Keyword Extractor

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/keyword-extractor

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

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

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• 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
• Summaries or paraphrasing requests
• Text analysis without keyword generation
• SEO and search

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. main subject
2. key topics
3. domain terminology
4. entities
5. concepts
6. core topics
7. related concepts

Imported Workflow Notes

Imported: Step 1 — Analyze

Identify:

• main subject
• key topics
• domain terminology
• entities
• concepts

Ignore filler words.

Imported: 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.

Imported: 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.

Imported: 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.

Imported: 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.

Imported: 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.

Examples

Example 1: Ask for the upstream workflow directly

Use @keyword-extractor to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @keyword-extractor against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @keyword-extractor for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @keyword-extractor using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: 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

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/keyword-extractor

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @base

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @calc

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @draw

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @image-studio

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a