Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/ai-safeter/antigravity-cli-plugin/rag-implementationnpx skills add AI-Safeter/antigravity-cli-plugin --skill rag-implementationgit clone --depth 1 https://github.com/AI-Safeter/antigravity-cli-pluginWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00049 | $0.02775 |
| Opus 5 | $0.00024 | $0.01388 |
| Sonnet 5 | $0.00010 | $0.00555 |
| Haiku 4.5 | $0.00005 | $0.00278 |
Grade A, and why
rag-implementation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
86% identical to rag-implementation — 23 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 422 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Implementation
Master Retrieval-Augmented Generation (RAG) to build LLM applications that provide accurate, grounded responses using external knowledge sources.
Use this skill when
- Building Q&A systems over proprietary documents
- Creating chatbots with current, factual information
- Implementing semantic search with natural language queries
- Reducing hallucinations with grounded responses
- Enabling LLMs to access domain-specific knowledge
- Building documentation assistants
- Creating research tools with source citation
Do not use this skill when
- You only need purely generative writing without retrieval
- The dataset is too small to justify embeddings
- You cannot store or process the source data safely
Instructions
- Define the corpus, update cadence, and evaluation targets.
- Choose embedding models and vector store based on scale.
- Build ingestion, chunking, and retrieval with reranking.
- Evaluate with grounded QA metrics and monitor drift.
Safety
- Redact sensitive data and enforce access controls.
- Avoid exposing source documents in responses when restricted.
Core Components
1. Vector Databases
Purpose: Store and retrieve document embeddings efficiently
Options:
- Pinecone: Managed, scalable, fast queries
- Weaviate: Open-source, hybrid search
- Milvus: High performance, on-premise
- Chroma: Lightweight, easy to use
- Qdrant: Fast, filtered search
- FAISS: Meta's library, local deployment
2. Embeddings
Purpose: Convert text to numerical vectors for similarity search
Models:
- text-embedding-ada-002 (OpenAI): General purpose, 1536 dims
- all-MiniLM-L6-v2 (Sentence Transformers): Fast, lightweight
- e5-large-v2: High quality, multilingual
- Instructor: Task-specific instructions
- bge-large-en-v1.5: SOTA performance
3. Retrieval Strategies
Approaches:
- Dense Retrieval: Semantic similarity via embeddings
- Sparse Retrieval: Keyword matching (BM25, TF-IDF)
- Hybrid Search: Combine dense + sparse
- Multi-Query: Generate multiple query variations
- HyDE: Generate hypothetical documents
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 422 lines · 49 tokens per session scan A a002c1698147
rag-implementation is a skill published in the GitHub repository AI-Safeter/antigravity-cli-plugin (10 stars, last pushed 3mo ago), licensed MIT. It adds 49 tokens to every session and 2,775 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to rag-implementation, differing in 23 lines, and is treated as a copy.
Other skills, from other repositories
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
9router-embeddings
Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.
potpie-source-ingestion
Use when the user explicitly asks to ingest, refresh, or deeply understand a repository, PR, issue, ticket, runbook, incident report, document, or web link into Potpie. The harness performs todo-driven discovery, uses local/GitHub/integration tools and read-only subagents when available, builds evidence-backed…
embedding-strategies
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
generate-rag-dataset
Generate a synthetic evaluation dataset from your RAG knowledge base. Creates diverse Q&A pairs with expected answers and relevant context, ready for LangWatch experiments and platform import. Use when you need test data for your RAG pipeline.
embeddings
Vector embeddings configuration and semantic search.