New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

RAG Implementation

by davila7 in davila7/claude-code-templates

Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.

MITAuto-check passedAI & LLM Engineering

Install RAG Implementation

skills CLI
$ npx skills add davila7/claude-code-templates --skill rag-implementation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install davila7/claude-code-templates rag-implementation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cli-tool/components/skills/ai-research/rag-implementation .claude/skills/rag-implementation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
rag-implementation
GitHub stars
32k
Used in
1 other repo
Token cost
~467 tokens
SKILL.md length
184 words
Files
1
Skills in repo
477
Repo updated
First seen
Licence
MIT

At a glance

Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.

  • Works in 2 steps: Chunking is critical—bad chunks mean bad… → Hybri
  • Retrieval augmented
  • SKILL.md covers Capabilities, Patterns, Anti-Patterns and ⚠️ Sharp Edges, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

RAG Implementation is an agent skill from davila7/claude-code-templates. Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.

Its SKILL.md is about 470 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Retrieval-augmented generation, Vector databases and Embeddings. The repository describes itself as: CLI tool for configuring and monitoring Claude Code. The licence is MIT.

When your agent uses it

  • Retrieval augmented
  • Semantic search

Example prompts

  • “/rag-implementation”

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. Chunking is critical—bad chunks mean bad retrieval
  2. Hybri

What it can do on your machine

Read from SKILL.md and the folder at commit 14680ec. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

RAG Implementation loads about 467 tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 184 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~53
When it runs · the whole SKILL.md, loaded when a task matches
~467

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from davila7/claude-code-templates at commit 14680ec, republished under its MIT licence (© davila7). 184 words, ~467 tokens.

Download SKILL.mdSave it as .claude/skills/rag-implementation/SKILL.md (or your agent's skills folder).
name
rag-implementation
description
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
source
vibeship-spawner-skills (Apache 2.0)

RAG Implementation

You're a RAG specialist who has built systems serving millions of queries over terabytes of documents. You've seen the naive "chunk and embed" approach fail, and developed sophisticated chunking, retrieval, and reranking strategies.

You understand that RAG is not just vector search—it's about getting the right information to the LLM at the right time. You know when RAG helps and when it's unnecessary overhead.

Your core principles:

  1. Chunking is critical—bad chunks mean bad retrieval
  2. Hybri

Capabilities

  • document-chunking
  • embedding-models
  • vector-stores
  • retrieval-strategies
  • hybrid-search
  • reranking

Patterns

Semantic Chunking

Chunk by meaning, not arbitrary size

Combine dense (vector) and sparse (keyword) search

Contextual Reranking

Rerank retrieved docs with LLM for relevance

Anti-Patterns

❌ Fixed-Size Chunking
❌ No Overlap
❌ Single Retrieval Strategy

⚠️ Sharp Edges

IssueSeveritySolution
Poor chunking ruins retrieval qualitycritical// Use recursive character text splitter with overlap
Query and document embeddings from different modelscritical// Ensure consistent embedding model usage
RAG adds significant latency to responseshigh// Optimize RAG latency
Documents updated but embeddings not refreshedmedium// Maintain sync between documents and embeddings

Works well with: context-window-management, conversation-memory, prompt-caching, data-pipeline

© davila7, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in cli-tool/components/skills/ai-research/rag-implementation of davila7/claude-code-templates.

Open the folder on GitHubat commit 14680ec

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in davila7/claude-code-templates, which our catalogue first saw on October 7, 2026.

Compare with similar skills

RAG Implementation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

RAG Implementation compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
RAG Implementation this skilldavila7/claude-code-templates32k1 repos~467Automated safety check: PassMIT
Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs13k8 repos~2.3kAutomated safety check: PassMIT
Ms Agent Framework RAGshuyu-labs/WebCode278—~1.1kAutomated safety check: PassCustom licence
Pgvector Semantic Searchtimescale/pg-aiguide1.9k1 repos~3.8kAutomated safety check: PassApache-2.0
RAG ArchitectJeffallan/claude-skills12k1 repos~2kAutomated safety check: PassMIT
RAG Implementationwshobson/agents40k10 repos~1.1kAutomated safety check: PassMIT

Similar skills

  • Chroma Vector Database

    Orchestra-Research/AI-Research-SKILLs

    Shows how to store documents and embeddings in Chroma, query them by similarity with metadata filters, and persist them to disk for RAG and semantic search projects.

    13k GitHub starsUsed in 8 repos~2.3k tokens
    AI & LLM EngineeringAuto-check passed

  • Ms Agent Framework RAG

    shuyu-labs/WebCode

    Comprehensive guide for building Agentic RAG systems using Microsoft Agent Framework in C.

    278 GitHub stars~1.1k tokensUpdated 3 mo ago
    AI & LLM EngineeringAuto-check passed

  • Pgvector Semantic Search

    timescale/pg-aiguide

    A skill your agent uses for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search.

    1.9k GitHub starsUsed in 1 repo~3.8k tokens
    AI & LLM EngineeringAuto-check passed

  • RAG Architect

    Jeffallan/claude-skills

    Designs retrieval-augmented generation systems: document chunking, embeddings, vector store setup, hybrid search, reranking and retrieval evaluation, with checks at each step.

    12k GitHub starsUsed in 1 repo~2k tokens
    AI & LLM EngineeringAuto-check passed

  • RAG Implementation

    wshobson/agents

    Build retrieval-augmented generation systems: pick a vector database and embedding model, choose retrieval and reranking strategies, and start from a LangGraph pipeline.

    40k GitHub starsUsed in 10 repos~1.1k tokens
    AI & LLM EngineeringAuto-check passed

  • Hunt RAG Vector

    elementalsouls/Claude-BugHunter

    Hunt vector-store / embedding-layer weaknesses in RAG pipelines (OWASP LLM08 Vector and Embedding Weaknesses) — persistent corpus poisoning that survives across sessions and users (distinct from…

    4.8k GitHub stars~2.6k tokensUpdated today
    AI & LLM EngineeringAuto-check passed

More from davila7/claude-code-templates

All 477 skills in this repo

  • Perplexity Web Search

    davila7/claude-code-templates

    Runs web-grounded searches through Perplexity's Sonar models over OpenRouter for current events, recent literature and cited facts beyond the model's training cutoff.

    32k GitHub starsUsed in 12 repos~3.5k tokens
    Auto-check: notes

  • Neuropixels Data Analysis

    davila7/claude-code-templates

    Analyzes Neuropixels recordings from SpikeGLX or Open Ephys through preprocessing, drift correction, Kilosort4 spike sorting, quality metrics and curation.

    32k GitHub starsUsed in 10 repos~2.8k tokens
    Auto-check passed

  • Scientific Venue Templates

    davila7/claude-code-templates

    Supplies LaTeX templates and formatting rules for journals, conferences, posters, and grant proposals, then can check a draft against them.

    32k GitHub starsUsed in 9 repos~5.1k tokens
    Auto-check: notes

  • Brand Voice Content Creator

    davila7/claude-code-templates

    Analyzes a brand's existing writing to lock in a consistent voice, then builds SEO blog posts and platform-specific social content around it.

    32k GitHub starsUsed in 3 repos~1.9k tokens
    Auto-check passed

  • CAPA Officer

    davila7/claude-code-templates

    Guides corrective and preventive action (CAPA) work in a quality management system, from initiation and root cause analysis through effectiveness verification.

    32k GitHub starsUsed in 1 repo~2k tokens
    Auto-check passed

  • Fda Consultant Specialist

    davila7/claude-code-templates

    Senior FDA consultant and specialist for medical device companies including HIPAA compliance and requirement management.

    32k GitHub starsUsed in 1 repo~2.7k tokens
    Auto-check passed

Categories

Questions about RAG Implementation

What does RAG Implementation do?

Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search. RAG Implementation is an agent skill from davila7/claude-code-templates. Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.

When should I use RAG Implementation?

RAG Implementation fits situations like: retrieval augmented; semantic search.

How do I install RAG Implementation in Claude Code?

Run `npx skills add davila7/claude-code-templates --skill rag-implementation -a claude-code`. Or copy the skill folder (cli-tool/components/skills/ai-research/rag-implementation in davila7/claude-code-templates) into .claude/skills/rag-implementation in your project. Claude Code loads it when a task matches its description.

How do I install RAG Implementation in Codex?

Run `npx skills add davila7/claude-code-templates --skill rag-implementation -a codex`. Or copy the skill folder (cli-tool/components/skills/ai-research/rag-implementation in davila7/claude-code-templates) into .agents/skills/rag-implementation in your project. Codex loads it when a task matches its description.

Can I use RAG Implementation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add davila7/claude-code-templates --skill rag-implementation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/rag-implementation, .gemini/skills/rag-implementation, .github/skills/rag-implementation and .opencode/skills/rag-implementation in your project.

What does RAG Implementation need to run?

SKILL.md names no scripts, command-line tools or credentials: RAG Implementation is instructions for the agent only.

Does RAG Implementation access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is RAG Implementation safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does RAG Implementation use?

RAG Implementation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does RAG Implementation use?

About 467 tokens (SKILL.md is roughly 1.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to RAG Implementation?

Skills that share tags, products or a category with RAG Implementation: Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), Ms Agent Framework RAG (shuyu-labs/WebCode, 278 stars), Pgvector Semantic Search (timescale/pg-aiguide, 1.9k stars) and RAG Architect (Jeffallan/claude-skills, 12k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains RAG Implementation?

davila7 (a GitHub user) maintains it in davila7/claude-code-templates, which has 32,463 GitHub stars. The repository holds 477 skills in this directory. The repository was last updated on October 8, 2026.

Source: davila7/claude-code-templates on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.