What is rag-agent-builder?

A guide to building Retrieval-Augmented Generation applications, which let an AI model search external documents or knowledge bases before writing an answer. It covers embeddings, vector databases, search strategies, and answer generation.

Build document question-answering tools, knowledge bases, enterprise search, and AI agents that retrieve information from external sources.

It helps ground AI responses in relevant source material instead of relying only on the model's built-in knowledge. It provides patterns for improving retrieval and measuring search and answer quality.

How Aniket-a14/SRA configures Claude Code and Codex

Reuse

Borrowing it

Nothing to install: this file belongs to Aniket-a14/SRA. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/Aniket-a14/SRA/main/.agents/skills/rag-agent-builder/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/Aniket-a14/SRA

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/aniket-a14/sra/rag-agent-builder/github.svg)](https://agentmods.dev/skills/aniket-a14/sra/rag-agent-builder)

Your own site

<a href="https://agentmods.dev/skills/aniket-a14/sra/rag-agent-builder"><img src="https://agentmods.dev/badge/skills/aniket-a14/sra/rag-agent-builder/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/aniket-a14/sra/rag-agent-builder"><img src="https://agentmods.dev/badge/skills/aniket-a14/sra/rag-agent-builder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 61 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,987 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

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	$0.00061	$0.02987
Opus 5	$0.00030	$0.01494
Sonnet 5	$0.00012	$0.00597
Haiku 4.5	$0.00006	$0.00299

Measured 9d ago against content hash 51538dccc9bb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

rag-agent-builder 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 9d ago.

The scan reads SKILL.md. This mod also ships 6 executable files (examples/agentic_rag.py, examples/basic_rag.py, examples/retrieval_strategies.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.agents/skills/rag-agent-builder/SKILL.md · 441 lines

How it starts

The opening of the file, as written. The whole thing — 441 lines — stays where its author put it; the contents beside it link to each section on GitHub.

RAG Agent Builder

Build powerful Retrieval-Augmented Generation (RAG) applications that enhance LLM capabilities with external knowledge sources, enabling accurate, contextualized AI responses.

Quick Start

Get started with RAG implementations in the examples and utilities:

Examples: See examples/ directory for complete implementations:
- basic_rag.py - Simple chunk-embed-retrieve-generate pipeline
- retrieval_strategies.py - Hybrid search, reranking, and filtering
- agentic_rag.py - Agent-controlled retrieval with iterative refinement
Utilities: See scripts/ directory for helper modules:
- embedding_management.py - Embedding generation, normalization, and caching
- vector_db_manager.py - Vector database abstraction and factory
- rag_evaluation.py - Retrieval and answer quality metrics

Overview

RAG systems combine three key components:

Document Retrieval - Find relevant information from knowledge bases
Context Integration - Pass retrieved context to the LLM
Response Generation - Generate answers grounded in the retrieved information

This skill covers building production-ready RAG applications with various frameworks and approaches.

Core Concepts

What is RAG?

RAG augments LLM knowledge with external data:

Without RAG: LLM relies on training data (may be outdated or limited)
With RAG: LLM uses real-time, custom knowledge + training knowledge

When to Use RAG

Document Q&A: Answer questions about PDFs, books, reports
Knowledge Base Search: Query internal documentation, wikis
Enterprise Search: Search proprietary company data
Context-Specific Assistants: Customer support, HR assistants
Fact-Heavy Applications: Legal docs, medical records, financial data

Read the full file on GitHub · 441 lines

Files

What ships with it

7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

9d ago First seen · 441 lines · 61 tokens per session scan A 51538dccc9bb

Subscribe to this mod's changes

rag-agent-builder is a skill published in the GitHub repository Aniket-a14/SRA (23 stars, last pushed 10d ago), licensed Apache-2.0. It adds 61 tokens to every session and 2,987 once invoked, about $0.0003 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 2026-08-30.

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Cursor rules for Next.js development with Tailwind CSS and TypeScript integration.

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ask-ai

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SRA: Skill for Claude Code