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Ailog RAG Skills for Claude Code

Ailog RAG Skills for Claude Code is a set of four Claude Code skills for working on retrieval-augmented generation pipelines: code auditing, quality evaluation, chunking recommendations and pipeline scaffolding. Its documented scaffolding choices include Python and TypeScript implementations using several frameworks, as well as the Ailog API.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Ailog RAG Skills for Claude Code — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use the skills within Claude Code when you want a structured RAG review, an evaluation workflow, chunking guidance or a scaffold for a new pipeline.

Who it is for

  • Developers reviewing or improving an existing RAG codebase.
  • Teams evaluating retrieval and answer-generation quality.
  • Developers planning chunking choices or starting a RAG implementation.

Documented uses

  • Find common RAG implementation issues across chunking, embeddings, retrieval, generation and production practices.
  • Assess retrieval quality with ranking metrics and generation quality with faithfulness, relevance, coherence and conciseness measures.
  • Get chunking recommendations informed by document types, query patterns and embedding-model constraints.
  • Generate RAG starter code for one of the documented framework or API options.
Before you choose

Requirements & limits

The repository is public and MIT-licensed. Claude Code 2.0.0 or later is listed as a requirement. Optional benchmarking against Ailog’s API requires an Ailog API key and workspace ID.

  • The project is a set of developer skills that operate within Claude Code, not a standalone RAG hosting service.
  • The scaffold documentation lists Python with LangChain and Qdrant, Python with LlamaIndex, framework-free Python, TypeScript with LangChain.js, and the Ailog API.
  • Ailog API benchmarking is an optional part of /rag-eval and requires credentials; the README points to creating an Ailog workspace.
  • Installation instructions cover both user-level and project-level Claude Code skills directories.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the skills by copying them into Claude Code

Make the repository’s RAG skills available in a Claude Code session.

Before you start

  • Claude Code version 2.0.0 or later
  • The GitHub repository and a Claude Code skills directory

Steps

  1. Clone the repository: the command below.

    Documented command
    git clone https://github.com/floflo777/claude-rag-skills.git

  2. Copy its contents into the user skills directory with cp -r claude-rag-skills/* ~/.claude/skills/, or into the current project with cp -r claude-rag-skills/* .claude/skills/.

  3. Use a documented skill command, such as /rag-audit or /chunking-advisor, in a Claude Code session.

Expected outcome

The installed skills can be invoked in Claude Code using their documented slash commands.

Guide limitations

  • The README lists Claude Code 2.0.0 or later as a requirement.
  • This is the documented manual installation process; no execution or installation test is claimed.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Retrieval-augmented applications

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Ailog RAG Skills for Claude CodeFind common RAG implementation issues across chunking, embeddings, retrieval, generation and production practices.CLIThe repository is public and MIT-licensed. Claude Code 2.0.0 or later is listed as a requirement. Optional benchmarking against Ailog’s API requires an Ailog API key and workspace ID.
LangsearchCrawl website or local data for a knowledge base.CLI, Self-hosted, Server
Hybrid Search RagCombine document retrieval and language-model generation in a configurable workflow.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
16 October 2026
Original website title
floflo777/claude-rag-skills · Official homepage title · retained in its source language
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