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

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:

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:

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

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