Use this repo skill for RAGs, a Streamlit app that builds configurable LlamaIndex RAG agents from natural-language setup, data sources, and model settings.
$ npx skills add VectorSpaceLab/AREX-Skill --skill rags -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill rags --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/repositories/repo-skills/rags .claude/skills/rags && rm -rf skills-srcUse ~/.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/
Install the "rags" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/rags into .claude/skills/rags/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rags", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/ragsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add VectorSpaceLab/AREX-Skill --skill rags -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill rags --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/repositories/repo-skills/rags .agents/skills/rags && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "rags" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/rags into .agents/skills/rags/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rags", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add VectorSpaceLab/AREX-Skill --skill rags -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill rags --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/repositories/repo-skills/rags .cursor/skills/rags && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "rags" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/rags into .cursor/skills/rags/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rags", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/VectorSpaceLab/AREX-Skill.git --path skills/repositories/repo-skills/rags--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add VectorSpaceLab/AREX-Skill --skill rags -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill rags --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/repositories/repo-skills/rags .gemini/skills/rags && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "rags" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/rags into .gemini/skills/rags/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rags", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install VectorSpaceLab/AREX-Skill ragsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add VectorSpaceLab/AREX-Skill --skill rags -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/repositories/repo-skills/rags .github/skills/rags && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "rags" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/rags into .github/skills/rags/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rags", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add VectorSpaceLab/AREX-Skill --skill rags -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill rags --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/repositories/repo-skills/rags .opencode/skills/rags && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "rags" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/rags into .opencode/skills/rags/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rags", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
ragsUse this repo skill for RAGs, a Streamlit app that builds configurable LlamaIndex RAG agents from natural-language setup, data sources, and model settings.
Rags is an agent skill from VectorSpaceLab/AREX-Skill. Use this repo skill for RAGs, a Streamlit app that builds configurable LlamaIndex RAG agents from natural-language setup, data sources, and model settings.
Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts and reference files (for example `references/app-architecture.md`, `references/repo-provenance.md` and `references/repo-routing-metadata.json`).
It sits in AI & LLM Engineering, covering Retrieval-augmented generation. It works with Streamlit and LlamaIndex. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is MIT.
4 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit ac3fe1a. It shows what the files ask for, not the result of running them.
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.
Ships 2 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
pythonFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Rags loads about 1k tokens when it runs, and up to ~3.5k if it reads all its reference files. Until then it costs about 40 tokens; SKILL.md has 404 words of instructions outside code blocks.
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.
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); the scripts in this folder are not scanned.
The full file from VectorSpaceLab/AREX-Skill at commit ac3fe1a, republished under its MIT licence (© VectorSpaceLab). 404 words, ~1,011 tokens.
.claude/skills/rags/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.RAGs is a Streamlit application for building retrieval-augmented chat agents over user-provided data. Use this skill when the task mentions RAGs, creating a RAG bot from natural language, configuring generated RAG parameters, querying a generated agent, or debugging RAGs cache/secrets/model behavior.
references/repo-provenance.md when
checking whether this skill is current for a checkout or when refreshing it.references/app-architecture.md when
you need the cross-page architecture, state, cache, model, and tool flow.scripts/check_install.py for a
safe dependency/source-import diagnostic. It does not call external LLMs.scripts/run_rags_app.py to validate or wrap
the Streamlit launch command for a user-provided RAGs checkout. It dry-runs
by default; pass --execute only when the user wants a long-running server.The current source snapshot is an app-style repository, not an installable
Python package named rags. Dependency-oriented setup and running from a RAGs
checkout is the supported operating model captured by this skill.
| User intent | Read |
|---|---|
| Build a new RAG bot from files, a directory, URLs, task text, RAG parameters, optional web search, or beta multimodal setup. | sub-skills/builder/SKILL.md |
| Inspect, edit, update, delete, rename, or repair a generated agent's configuration or cache. | sub-skills/configuration/SKILL.md |
| Ask questions to an existing generated agent, inspect sources, or debug no/irrelevant/broken sources. | sub-skills/chat/SKILL.md |
| Diagnose install, secrets, dependency version, root-package install, cache upgrade, or optional dependency problems. | references/troubleshooting.md |
Use public project instructions or equivalent dependency installation. The
verified inspection environment used Python 3.10 with streamlit==1.28.0,
llama-index==0.9.7, llama-hub==0.0.44, langchain==0.0.305, and
pypdf==3.17.1. A dependency-only Poetry setup or requirements-based setup may
be needed because root package installation fails for this snapshot.
Safe checks:
python scripts/check_install.py
python scripts/check_install.py --repo-root /path/to/rags
python scripts/run_rags_app.py --repo-root /path/to/rags --check-secretsLaunch only with user intent:
python scripts/run_rags_app.py --repo-root /path/to/rags --execute -- --server.headless trueRAGs reads a Streamlit secret named openai_key while configuring the builder
LLM. Provider-specific routes may also need anthropic_key, replicate_key, or
metaphor_key.
The generated skill is based on source inspection plus live dependency/source
module inspection. Safe checks covered imports, signatures, RAGParams defaults,
local text load_data, Streamlit CLI help, and cache-registry behavior. The
following were intentionally not executed by default: real OpenAI/Anthropic/
Replicate/Metaphor calls, URL downloads, a long-running Streamlit server, and
actual beta multimodal construction with torch/CLIP dependencies.
Do not use this skill as a generic LlamaIndex manual. It is specifically for the RAGs app's builder/configuration/chat workflow and its cache/secrets behavior. For changing the RAGs source code itself, treat this as repository maintenance and combine with ordinary code inspection rather than relying only on this operating skill.
© VectorSpaceLab, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 7 other files (scripts, references) in skills/repositories/repo-skills/rags of VectorSpaceLab/AREX-Skill.
Open the folder on GitHubat commit ac3fe1a
Rags 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Rags this skillVectorSpaceLab/AREX-Skill | 330 | — | ~1k | Automated safety check: Pass | MIT | |
| Sentence Transformers EmbeddingsOrchestra-Research/AI-Research-SKILLs | 13k | 2 repos | ~1.6k | Automated safety check: Pass | MIT | |
| RAG Skillsllama-farm/llamafarm | 836 | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Agentsop Idempotent Ingestionagentsope/SkillAlchemy | 466 | — | ~6.8k | Automated safety check: Pass | MIT | |
| Agentsop Llamaindexagentsope/SkillAlchemy | 466 | — | ~6.4k | Automated safety check: Pass | MIT | |
| Agentsop Multi Tenant RAGagentsope/SkillAlchemy | 466 | — | ~9.8k | Automated safety check: Pass | MIT |
Orchestra-Research/AI-Research-SKILLs
Generates text embeddings locally with the sentence-transformers library for RAG, semantic search, clustering and similarity, with model picks for general, multilingual and legal text.
llama-farm/llamafarm
RAG-specific best practices for LlamaIndex, ChromaDB, and Celery workers.
agentsope/SkillAlchemy
Re-ingest-correctness SOP for production RAG. An agent skill from agentsope/SkillAlchemy.
agentsope/SkillAlchemy
Operating-system distillation of LlamaIndex — the leading RAG / document-agent framework.
agentsope/SkillAlchemy
Security-first SOP for multi-tenant RAG systems. An agent skill from agentsope/SkillAlchemy.
magnus919/agent-skills
Build LLM applications with the LlamaIndex framework. An agent skill from magnus919/agent-skills.
VectorSpaceLab/AREX-Skill
Use this repo skill for Agent Lightning package tasks: authoring trainable agents, tracing rewards and spans, running LightningStore/Trainer loops, using agl CLI services, choosing examples, and…
VectorSpaceLab/AREX-Skill
A skill your agent uses when configuring LiteLLM for MCP tools, A2A agents, Claude Code/Cursor agent gateway traffic, MCP auth/OAuth, tool permissions, semantic filtering, or agent-specific proxy…
VectorSpaceLab/AREX-Skill
Build and debug DB-GPT agents, tools, skills, teams, and AWEL workflows, including deterministic local DAG runs and HTTP-trigger topology without assuming an LLM, credential, or external service.
VectorSpaceLab/AREX-Skill
Work on the actively maintained LangChain v1 agent package: initchatmodel, createagent, structured output, tools, middleware, embeddings initialization, provider routing, and agent runtime…
VectorSpaceLab/AREX-Skill
A skill your agent uses for giskard.agents async chat workflows, tools, prompt templates, structured outputs, retries, rate limiting, embeddings, and optional LiteLLM backend.
VectorSpaceLab/AREX-Skill
A skill your agent uses for AlphaFold 3 input preparation, prediction command planning, output interpretation, and Python API inspection.
Works with
Categories
Use this repo skill for RAGs, a Streamlit app that builds configurable LlamaIndex RAG agents from natural-language setup, data sources, and model settings. Rags is an agent skill from VectorSpaceLab/AREX-Skill. Use this repo skill for RAGs, a Streamlit app that builds configurable LlamaIndex RAG agents from natural-language setup, data sources, and model settings.
Rags fits situations like: tasks that involve Retrieval-augmented generation.
Run `npx skills add VectorSpaceLab/AREX-Skill --skill rags -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/rags in VectorSpaceLab/AREX-Skill) into .claude/skills/rags in your project. Claude Code loads it when a task matches its description.
Run `npx skills add VectorSpaceLab/AREX-Skill --skill rags -a codex`. Or copy the skill folder (skills/repositories/repo-skills/rags in VectorSpaceLab/AREX-Skill) into .agents/skills/rags in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add VectorSpaceLab/AREX-Skill --skill rags -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/rags, .gemini/skills/rags, .github/skills/rags and .opencode/skills/rags in your project.
Going by SKILL.md and its folder, Rags needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
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.
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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Rags is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 1k tokens (SKILL.md is roughly 4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.4k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Rags: Sentence Transformers Embeddings (Orchestra-Research/AI-Research-SKILLs, 13k stars), RAG Skills (llama-farm/llamafarm, 836 stars), Agentsop Idempotent Ingestion (agentsope/SkillAlchemy, 466 stars) and Agentsop Llamaindex (agentsope/SkillAlchemy, 466 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 330 GitHub stars. The repository holds 159 skills in this directory. The repository was last updated on September 3, 2026.
Source: VectorSpaceLab/AREX-Skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.
