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rag-implementation

rag-implementation is a skill for Claude Code from sickn33/agentic-awesome-skills. It costs 31 tokens per session (1,213 once invoked), scanned A, original, MIT.

A workflow for building retrieval-augmented generation, or RAG, systems that find relevant source material before an AI model writes an answer. It covers embeddings, vector databases, document splitting, retrieval, and evaluation.

In plain words
What is it for?
Use it to build knowledge-grounded AI apps, semantic search, document question-answering, or retrieval evaluations.
Why use it?
It helps reduce answers that are unsupported because the model was given the wrong context. It provides a way to plan and measure the search part of a document question-answering system.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the agentic-awesome-skills plugin — 197 skills, 1 plugin shipped together

not rated 50krepo +312 today A scan Socket: passSnyk: pass 31 tokens original MIT

Good fit Use it to build knowledge-grounded AI apps, semantic search, document question-answering, or retrieval evaluations.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/sickn33/agentic-awesome-skills/rag-implementation
View source ↗ sickn33/agentic-awesome-skills aaskills.tech
About the project

AAS Core is a local control plane for coding agents that lets them search a large catalogue of skills, choose a stack, validate it, and create a reproducible plan. It is used to assemble and review agent workflows through its CLI, local MCP server, catalogue, plugins, and Workbench. The catalogue add-ons provide the skills, plugins, bundles, and workflows that AAS Core helps agents select and validate.

sickn33/agentic-awesome-skills · 49,614 stars · on GitHub · aaskills.tech

Install

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.

Any agent
npx skills add sickn33/agentic-awesome-skills --skill rag-implementation
Clone the repo
git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills

Made for: Claude Code.

Or install agentic-awesome-skills, the plugin that ships this one along with the rest of its 197 skills, 1 plugin.

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.

agentmods badge for rag-implementation

README.md
[![agentmods](https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/rag-implementation/github.svg)](https://agentmods.dev/skills/sickn33/agentic-awesome-skills/rag-implementation)
Your own site
<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/rag-implementation"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/rag-implementation/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.

agentmods 80×15 button for rag-implementation

Your own site · 80×15
<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/rag-implementation"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/rag-implementation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,213 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
  • Socket pass 28 Oct 2026
  • Snyk pass 28 Oct 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00031 $0.01213
Opus 5.5 $0.00012 $0.00485
Sonnet 5.5 $0.00006 $0.00243
Haiku 4.5 $0.00003 $0.00121

Measured yesterday against content hash 289a0397fd80, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 yesterday.

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.

plugins/aasb-aas-agent-mcp-builder/skills/rag-implementation/SKILL.md · 207 lines

How it starts

The opening of the file, as written. The whole thing — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.

RAG Implementation Workflow

Overview

Specialized workflow for implementing RAG (Retrieval-Augmented Generation) systems including embedding model selection, vector database setup, chunking strategies, retrieval optimization, and evaluation.

When to Use This Workflow

Use this workflow when:

  • Building RAG-powered applications
  • Implementing semantic search
  • Creating knowledge-grounded AI
  • Setting up document Q&A systems
  • Optimizing retrieval quality

Workflow Phases

Phase 1: Requirements Analysis

Skills to Invoke
  • ai-product - AI product design
  • rag-engineer - RAG engineering
Actions
  1. Define use case
  2. Identify data sources
  3. Set accuracy requirements
  4. Determine latency targets
  5. Plan evaluation metrics
Copy-Paste Prompts
Use @ai-product to define RAG application requirements

Phase 2: Embedding Selection

Skills to Invoke
  • embedding-strategies - Embedding selection
  • rag-engineer - RAG patterns
Actions
  1. Evaluate embedding models
  2. Test domain relevance
  3. Measure embedding quality
  4. Consider cost/latency
  5. Select model
Copy-Paste Prompts
Use @embedding-strategies to select optimal embedding model

Phase 3: Vector Database Setup

Skills to Invoke
  • vector-database-engineer - Vector DB
  • similarity-search-patterns - Similarity search
Actions
  1. Choose vector database
  2. Design schema
  3. Configure indexes
  4. Set up connection
  5. Test queries
Copy-Paste Prompts
Use @vector-database-engineer to set up vector database

Phase 4: Chunking Strategy

Skills to Invoke
  • rag-engineer - Chunking strategies
  • rag-implementation - RAG implementation
Actions
  1. Choose chunk size
  2. Implement chunking
  3. Add overlap handling
  4. Create metadata
  5. Test retrieval quality
Copy-Paste Prompts
Use @rag-engineer to implement chunking strategy

Phase 5: Retrieval Implementation

Skills to Invoke
  • similarity-search-patterns - Similarity search
  • hybrid-search-implementation - Hybrid search

Read the full file on GitHub · 207 lines

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.

  1. yesterday First seen · 207 lines · 31 tokens per session scan A 289a0397fd80

Subscribe to this mod's changes

rag-implementation is a skill published in the GitHub repository sickn33/agentic-awesome-skills (49,614 stars, last pushed today), licensed MIT. It adds 31 tokens to every session and 1,213 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-11-12.

Claude CodeData and AIRAG and vectorsagentic-skillsai-agentsai-workflowsantigravitymcp
Related

rag-implementation compared with skills from other repositories

rag-implementation next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
rag-implementationsickn33/agentic-awesome-skillsSkill50ktodayMITA31Claude Code
rag-builderAtulPurohit/Antigravity-Awesome-SkillsSkill54mo agononeA25Claude Code, Codex
semantic-searchAtulPurohit/Antigravity-Awesome-SkillsSkill54mo agononeA26Claude Code, Codex
vector-databaseAtulPurohit/Antigravity-Awesome-SkillsSkill54mo agononeA25Claude Code, Codex
rag-retrieval-auditalivirgo/Major-AI-SkillsSkill2yesterdayMITA66Claude Code
ai-engineering-toolkitmarysatasselshaped667/skills-collection-1Skill11mo agoMITA47Claude Code, Codex
ai-content-filterAtulPurohit/Antigravity-Awesome-SkillsSkill54mo agononeA28Claude Code, Codex