs

• file_path: Path to the markdown article file
• max_new_tags (optional): Maximum number of new tags to add (default: 5)

Tag Format Rules

Rule	Example
All lowercase	machine-learning not Machine-Learning
Multi-word: kebab-case	python-venv not python_venv or pythonvenv
Single word: as-is	python, docker, kubernetes
Acronyms: lowercase	llm, rag, api, cli
Version numbers: with dash	python-3, gpt-4

Process

Step 1: Read and Parse Article

1. Read the markdown file
2. Parse front matter (YAML between --- markers)
3. Extract existing tags (may be list or comma-separated)
4. Store existing tags in normalized form (lowercase, trimmed)

Step 2: Analyze Article Content

Extract tag candidates from:

Title and Headings:

• Article title
• All ## and ### headings
• Look for technology names, concepts, methodologies

Content Analysis:

• Code block languages (python, bash, etc.)
• Library/framework mentions (pandas, langchain, fastapi)
• Concept mentions (embedding, vector-database, fine-tuning)
• Tool mentions (docker, kubernetes, git)
• Methodology mentions (rag, few-shot, zero-shot)

Category Context:

• Use category to inform relevant domain tags
• E.g., "Generative AI" category suggests LLM-related tags

Step 3: Build Tag Candidates

High-confidence indicators:

• Mentioned 3+ times in content
• Appears in title or multiple headings
• Is a code block language
• Is a well-known technology/framework name

Tag categories to consider:

Category	Examples
Languages	python, javascript, rust, go
Frameworks	fastapi, flask, langchain, pytorch
Concepts	machine-learning, deep-learning, nlp
Tools	docker, kubernetes, git, vscode
Methodologies	rag, fine-tuning, prompt-engineering
Domains	data-science, web-development, devops
Formats	api, cli, rest, graphql

Step 4: Filter Candidates

Remove candidates that:

• Already exist in tags (case-insensitive match)
• Are too generic (e.g., "code", "programming", "tutorial")
• Are too specific/obscure (mentioned only once in passing)
• Are variations of existing tags (e.g., don't add ml if machine-learning exists)

Synonym/variation mapping:

If exists	Don't add
machine-learning	ml, machinelearning
python	py, python3
javascript	js, node
llm	large-language-model
rag	retrieval-augmented-generation
api	rest-api, web-api

Step 5: Rank and Select Tags

Ranking criteria (in order):

1. Frequency of mention in content
2. Presence in title or headings
3. Specificity (more specific = better)
4. Relevance to category

Selection rules:

• Maximum 5 new tags by default (configurable)
• Prefer specific over generic
• Ensure diversity (don't add 5 similar tags)
• Must be clearly relevant to article content

Step 6: Format New Tags

Convert all candidates to proper format:

"Python Virtual Environment" → "python-virtual-environment"
"LangChain" → "langchain"
"RAG Pipeline" → "rag-pipeline"
"GPT-4" → "gpt-4"
"REST API" → "rest-api"
"t-SNE" → "t-sne"

Formatting rules:

1. Convert to lowercase
2. Replace spaces with hyphens
3. Remove special characters except hyphens
4. Collapse multiple hyphens to single
5. Trim leading/trailing hyphens

Step 7: Update Front Matter

Add new tags to existing tags list:

Before:

tags:
  - python
  - machine-learning

After:

tags:
  - python
  - machine-learning
  - langchain
  - vector-database
  - embeddings

Rules:

• Append new tags after existing ones
• Maintain YAML list format
• Don't reorder existing tags
• Preserve any tag formatting style used

Step 8: Invoke Post-Edit Actions

After adding tags, invoke the article-post-edit-actions skill with:

• file_path: Same article file
• edit_descriptions:
  • "Added tags: tag1, tag2, tag3" (list all new tags)

Example Transformation

Article content excerpts:

Title: Techniques to Boost RAG Performance in Production
Category: Generative AI

...discusses hybrid search, re-ranking...
...using LangChain and ChromaDB...
...vector store optimization...

Before:

tags:
  - machine-learning
  - rag
  - llm

After:

tags:
  - machine-learning
  - rag
  - llm
  - langchain
  - chromadb
  - vector-database
  - hybrid-search
  - re-ranking

Output

• Modified article with new tags added
• List of tags added with brief justification
• Post-edit actions invoked for metadata updates

Success Criteria

• Only relevant tags added (clearly present in content)
• No duplicate tags (case-insensitive)
• Tags in correct kebab-case format
• Maximum tag limit respected
• Original tags preserved and unchanged
• YAML formatting valid
• Post-edit actions skill invoked
• Modified date updated

Edge Cases

No New Tags Needed

• If existing tags already cover content well, add none
• Report: "No additional tags needed - existing tags are comprehensive"

Ambiguous Technology Names

• Only add if clearly used as technology (not generic word)
• E.g., "Flask" the framework vs "flask" the container
• Check context: code blocks, imports, documentation links

Similar Existing Tags

• Don't add vector-store if vector-database exists
• Don't add neural-network if deep-learning exists
• Use judgment on semantic overlap

Many Potential Tags

• Prioritize by relevance and specificity
• Better to add 3 highly relevant tags than 5 marginally relevant

Tag Format Variations in Existing Tags

• Match the existing format style if possible
• If existing uses underscores, still use hyphens for new (standard format)

Common Tag Patterns

By Domain

Machine Learning / AI:

• machine-learning, deep-learning, neural-network
• nlp, computer-vision, reinforcement-learning
• pytorch, tensorflow, scikit-learn, keras

LLM / Generative AI:

• llm, gpt, chatgpt, claude
• langchain, llamaindex, openai
• rag, prompt-engineering, fine-tuning
• embeddings, vector-database, semantic-search

Data Science:

• pandas, numpy, data-analysis
• visualization, matplotlib, plotly
• statistics, data-cleaning, eda

DevOps / Infrastructure:

• docker, kubernetes, aws, gcp, azure
• ci-cd, github-actions, terraform
• monitoring, logging, observability

Python Development:

• python, fastapi, flask, django
• async, typing, testing, pytest
• poetry, pip, virtual-environment

Technology Detection Hints

Content Pattern	Suggested Tag
import langchain	langchain
from chromadb	chromadb
pip install X	Consider X as tag
docker run	docker
kubectl commands	kubernetes
.ipynb mentions	jupyter-notebook
async def	async
@pytest	pytest, testing

Notes

• This skill chains to article-post-edit-actions on completion
• Quality over quantity - fewer relevant tags is better than many marginal ones
• Consider the blog's existing tag taxonomy when adding tags
• Tags improve both human navigation and SEO/AI discoverability
• When in doubt, don't add the tag