Keyword Extractor

Keyword Extractor

Extracts max 50 relevant keywords from text and formats them in a strict machine-ready structure.

QUICK START

Jump to any section:

1. CORE MANDATE – Output rules and formatting
2. WHEN TO USE – Trigger conditions for this skill
3. KEYWORD QUALITY RULES – Priorities and forbidden keywords
4. WORKFLOW – Step-by-step generation and processing
5. 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:

1. main topic of the text
2. high-value domain terminology
3. technologies, tools, or entities mentioned
4. common search queries related to the topic
5. 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.

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior decisions and patterns to avoid re-discovering solutions. Cache results for instant retrieval in future sessions.

# Check for prior development context before starting
python3 execution/memory_manager.py auto --query "prior work and patterns related to Keyword Extractor"

Storing Results

After completing work, store development decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Completed task with key insights documented for future reference" \
  --type decision --project <project> \
  --tags keyword-extractor default

Multi-Agent Collaboration

Share outcomes with other agents so the team stays aligned and avoids duplicate work.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Task completed — results documented and shared with team" \
  --project <project>