.cursor/skills/streamlit-skill/SKILL.md
Builds simple user interfaces in Python. Use when creating chatbots, dashboards, or data apps with Streamlit—especially chat UIs with st.chat_message and st.chat_input, or when the user mentions Streamlit.
npx skillsauth add chicagopeabodydev-sudo/library_bot_poc streamlit-skillInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when user interfaces are needed, such as an interface to communicate with a chatbot.
For chat interfaces, use st.chat_message(role) to display messages and st.chat_input() to accept user text. Store message history in st.session_state.messages and iterate to render the conversation. See Example 6 in examples.md.
development
Connects to Supabase-hosted PostgreSQL and pgvector vector databases. Use when storing or querying vector embeddings, building RAG pipelines with Supabase, using vecs for similarity search, or when the user mentions Supabase, pgvector, or vector databases.
data-ai
Defines structured data models with Pydantic BaseModel. Use when defining LLM outputs or inputs for RAG, LlamaIndex structured outputs, or when the user mentions Pydantic, structured output, or response schemas.
tools
NeMo Guardrails is an open-source Python package for adding programmable guardrails around LLM calls. Use it to block unsafe, malicious, off-topic, or policy-violating user inputs, retrieved RAG content, tool usage, and model responses.
data-ai
Used to supplement the data LLMs can use when answering questions by supplying them with custom data generated and managed by llama-index.