Agent skill · Backend & API

gemini-embeddings

Generate text embeddings using Gemini Embedding API via scripts/. Use for creating vector representations of text, semantic search, similarity matching, clustering, and RAG applications. Triggers on "embeddings", "semantic search", "vector search", "text similarity", "RAG", "retrieval".

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill gemini-embeddings-akrindev-google-studio-skills --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 10 KB
Bundled scripts: none
Version: 1.0.0
Path: skills/ai-llm/gemini-embeddings-akrindev-google-studio-skills/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Gemini Embeddings Generate high-quality text embeddings for semantic search, similarity analysis, clustering, and RAG (Retrieval Augmented Generation) applications through executable scripts. ## When to Use This Skill Use this skill when you need to: - Find semantically similar documents or texts - Build semantic search engines - Implement RAG (Retrieval Augmented Generation) - Cluster or group similar documents - Calculate text similarity scores - Power recommendation systems - Enable semantic document retrieval - Create vector databases for AI applications ## Available Scripts ### scripts/embed.py **Purpose**: Generate embeddings and calculate similarity **When to use**: - Creating vector representations of text - Comparing text similarity - Building semantic search systems - Implementing RAG pipelines - Clustering documents **Key parameters**: | Parameter | Description | Example | |-----------|-------------|---------| | `texts` | Text(s) to embed (required) | `"Your text here"` | | `--model`, `-m` | Embedding model | `gemini-embedding-001` | | `--task`, `-t` | Task type | `SEMANTIC_SIMILARITY` | | `--dim`, `-d` | Output dimensionality | `768`, `1536`, `3072` | | `--similarity`

What's inside
Steps it walks through
  1. When to Use This Skill
  2. Available Scripts
  3. scripts/embed.py
  4. Workflows
  5. Workflow 1: Single Text Embedding
  6. Workflow 2: Semantic Search
  7. Workflow 3: Text Similarity Comparison
  8. Workflow 4: Dimensionality Reduction for Efficiency
  9. Workflow 5: Document Clustering
  10. Workflow 6: RAG Implementation
  11. Workflow 7: JSON Output for API Integration
  12. Workflow 8: Batch Document Processing
  13. Parameters Reference
  14. Task Types
Ships with 1 file
  • metadata.json
Commands it runs
python scripts/embed.py "What is the meaning of life?"
python scripts/embed.py "best practices for coding" --task RETRIEVAL_QUERY > query.json
python scripts/embed.py "Coding best practices include version control" "Clean code is essential" --task RETRIEVAL_DOCUMENT > docs.json
python scripts/embed.py "What is the meaning of life?" "What is the purpose of existence?" "How do I bake a cake?" --similarity
python scripts/embed.py "Text to embed" --dim 768
python scripts/embed.py "Machine learning is AI" "Deep learning is a subset" "Neural networks power AI" --json > embeddings.jsonl
Use scikit-learn, KMeans, etc.
python scripts/embed.py "Document 1 content" "Document 2 content" --task RETRIEVAL_DOCUMENT --dim 1536
python scripts/embed.py "User query here" --task RETRIEVAL_QUERY
python skills/gemini-text/scripts/generate.py "Context: [retrieved docs]. Answer: [user query]"
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About this skill
What does the gemini-embeddings skill do?

Generate text embeddings using Gemini Embedding API via scripts/. Use for creating vector representations of text, semantic search, similarity matching, clustering, and RAG applications. Triggers on "embeddings", "semantic search", "vector search", "text similarity", "RAG", "retrieval".

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill gemini-embeddings-akrindev-google-studio-skills --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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