Skip to content
TrustedSkillsbeta
Back to all skills

Rag Engineer

Listed
sickn33·vlatest·ai-ml·MIT

The Rag Engineer skill automatically constructs high-quality retrieval augmented generation (RAG) pipelines for seamless knowledge integration and improved AI responses.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add sickn33/agentic-awesome-skills --skill rag-engineer -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

sickn33

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • Declares what it does: failed

    No SKILL.md was found in the skill's directory.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned sickn33/antigravity-awesome-skills at 5a4cdd9df8.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
5a4cdd9df8
Manifest
skills/rag-engineer/SKILL.md
Scanned
0 files
Copies in repo
3 — all of them scanned
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The rag-engineer skill allows AI agents to optimize Retrieval Augmented Generation (RAG) pipelines. It can automatically evaluate RAG performance, identify bottlenecks in the retrieval process, and suggest improvements to enhance accuracy and relevance of generated responses. This includes adjusting chunk sizes, embedding models, and retrieval strategies.

When to use it

  • Improving RAG Accuracy: When a knowledge base is used for context but the AI's answers are often inaccurate or irrelevant.
  • Optimizing Retrieval Speed: To reduce latency in RAG pipelines where response time is critical.
  • Evaluating Different Knowledge Bases: To compare the effectiveness of various data sources when building a RAG system.
  • Tuning Embedding Models: When exploring different embedding models to find the best fit for your specific knowledge base and query patterns.

Key capabilities

  • RAG performance evaluation
  • Bottleneck identification in retrieval process
  • Chunk size optimization
  • Embedding model selection assistance
  • Retrieval strategy adjustment

Example prompts

  • "Evaluate the RAG pipeline's accuracy using this dataset."
  • "Suggest improvements to my RAG setup; I’m seeing slow response times."
  • "Compare the performance of Sentence Transformers and OpenAI embeddings for my knowledge base."

Tips & gotchas

The skill requires access to both your knowledge base data and a mechanism for evaluating generated responses. Ensure you have appropriate permissions and logging in place before running evaluations.