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name: rags description: "Use this repo skill for RAGs, a Streamlit app that builds configurable LlamaIndex RAG agents from natural-language setup, data sources, and model settings." disable-model-invocation: true metadata: disco-role: operating license: MIT

RAGs Repo Skill

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.

Before Acting

1. Read references/repo-provenance.md when checking whether this skill is current for a checkout or when refreshing it.
2. Read references/app-architecture.md when you need the cross-page architecture, state, cache, model, and tool flow.
3. Run or inspect scripts/check_install.py for a safe dependency/source-import diagnostic. It does not call external LLMs.
4. Use 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.

Route Map

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

Install and Launch Checks

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-secrets

Launch only with user intent:

python scripts/run_rags_app.py --repo-root /path/to/rags --execute -- --server.headless true

RAGs 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.

Verification Scope

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.

Boundaries

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.