nous-hermeshub Keyword Extractor
Extracts max 50 relevant keywords from text and formats them in a strict machine-ready structure.
QUICK START
Jump to any section:
- CORE MANDATE – Output rules and formatting
- WHEN TO USE – Trigger conditions for this skill
- KEYWORD QUALITY RULES – Priorities and forbidden keywords
- WORKFLOW – Step-by-step generation and processing
- FAILURE HANDLING – Short text or edge cases
CORE MANDATE
Return exactly one comma-separated line of keywords, following these rules:
- max 50 keywords
- ordered by relevance
- all lowercase
- no duplicates or near-duplicates
- mix of single words and 2–4 word phrases
- no numbering, bullets, explanations, or trailing period
When to Use
Use this skill when the user wants to generate or extract SEO-friendly keywords or tags from text including:
- Extracting keywords or tags for any given text or paragraph
- Creating comma-separated keywords or tags suitable for SEO, search, or metadata
- Generating topic-specific keywords or tags based on the content’s main subjects and concepts
This skill should be triggered for all text-based keyword extraction requests, regardless of phrasing, as long as the goal is SEO, tagging, or metadata generation.
Do NOT trigger this skill for:
- Summaries or paraphrasing requests
- Text analysis without keyword generation
KEYWORD QUALITY RULES
Prefer noun phrases over verbs or adjectives. Prefer keywords useful for:
- SEO and search
- tagging
- metadata
Prioritize:
- domain terminology
- meaningful nouns
- search phrases
- entities
- technical concepts
Avoid weak keywords like:
- things and various topics
- general concepts
- important ideas
- methods
IMPORTANT: Each keyword must strictly represent a phrase that a user would type into a search engine
WORKFLOW
Step 1 — Analyze
Identify:
- main subject
- key topics
- domain terminology
- entities
- concepts
Ignore filler words.
Step 2 — Generate Keywords
Generate up to 50 strictly SEO-friendly keywords directly from the text.
Include:
- core topics
- domain terminology
- related concepts
- common search queries
Allowed formats:
- single words
- 2 word phrases
- 3 word phrases
- 4 word phrases
Example:
machine learning, neural networks, deep learning models, ai algorithms, data science tools
Avoid vague keywords, filler phrases, adjectives without nouns like:
important methods, different ideas, various techniques, things
Keywords must not exceed 4 words.
Step 3 — Rank
Order keywords by SEO importance using these signals:
- main topic of the text
- high-value domain terminology
- technologies, tools, or entities mentioned
- common search queries related to the topic
- supporting contextual topics
Most important keywords should always appear first.
Step 4 — Normalize
Ensure:
- lowercase, comma separated, no duplicates
- ≤50 keywords
- Remove near-duplicate keywords that represent the same concept.
- Keep only the most common search phrase.
- If two keywords represent the same concept, keep only the more common search phrase.
Step 5 — Validate
Before returning output ensure:
- keyword_count <= 50
- no duplicates and near-duplicates
- all lowercase and comma separated
- no trailing period
- each keyword is a clear searchable topic
- keywords do not exceed 4 words
If any rule fails regenerate the list.
FAILURE HANDLING
If text is very short, infer likely topics and still generate keywords. Never exceed 50 keywords.
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.