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 skills add NOMARJ/sigil --skill rag-implementationgit clone --depth 1 https://github.com/NOMARJ/sigilWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nomarj/sigil/rag-implementation)<a href="https://agentmods.dev/skills/nomarj/sigil/rag-implementation"><img src="https://agentmods.dev/badge/skills/nomarj/sigil/rag-implementation.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00049 | $0.04038 |
| Opus 5 | $0.00024 | $0.02019 |
| Sonnet 5 | $0.00010 | $0.00808 |
| Haiku 4.5 | $0.00005 | $0.00404 |
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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 571 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.
When to Use This Skill
- 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
Core Components
1. Vector Databases
Purpose: Store and retrieve document embeddings efficiently
Options:
- Pinecone: Managed, scalable, serverless
- Weaviate: Open-source, hybrid search, GraphQL
- Milvus: High performance, on-premise
- Chroma: Lightweight, easy to use, local development
- Qdrant: Fast, filtered search, Rust-based
- pgvector: PostgreSQL extension, SQL integration
2. Embeddings
Purpose: Convert text to numerical vectors for similarity search
Models (2026):
| Model | Dimensions | Best For |
|---|---|---|
| voyage-3-large | 1024 | Claude apps (Anthropic recommended) |
| voyage-code-3 | 1024 | Code search |
| text-embedding-3-large | 3072 | OpenAI apps, high accuracy |
| text-embedding-3-small | 1536 | OpenAI apps, cost-effective |
| bge-large-en-v1.5 | 1024 | Open source, local deployment |
| multilingual-e5-large | 1024 | Multi-language support |
3. Retrieval Strategies
Approaches:
- Dense Retrieval: Semantic similarity via embeddings
- Sparse Retrieval: Keyword matching (BM25, TF-IDF)
- Hybrid Search: Combine dense + sparse with weighted fusion
- Multi-Query: Generate multiple query variations
- HyDE: Generate hypothetical documents for better retrieval
4. Reranking
Purpose: Improve retrieval quality by reordering results
Methods:
- Cross-Encoders: BERT-based reranking (ms-marco-MiniLM)
- Cohere Rerank: API-based reranking
- Maximal Marginal Relevance (MMR): Diversity + relevance
- LLM-based: Use LLM to score relevance
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.
- 5d ago First seen · 571 lines · 49 tokens per session scan A d5291ecdfa63
rag-implementation is a skill published in the GitHub repository NOMARJ/sigil (5 stars, last pushed 2d ago), licensed Apache-2.0. It adds 49 tokens to every session and 4,038 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
amazon-bedrock
Builds generative AI applications on Amazon Bedrock. Covers model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore. Use when invoking models, setting up Knowledge Bases, creating agents, applying guardrails, deploying to AgentCore, troubleshooting Bedrock…
azure-ai-contentunderstanding-py
Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video.
ai-ml
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
ai-engineer
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.
ai-product
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production.
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.