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But the assistant is working from general knowledge that may be months or years out of date.\u003C/p\u003E\n","\u003Cp\u003EHere's what that looks like in practice:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# What an unaided AI often generates:\nst.dataframe(df, use_container_width=True)  # deprecated parameter\nif "messages" not in st.session_state:      # verbose initialization\n    st.session_state.messages = []\n\n# What you actually want:\nst.dataframe(df, width="stretch")           # current API\nst.session_state.setdefault("messages", []) # concise one-liner\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBeyond deprecated APIs, the AI might skip caching entirely — meaning your app re-runs expensive queries every time a user clicks a filter instead of reusing results it already fetched. Or it might initialize session state with verbose \u003Ccode\u003Eif/else\u003C/code\u003E blocks instead of the one-liner \u003Ccode\u003Esetdefault()\u003C/code\u003E pattern.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAgent skills\u003C/strong\u003E fix this by giving your AI assistant specialized, up-to-date knowledge about Streamlit development. Think of them as reference guides that your AI reads before writing code. Instead of guessing how to build a chat interface or configure a theme, the AI loads a skill that contains the exact patterns, code examples, and guidelines for that task.\u003C/p\u003E\n","\u003Cp\u003EThe \u003Ca href=\"https://github.com/streamlit/agent-skills\"\u003Estreamlit/agent-skills\u003C/a\u003E repository is an open-source collection of 17 specialized skills for Streamlit development. They cover everything from building dashboards and chat UIs to connecting to Snowflake and creating custom components. The skills work with any AI coding assistant that supports the agent skills format — including Claude Code, Cursor, and Snowflake CoCo (where they come pre-installed).\u003C/p\u003E\n","\u003Cp\u003EThis guide walks you through installing the skills, understanding what each one does, and using them to build real apps. No prior experience with agent skills is required.\u003C/p\u003E\n","\u003Cp\u003EHere's a schematic summary of the Streamlit app creation process using the agent skills:\u003C/p\u003E\n<!-- Diagram: https://excalidraw.com/#json=U51O43YAwXyA-pFGyDIRX,cBQiyuTWzTpt9-yB8xWISA -->\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-streamlit-apps-with-agent-skills/diagram.png?v=d4e4cfb8\" alt=\"diagram\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EWhat agent skills are and how they help your AI assistant write better Streamlit code\u003C/li\u003E\u003Cli\u003EHow to install the skills for your specific AI coding assistant\u003C/li\u003E\u003Cli\u003EHow the 17 sub-skills are organized and when each one activates\u003C/li\u003E\u003Cli\u003EHow to build a dashboard and a chatbot using the skills\u003C/li\u003E\u003Cli\u003EHow to apply themes and use ready-made app templates\u003C/li\u003E\u003Cli\u003EHow to connect your app to Snowflake\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003ETwo working Streamlit apps — a dashboard with KPI metrics and charts, and a chatbot with streaming responses — built with the help of agent skills.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAn AI coding assistant (any of: \u003Ca href=\"https://signup.snowflake.com/cortex-code?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake CoCo\u003C/a\u003E, \u003Ca href=\"https://docs.anthropic.com/en/docs/claude-code\"\u003EClaude Code\u003C/a\u003E, \u003Ca href=\"https://cursor.com\"\u003ECursor\u003C/a\u003E)\u003C/li\u003E\u003Cli\u003EPython 3 installed on your machine\u003C/li\u003E\u003Cli\u003EStreamlit installed (\u003Ccode\u003Epip install streamlit\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EBasic familiarity with Python (you don't need to be an expert, know enough to make edits when necessary)\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EWhat Are Agent Skills?\u003C/h2\u003E\n","\u003Cp\u003EBefore we dive in, let's understand what agent skills actually are and why they matter.\u003C/p\u003E\n","\u003Ch3\u003EThe Problem\u003C/h3\u003E\n","\u003Cp\u003EAI coding assistants are trained on massive amounts of code, but their knowledge has a cutoff date. When you ask your AI to build a Streamlit app, it might generate code based on patterns that were correct a year ago but are outdated today.\u003C/p\u003E\n","\u003Cp\u003EThe deprecated API in the Overview is just one example. Here are other common issues with unaided AI-generated Streamlit code:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EMissing \u003Ccode\u003E@st.cache_data\u003C/code\u003E on data-loading functions, so the app re-fetches data on every user interaction instead of reusing cached results\u003C/li\u003E\u003Cli\u003EVerbose session state initialization (\u003Ccode\u003Eif "key" not in st.session_state: ...\u003C/code\u003E) instead of the one-liner \u003Ccode\u003Esetdefault()\u003C/code\u003E pattern\u003C/li\u003E\u003Cli\u003EOutdated navigation patterns instead of the current \u003Ccode\u003Est.navigation\u003C/code\u003E and \u003Ccode\u003Est.Page\u003C/code\u003E API\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EThe Solution: Agent Skills\u003C/h3\u003E\n","\u003Cp\u003EAn agent skill is a folder containing a \u003Ccode\u003ESKILL.md\u003C/code\u003E file and optional supporting resources:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Edeveloping-with-streamlit/\n├── SKILL.md              # Instructions the AI reads\n├── skills/               # 17 specialized sub-skills\n│   ├── building-streamlit-chat-ui/\n│   ├── building-streamlit-dashboards/\n│   ├── optimizing-streamlit-performance/\n│   └── ...\n└── templates/            # Ready-to-use app and theme templates\n    ├── apps/             # 9 app templates\n    └── themes/           # 8 theme templates\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhen you ask your AI to build a Streamlit app, it reads the relevant \u003Ccode\u003ESKILL.md\u003C/code\u003E file and follows the instructions inside. This means the AI uses current APIs, proper caching patterns, and Streamlit-specific best practices — because the skill told it exactly what to do.\u003C/p\u003E\n","\u003Ch3\u003EHow Routing Works\u003C/h3\u003E\n","\u003Cp\u003EYou don't need to tell the AI which skill to use. The parent skill (\u003Ccode\u003Edeveloping-with-streamlit\u003C/code\u003E) acts as a router that matches keywords in your prompt to the right sub-skill. The diagram below shows this flow end-to-end: your prompt enters on the left, the parent skill identifies relevant keywords and fans out to one or more sub-skills, and the AI uses those skills to generate code that follows current best practices.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-streamlit-apps-with-agent-skills/streamlit-agent-skills-workflow.png?v=d4e4cfb8\" alt=\"Agent skills workflow — from prompt to parent skill router to sub-skills to production-ready Streamlit code\"\u003E\u003C/p\u003E\n","\u003Cp\u003EHere's how it works in practice:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EYour Prompt\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EMatched Keywords\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESub-skill Loaded\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"add a sidebar with filters"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Esidebar, filters\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eusing-streamlit-layouts\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"build a chatbot"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Echatbot\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ebuilding-streamlit-chat-ui\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"it's slow, add caching"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Eslow, caching\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eoptimizing-streamlit-performance\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"connect to Snowflake"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Econnecting-streamlit-to-snowflake\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EIf your prompt touches multiple areas — like "build a dashboard with a sidebar and charts" — the parent skill loads multiple sub-skills at once so the AI has all the code examples and guidelines it needs.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EInstall the Skills\u003C/h2\u003E\n","\u003Cp\u003EInstallation depends on which AI coding assistant you use. Pick your tool below.\u003C/p\u003E\n","\u003Ch3\u003ESnowflake CoCo (Snowflake)\u003C/h3\u003E\n","\u003Cp\u003EGood news — the skills are already bundled with Snowflake CoCo under the name \u003Ccode\u003Edeveloping-with-streamlit\u003C/code\u003E. No installation needed. You can verify that they're available by running:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecortex skill list\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELook for \u003Ccode\u003Edeveloping-with-streamlit\u003C/code\u003E under \u003Ccode\u003E[BUNDLED]\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003EClaude Code\u003C/h3\u003E\n","\u003Cp\u003ECopy the skill folder to your \u003Ca href=\"https://docs.anthropic.com/en/docs/claude-code/skills\"\u003EClaude Code skills directory\u003C/a\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit clone https://github.com/streamlit/agent-skills.git\ncp -r agent-skills/developing-with-streamlit ~/.claude/skills/\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECursor\u003C/h3\u003E\n","\u003Cp\u003ECopy the skill folder to your \u003Ca href=\"https://cursor.com/docs/context/skills\"\u003ECursor skills directory\u003C/a\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit clone https://github.com/streamlit/agent-skills.git\ncp -r agent-skills/developing-with-streamlit ~/.cursor/skills/\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EOther AI Assistants\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAgent\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESkills Folder\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDocumentation\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOpenAI Codex\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E.codex/skills/\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://developers.openai.com/codex/skills/\"\u003ECodex Skills Docs\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGemini CLI\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E.gemini/skills/\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://geminicli.com/docs/cli/skills/\"\u003EGemini CLI Skills Docs\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EThe same \u003Ccode\u003Edeveloping-with-streamlit\u003C/code\u003E folder works with any assistant that supports the \u003Ca href=\"https://agentskills.io/specification\"\u003Eagent skills format\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EThe Skill Map\u003C/h2\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Edeveloping-with-streamlit\u003C/code\u003E skill contains 17 specialized sub-skills. Here's what each one does and when it activates.\u003C/p\u003E\n","\u003Ch3\u003EUI & Layout Skills\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESkill\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat It Does\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EActivates When You Say...\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ebuilding-streamlit-chat-ui\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EChat interfaces, chatbots, AI assistants\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"build a chatbot," "add chat interface"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ebuilding-streamlit-dashboards\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EKPI cards, metrics, dashboard layouts\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"create a dashboard," "show metrics"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ebuilding-streamlit-multipage-apps\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMulti-page app structure with \u003Ccode\u003Est.navigation\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"multipage app," "add navigation"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eusing-streamlit-layouts\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESidebar, columns, containers, dialogs\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"add a sidebar," "two-column layout"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Echoosing-streamlit-selection-widgets\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPicking the right widget (selectbox, radio, etc.)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"dropdown menu," "let users pick"\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EData & Display Skills\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESkill\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat It Does\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EActivates When You Say...\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Edisplaying-streamlit-data\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDataFrames, column config, charts\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"show a table," "add a chart"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Econnecting-streamlit-to-snowflake\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Est.connection("snowflake")\u003C/code\u003E setup\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"connect to Snowflake," "query my data"\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EStyle & Design Skills\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESkill\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat It Does\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EActivates When You Say...\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ecreating-streamlit-themes\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETheme configuration, colors, fonts, light/dark modes\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"change the theme," "dark mode"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eimproving-streamlit-design\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIcons, badges, spacing, text styling\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"make it look better," "add icons"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eusing-streamlit-markdown\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EColored text, badges, LaTeX, markdown features\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"colored text," "add a badge"\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EPerformance & Code Skills\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESkill\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat It Does\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EActivates When You Say...\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eoptimizing-streamlit-performance\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECaching, fragments, forms, performance tuning\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"it's slow," "add caching"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eorganizing-streamlit-code\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESeparating UI from logic, clean modules\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"organize the code," "refactor"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eusing-streamlit-session-state\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESession state, widget keys, callbacks\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"remember user input," "persist state"\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EComponents & Tooling Skills\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESkill\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat It Does\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EActivates When You Say...\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ebuilding-streamlit-custom-components-v2\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECustom components with HTML/JS/CSS (v2 API)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"custom component," "embed JavaScript"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eusing-streamlit-custom-components\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThird-party components from the community\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"is there a component for...," "community widget"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eusing-streamlit-cli\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECLI commands, running and configuring apps\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"how to run," "streamlit config"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esetting-up-streamlit-environment\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPython environment setup\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"set up my environment," "install streamlit"\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EYou don't need to memorize this table. Just describe what you want, and the parent skill routes to the right sub-skill automatically.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild a Dashboard\u003C/h2\u003E\n","\u003Cp\u003ELet's put the skills to work. We'll ask our AI assistant to build a dashboard and see how the skills improve the output.\u003C/p\u003E\n","\u003Ch3\u003EThe Prompt\u003C/h3\u003E\n","\u003Cp\u003EOpen your AI coding assistant and type:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EBuild a Streamlit dashboard that shows sales metrics. Include KPI cards\nat the top, a line chart for trends over time, and sidebar filters for\ndate range. Use sample data so I can run it immediately.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhat the Skills Do Behind the Scenes\u003C/h3\u003E\n","\u003Cp\u003EWhen the AI receives this prompt, the parent skill routes to three sub-skills:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Ebuilding-streamlit-dashboards\u003C/code\u003E\u003C/strong\u003E — Provides the KPI card pattern using \u003Ccode\u003Est.metric(border=True)\u003C/code\u003E and \u003Ccode\u003Est.columns\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Edisplaying-streamlit-data\u003C/code\u003E\u003C/strong\u003E — Provides chart patterns using \u003Ccode\u003Est.line_chart\u003C/code\u003E with proper data formatting\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Eoptimizing-streamlit-performance\u003C/code\u003E\u003C/strong\u003E — Ensures \u003Ccode\u003E@st.cache_data\u003C/code\u003E is added to the data loading function\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EWhat You Get\u003C/h3\u003E\n","\u003Cp\u003EThe AI generates something like this:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport streamlit as st\nimport pandas as pd\nimport numpy as np\nimport datetime\n\nst.set_page_config(\n    page_title="Sales Dashboard",\n    page_icon=":material/bar_chart:",\n    layout="wide"\n)\n\n# --- Sample Data ---\n@st.cache_data\ndef load_data():\n    dates = pd.date_range("2024-01-01", periods=365, freq="D")\n    np.random.seed(42)\n    return pd.DataFrame({\n        "date": dates,\n        "revenue": np.random.lognormal(9.2, 0.3, len(dates)),\n        "orders": np.random.poisson(50, len(dates)),\n        "customers": np.random.poisson(30, len(dates)),\n    })\n\ndf = load_data()\n\n# --- Sidebar Filters ---\nwith st.sidebar:\n    st.header("Filters")\n    date_range = st.date_input(\n        "Date range",\n        value=(df["date"].min(), df["date"].max()),\n        min_value=df["date"].min(),\n        max_value=df["date"].max(),\n    )\n\n# Apply filter\nif len(date_range) == 2:\n    mask = (df["date"] >= pd.Timestamp(date_range[0])) & (\n        df["date"] <= pd.Timestamp(date_range[1])\n    )\n    filtered = df[mask]\nelse:\n    filtered = df\n\n# --- KPI Cards ---\nst.title("Sales Dashboard")\n\ncols = st.columns(3)\ncols[0].metric("Total Revenue", f"${filtered['revenue'].sum():,.0f}", border=True)\ncols[1].metric("Total Orders", f"{filtered['orders'].sum():,}", border=True)\ncols[2].metric("Unique Customers", f"{filtered['customers'].sum():,}", border=True)\n\n# --- Trend Chart ---\nst.subheader("Revenue Over Time")\nst.line_chart(filtered.set_index("date")["revenue"])\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhat the Skills Added\u003C/h3\u003E\n","\u003Cp\u003EHere's what changed compared to what an unaided AI would generate. Each comparison below shows the "without skills" version first, followed by the "with skills" version:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EData loading — without vs. with skills:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ECaching tells Streamlit to store the result of a function so it doesn't re-run every time the page refreshes. The \u003Ccode\u003E@st.cache_data\u003C/code\u003E line below is a Python decorator — a one-line annotation you place above a function to change its behavior.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# WITHOUT skills: no caching, data reloads on every widget click\ndef load_data():\n    dates = pd.date_range("2024-01-01", periods=365, freq="D")\n    return pd.DataFrame({...})\n\n# WITH skills: cached, loads once and stays in memory\n@st.cache_data\ndef load_data():\n    dates = pd.date_range("2024-01-01", periods=365, freq="D")\n    return pd.DataFrame({...})\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPage config — without vs. with skills:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# WITHOUT skills: missing entirely, or minimal\nst.title("Dashboard")\n\n# WITH skills: full config with wide layout and material icons\nst.set_page_config(\n    page_title="Sales Dashboard",\n    page_icon=":material/bar_chart:",\n    layout="wide"\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDate filter — without vs. with skills:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# WITHOUT skills: unbounded, users can pick impossible dates\ndate_range = st.date_input("Date range")\n\n# WITH skills: bounded to actual data range\ndate_range = st.date_input(\n    "Date range",\n    value=(df["date"].min(), df["date"].max()),\n    min_value=df["date"].min(),\n    max_value=df["date"].max(),\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEach change is small on its own, but they compound fast. An app without caching feels sluggish. An app without bounded date inputs breaks when a user selects a date in the future like December 2030. An app without \u003Ccode\u003Elayout="wide"\u003C/code\u003E wastes half the screen on a dashboard. The skills prevent all of these issues because the AI follows proven patterns instead of improvising.\u003C/p\u003E\n","\u003Ch3\u003ERun It\u003C/h3\u003E\n","\u003Cp\u003ESave the code as \u003Ccode\u003Eapp.py\u003C/code\u003E and run:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Estreamlit run app.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see Streamlit start up in your terminal:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E  You can now view your Streamlit app in your browser.\n\n  Local URL: http://localhost:8501\n  Network URL: http://192.168.1.100:8501\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOpen \u003Ccode\u003Ehttp://localhost:8501\u003C/code\u003E in your browser. You should see a dashboard with three KPI cards, a line chart, and a sidebar date filter — all working out of the box.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-streamlit-apps-with-agent-skills/dashboard-app.png?v=d4e4cfb8\" alt=\"\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild a Chat App\u003C/h2\u003E\n","\u003Cp\u003ENow let's build something completely different — a chatbot with streaming responses.\u003C/p\u003E\n","\u003Ch3\u003EThe Prompt\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003ECreate a Streamlit chat app with streaming responses. Use a simple\necho bot for now (repeat back what the user types, word by word, to\nshow the streaming effect). Include a sidebar with a clear-chat button.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhat the Skills Do\u003C/h3\u003E\n","\u003Cp\u003EThe parent skill routes to:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Ebuilding-streamlit-chat-ui\u003C/code\u003E\u003C/strong\u003E — Provides the \u003Ccode\u003Est.chat_message\u003C/code\u003E + \u003Ccode\u003Est.chat_input\u003C/code\u003E pattern with message history\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Eusing-streamlit-session-state\u003C/code\u003E\u003C/strong\u003E — Ensures proper initialization with \u003Ccode\u003Est.session_state.setdefault()\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EWhat You Get\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport streamlit as st\nimport time\n\nst.set_page_config(\n    page_title="Chat App",\n    page_icon=":material/chat:",\n    layout="centered"\n)\n\n# --- Sidebar ---\nwith st.sidebar:\n    st.header("Settings")\n    if st.button("Clear chat", width="stretch"):\n        st.session_state.messages = []\n        st.rerun()\n\n# --- Initialize State ---\nst.session_state.setdefault("messages", [])\n\nst.title("Chat App")\n\n# --- Display History ---\nfor msg in st.session_state.messages:\n    st.chat_message(msg["role"]).write(msg["content"])\n\n# --- Chat Input ---\nif prompt := st.chat_input("Type a message..."):\n    # Add user message\n    st.session_state.messages.append({"role": "user", "content": prompt})\n    st.chat_message("user").write(prompt)\n\n    # Stream the response word by word\n    def response_stream():\n        for word in f"You said: {prompt}".split():\n            yield word + " "\n            time.sleep(0.05)\n\n    with st.chat_message("assistant"):\n        full_response = st.write_stream(response_stream())\n\n    st.session_state.messages.append(\n        {"role": "assistant", "content": full_response}\n    )\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EKey Patterns the Skills Taught the AI\u003C/h3\u003E\n","\u003Cp\u003ELet's look at the specific patterns the skills provided:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESession state initialization:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# The skills teach this pattern:\nst.session_state.setdefault("messages", [])\n\n# Instead of this older pattern:\nif "messages" not in st.session_state:\n    st.session_state.messages = []\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBoth work, but \u003Ccode\u003Esetdefault\u003C/code\u003E is more concise and is the pattern the skills teach.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStreaming with \u003Ccode\u003Est.write_stream\u003C/code\u003E:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# The skills teach this clean pattern:\nfull_response = st.write_stream(response_stream())\n\n# Instead of manually building the response:\nplaceholder = st.empty()\nfull_response = ""\nfor chunk in response_stream():\n    full_response += chunk\n    placeholder.markdown(full_response)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Est.write_stream\u003C/code\u003E function handles all the complexity of displaying streaming text. It replaces several lines of manual placeholder logic with a single call.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EChat message pattern:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Walrus operator for clean flow:\nif prompt := st.chat_input("Type a message..."):\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis assigns the input to \u003Ccode\u003Eprompt\u003C/code\u003E and checks if it's non-empty in one line.\u003C/p\u003E\n","\u003Ch3\u003ERun It\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Estreamlit run app.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EType a message and watch it stream back word by word. Click "Clear chat" in the sidebar to reset the conversation.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-streamlit-apps-with-agent-skills/chatbot-app.png?v=d4e4cfb8\" alt=\"\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EApply a Theme\u003C/h2\u003E\n","\u003Cp\u003EEvery Streamlit app starts with the default theme, but the skills include 8 ready-made theme templates you can apply instantly.\u003C/p\u003E\n","\u003Ch3\u003EAvailable Themes\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Ecreating-streamlit-themes\u003C/code\u003E skill provides these templates:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETheme\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EStyle\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EBest For\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esnowflake\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBlue and white, professional\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake-branded apps\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Edracula\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDark purple with green/pink accents\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDeveloper tools\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Enord\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECool blue-gray, muted palette\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EClean, minimal apps\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Estripe\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECrisp white with purple accents\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFinancial, business apps\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esolarized-light\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWarm light with teal accents\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EReading-heavy apps\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Espotify\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDark with bright green\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMedia, entertainment apps\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Egithub\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELight, high contrast\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDocumentation, code tools\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eminimal\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPure black and white\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EContent-first apps\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EApply a Theme\u003C/h3\u003E\n","\u003Cp\u003ETo apply a theme, ask your AI assistant:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EApply the dracula theme to my app\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Ecreating-streamlit-themes\u003C/code\u003E skill activates and the AI creates a \u003Ccode\u003E.streamlit/config.toml\u003C/code\u003E file:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-toml\"\u003E[theme]\nprimaryColor = "#bd93f9"\nbackgroundColor = "#282a36"\nsecondaryBackgroundColor = "#44475a"\ntextColor = "#f8f8f2"\nfont = "sans serif"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThat's it. Restart your app and the theme is applied. No CSS, no custom components — just a config file.\u003C/p\u003E\n","\u003Cp\u003EStreamlit reads \u003Ccode\u003Econfig.toml\u003C/code\u003E on startup and applies these five settings (four colors and a font) to every widget, chart, and layout element. This means a single file controls the look of your entire app — buttons, sliders, DataFrames, charts, sidebar, and text all inherit from these settings automatically.\u003C/p\u003E\n","\u003Ch3\u003ELight and Dark Mode\u003C/h3\u003E\n","\u003Cp\u003EStreamlit supports both light and dark modes. The theme in \u003Ccode\u003Econfig.toml\u003C/code\u003E sets the default, but users can toggle between modes from the app menu (the "\u003Cstrong\u003E⋮\u003C/strong\u003E" icon in the top right corner, then \u003Cstrong\u003ESettings\u003C/strong\u003E).\u003C/p\u003E\n","\u003Cp\u003EIf you want to force a specific mode, add:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-toml\"\u003E[theme]\nbase = "dark"   # or "light"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EUsing Google Fonts\u003C/h3\u003E\n","\u003Cp\u003EThe theme templates also include Google Font configurations for a more polished look. Ask your AI:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EApply the stripe theme with the Inter font\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe skill knows to configure the font in the theme settings for a clean, professional appearance.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUse the App Templates\u003C/h2\u003E\n","\u003Cp\u003ESometimes you don't want to start from scratch. The skills include 9 ready-to-use app templates that give you a working starting point.\u003C/p\u003E\n","\u003Ch3\u003EAvailable Templates\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELocal Templates (6):\u003C/strong\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETemplate\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat It Includes\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBasic Dashboard\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EKPIs, line chart, sidebar filters, sample data\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAdvanced Dashboard\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMultiple chart types, data tables, export\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EChat App\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EChat UI with message history, streaming\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EData Explorer\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFile upload, data profiling, column stats\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMultipage App\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENavigation, multiple pages, shared state\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EForm App\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EInput forms, validation, submission handling\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESnowflake Templates (3):\u003C/strong\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETemplate\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat It Includes\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake Dashboard\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Est.connection\u003C/code\u003E, KPIs from live queries\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake Chat\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex AI-powered chatbot\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake Explorer\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBrowse tables, run queries, view results\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EUse a Template\u003C/h3\u003E\n","\u003Cp\u003EAsk your AI assistant:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EStart a new app from the basic dashboard template\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe AI reads the template from the skills directory and creates your project files. Templates include sample data so you can run them immediately without setting up a database.\u003C/p\u003E\n","\u003Ch3\u003EWhen to Use Templates vs. Prompting\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EUse a template\u003C/strong\u003E when you want a quick starting point and plan to customize from there\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUse a prompt\u003C/strong\u003E when you have a specific vision that doesn't match any template\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECombine both\u003C/strong\u003E — start from a template, then ask the AI to modify it ("change this dashboard to show inventory data instead of sales")\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConnect to Snowflake\u003C/h2\u003E\n","\u003Cp\u003EIf your data lives in Snowflake, the \u003Ccode\u003Econnecting-streamlit-to-snowflake\u003C/code\u003E skill teaches the AI how to set up the connection properly.\u003C/p\u003E\n","\u003Ch3\u003EThe Prompt\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003EConnect my dashboard to Snowflake and load data from the ORDERS table\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhat the Skill Provides\u003C/h3\u003E\n","\u003Cp\u003EThe AI creates two files:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Ccode\u003E.streamlit/secrets.toml.example\u003C/code\u003E\u003C/strong\u003E (rename to \u003Ccode\u003Esecrets.toml\u003C/code\u003E and fill in your credentials):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-toml\"\u003E[connections.snowflake]\naccount = "myorg-myaccount"\nuser = "myuser"\npassword = "mypassword"\nwarehouse = "COMPUTE_WH"\ndatabase = "MY_DB"\nschema = "PUBLIC"\nrole = "MY_ROLE"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Ccode\u003Eapp.py\u003C/code\u003E\u003C/strong\u003E with a Snowflake connection:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport streamlit as st\n\nst.set_page_config(page_title="Snowflake Dashboard", layout="wide")\n\n# Connect to Snowflake\nconn = st.connection("snowflake")\n\n# Load data with caching\n@st.cache_data(ttl=600)\ndef load_orders():\n    return conn.query("SELECT * FROM orders LIMIT 1000")\n\ndf = load_orders()\n\n# Display\nst.title("Orders Dashboard")\nst.dataframe(df, width="stretch")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EKey Things the Skill Ensures\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Ccode\u003Est.connection("snowflake")\u003C/code\u003E\u003C/strong\u003E — The modern way to connect. The skill prevents the AI from using older patterns like \u003Ccode\u003Esnowflake.connector.connect()\u003C/code\u003E directly, which requires more boilerplate code and doesn't integrate with Streamlit's caching or secrets management.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Ccode\u003E@st.cache_data(ttl=600)\u003C/code\u003E\u003C/strong\u003E — Caches query results for 10 minutes. Without this, every widget interaction would re-run the query.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EParameterized queries\u003C/strong\u003E — When user input is involved, the skill teaches the AI to use parameterized queries instead of f-strings, which prevents SQL injection:\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# UNSAFE — f-string opens you to SQL injection:\ndf = conn.query(f"SELECT * FROM orders WHERE status = '{user_input}'")\n\n# SAFE — parameterized query the skill teaches:\ndf = conn.query(\n    "SELECT * FROM orders WHERE status = :status",\n    params={"status": user_input},\n)\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003E\u003Cstrong\u003ESecrets in \u003Ccode\u003E.streamlit/secrets.toml\u003C/code\u003E\u003C/strong\u003E — Credentials stay out of your code. The skill also creates a \u003Ccode\u003E.example\u003C/code\u003E file so you know what to fill in.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EImportant: Snowflake Returns Uppercase Column Names\u003C/h3\u003E\n","\u003Cp\u003EOne gotcha the skill handles: when you query Snowflake, pandas DataFrames come back with UPPERCASE column names. So \u003Ccode\u003ESELECT order_id FROM orders\u003C/code\u003E returns a column named \u003Ccode\u003EORDER_ID\u003C/code\u003E, not \u003Ccode\u003Eorder_id\u003C/code\u003E. The skill ensures the AI references columns in uppercase.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003EYou've learned how agent skills transform your AI coding assistant from a general-purpose code generator into a Streamlit specialist. By loading domain-specific knowledge about current APIs, caching patterns, and design best practices, the skills ensure your AI writes Streamlit code that works correctly from the start.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAgent skills are instruction sets that give AI assistants specialized, up-to-date knowledge\u003C/li\u003E\u003Cli\u003EThe \u003Ccode\u003Edeveloping-with-streamlit\u003C/code\u003E skill collection contains 17 sub-skills that cover the full Streamlit development workflow\u003C/li\u003E\u003Cli\u003ESkills activate automatically based on your prompt — you describe what you want and the right skill loads\u003C/li\u003E\u003Cli\u003EThe skills ensure modern patterns: \u003Ccode\u003E@st.cache_data\u003C/code\u003E, \u003Ccode\u003Est.write_stream\u003C/code\u003E, \u003Ccode\u003Ewidth="stretch"\u003C/code\u003E, \u003Ccode\u003Est.session_state.setdefault()\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EReady-made templates (9 apps, 8 themes) give you polished starting points\u003C/li\u003E\u003Cli\u003ESnowflake connections use \u003Ccode\u003Est.connection("snowflake")\u003C/code\u003E with proper caching and parameterized queries\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ENext Steps\u003C/h3\u003E\n","\u003Cp\u003ENow that you have the skills installed, here are concrete things to try:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ESwap in real data\u003C/strong\u003E — Take the dashboard you built and replace the sample data with a live Snowflake query using \u003Ccode\u003Est.connection("snowflake")\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECombine apps\u003C/strong\u003E — Ask the AI to "add a chat sidebar to my dashboard" and watch the routing load both \u003Ccode\u003Ebuilding-streamlit-chat-ui\u003C/code\u003E and \u003Ccode\u003Ebuilding-streamlit-dashboards\u003C/code\u003E simultaneously\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EContribute a skill\u003C/strong\u003E — The \u003Ca href=\"https://github.com/streamlit/agent-skills\"\u003Estreamlit/agent-skills\u003C/a\u003E repository accepts pull requests. If you find a pattern the skills don't cover, add it\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n","\u003Cp\u003EDocumentation:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/streamlit/agent-skills\"\u003Estreamlit/agent-skills on GitHub\u003C/a\u003E — The full skill collection\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://agentskills.io/specification\"\u003EAgent Skills Specification\u003C/a\u003E — How skills are structured\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.streamlit.io\"\u003EStreamlit Documentation\u003C/a\u003E — Official Streamlit docs\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.streamlit.io/llms.txt\"\u003EStreamlit LLM Docs\u003C/a\u003E — Docs in LLM-optimized format\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EAI Coding Assistants:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ESnowflake CoCo\u003C/a\u003E — Snowflake's AI coding CLI (skills pre-installed)\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.anthropic.com/en/docs/claude-code\"\u003EClaude Code\u003C/a\u003E — Anthropic's terminal coding tool\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://cursor.com\"\u003ECursor\u003C/a\u003E — AI-native code editor\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Build Streamlit Apps with Agent Skills",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nWhen you ask an AI coding assistant to \"build a Streamlit dashboard,\" it does its best. But the assistant is working from general knowledge that may be months or years out of date. \n\nHere's what that looks like in practice:\n\n```python\n# What an unaided AI often generates:\nst.dataframe(df, use_container_width=True)  # deprecated parameter\nif \"messages\" not in st.session_state:      # verbose initialization\n    st.session_state.messages = []\n\n# What you actually want:\nst.dataframe(df, width=\"stretch\")           # current API\nst.session_state.setdefault(\"messages\", []) # concise one-liner\n```\n\nBeyond deprecated APIs, the AI might skip caching entirely — meaning your app re-runs expensive queries every time a user clicks a filter instead of reusing results it already fetched. Or it might initialize session state with verbose `if/else` blocks instead of the one-liner `setdefault()` pattern.\n\n**Agent skills** fix this by giving your AI assistant specialized, up-to-date knowledge about Streamlit development. Think of them as reference guides that your AI reads before writing code. Instead of guessing how to build a chat interface or configure a theme, the AI loads a skill that contains the exact patterns, code examples, and guidelines for that task.\n\nThe [streamlit/agent-skills](https://github.com/streamlit/agent-skills) repository is an open-source collection of 17 specialized skills for Streamlit development. They cover everything from building dashboards and chat UIs to connecting to Snowflake and creating custom components. The skills work with any AI coding assistant that supports the agent skills format — including Claude Code, Cursor, and Snowflake CoCo (where they come pre-installed).\n\nThis guide walks you through installing the skills, understanding what each one does, and using them to build real apps. No prior experience with agent skills is required.\n\nHere's a schematic summary of the Streamlit app creation process using the agent skills:\n\n\u003C!-- Diagram: https://excalidraw.com/#json=U51O43YAwXyA-pFGyDIRX,cBQiyuTWzTpt9-yB8xWISA --\u003E\n![diagram](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-streamlit-apps-with-agent-skills/diagram.png?v=d4e4cfb8)\n\n### What You'll Learn\n- What agent skills are and how they help your AI assistant write better Streamlit code\n- How to install the skills for your specific AI coding assistant\n- How the 17 sub-skills are organized and when each one activates\n- How to build a dashboard and a chatbot using the skills\n- How to apply themes and use ready-made app templates\n- How to connect your app to Snowflake\n\n### What You'll Build\nTwo working Streamlit apps — a dashboard with KPI metrics and charts, and a chatbot with streaming responses — built with the help of agent skills.\n\n### Prerequisites\n- An AI coding assistant (any of: [Snowflake CoCo](https://signup.snowflake.com/cortex-code?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides), [Claude Code](https://docs.anthropic.com/en/docs/claude-code), [Cursor](https://cursor.com))\n- Python 3 installed on your machine\n- Streamlit installed (`pip install streamlit`)\n- Basic familiarity with Python (you don't need to be an expert, know enough to make edits when necessary)\n\n\u003C!-- ------------------------ --\u003E\n## What Are Agent Skills?\n\nBefore we dive in, let's understand what agent skills actually are and why they matter.\n\n### The Problem\n\nAI coding assistants are trained on massive amounts of code, but their knowledge has a cutoff date. When you ask your AI to build a Streamlit app, it might generate code based on patterns that were correct a year ago but are outdated today.\n\nThe deprecated API in the Overview is just one example. Here are other common issues with unaided AI-generated Streamlit code:\n- Missing `@st.cache_data` on data-loading functions, so the app re-fetches data on every user interaction instead of reusing cached results\n- Verbose session state initialization (`if \"key\" not in st.session_state: ...`) instead of the one-liner `setdefault()` pattern\n- Outdated navigation patterns instead of the current `st.navigation` and `st.Page` API\n\n### The Solution: Agent Skills\n\nAn agent skill is a folder containing a `SKILL.md` file and optional supporting resources:\n\n```\ndeveloping-with-streamlit/\n├── SKILL.md              # Instructions the AI reads\n├── skills/               # 17 specialized sub-skills\n│   ├── building-streamlit-chat-ui/\n│   ├── building-streamlit-dashboards/\n│   ├── optimizing-streamlit-performance/\n│   └── ...\n└── templates/            # Ready-to-use app and theme templates\n    ├── apps/             # 9 app templates\n    └── themes/           # 8 theme templates\n```\n\nWhen you ask your AI to build a Streamlit app, it reads the relevant `SKILL.md` file and follows the instructions inside. This means the AI uses current APIs, proper caching patterns, and Streamlit-specific best practices — because the skill told it exactly what to do.\n\n### How Routing Works\n\nYou don't need to tell the AI which skill to use. The parent skill (`developing-with-streamlit`) acts as a router that matches keywords in your prompt to the right sub-skill. The diagram below shows this flow end-to-end: your prompt enters on the left, the parent skill identifies relevant keywords and fans out to one or more sub-skills, and the AI uses those skills to generate code that follows current best practices.\n\n![Agent skills workflow — from prompt to parent skill router to sub-skills to production-ready Streamlit code](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-streamlit-apps-with-agent-skills/streamlit-agent-skills-workflow.png?v=d4e4cfb8)\n\nHere's how it works in practice:\n\n| Your Prompt | Matched Keywords | Sub-skill Loaded |\n|---|---|---|\n| \"add a sidebar with filters\" | sidebar, filters | `using-streamlit-layouts` |\n| \"build a chatbot\" | chatbot | `building-streamlit-chat-ui` |\n| \"it's slow, add caching\" | slow, caching | `optimizing-streamlit-performance` |\n| \"connect to Snowflake\" | Snowflake | `connecting-streamlit-to-snowflake` |\n\nIf your prompt touches multiple areas — like \"build a dashboard with a sidebar and charts\" — the parent skill loads multiple sub-skills at once so the AI has all the code examples and guidelines it needs.\n\n\u003C!-- ------------------------ --\u003E\n## Install the Skills\n\nInstallation depends on which AI coding assistant you use. Pick your tool below.\n\n### Snowflake CoCo (Snowflake)\n\nGood news — the skills are already bundled with Snowflake CoCo under the name `developing-with-streamlit`. No installation needed. You can verify that they're available by running:\n\n```bash\ncortex skill list\n```\n\nLook for `developing-with-streamlit` under `[BUNDLED]`.\n\n### Claude Code\n\nCopy the skill folder to your [Claude Code skills directory](https://docs.anthropic.com/en/docs/claude-code/skills):\n\n```bash\ngit clone https://github.com/streamlit/agent-skills.git\ncp -r agent-skills/developing-with-streamlit ~/.claude/skills/\n```\n\n### Cursor\n\nCopy the skill folder to your [Cursor skills directory](https://cursor.com/docs/context/skills):\n\n```bash\ngit clone https://github.com/streamlit/agent-skills.git\ncp -r agent-skills/developing-with-streamlit ~/.cursor/skills/\n```\n\n### Other AI Assistants\n\n| Agent | Skills Folder | Documentation |\n|-------|---------------|---------------|\n| OpenAI Codex | `.codex/skills/` | [Codex Skills Docs](https://developers.openai.com/codex/skills/) |\n| Gemini CLI | `.gemini/skills/` | [Gemini CLI Skills Docs](https://geminicli.com/docs/cli/skills/) |\n\nThe same `developing-with-streamlit` folder works with any assistant that supports the [agent skills format](https://agentskills.io/specification).\n\n\u003C!-- ------------------------ --\u003E\n## The Skill Map\n\nThe `developing-with-streamlit` skill contains 17 specialized sub-skills. Here's what each one does and when it activates.\n\n### UI & Layout Skills\n\n| Skill | What It Does | Activates When You Say... |\n|-------|-------------|--------------------------|\n| `building-streamlit-chat-ui` | Chat interfaces, chatbots, AI assistants | \"build a chatbot,\" \"add chat interface\" |\n| `building-streamlit-dashboards` | KPI cards, metrics, dashboard layouts | \"create a dashboard,\" \"show metrics\" |\n| `building-streamlit-multipage-apps` | Multi-page app structure with `st.navigation` | \"multipage app,\" \"add navigation\" |\n| `using-streamlit-layouts` | Sidebar, columns, containers, dialogs | \"add a sidebar,\" \"two-column layout\" |\n| `choosing-streamlit-selection-widgets` | Picking the right widget (selectbox, radio, etc.) | \"dropdown menu,\" \"let users pick\" |\n\n### Data & Display Skills\n\n| Skill | What It Does | Activates When You Say... |\n|-------|-------------|--------------------------|\n| `displaying-streamlit-data` | DataFrames, column config, charts | \"show a table,\" \"add a chart\" |\n| `connecting-streamlit-to-snowflake` | `st.connection(\"snowflake\")` setup | \"connect to Snowflake,\" \"query my data\" |\n\n### Style & Design Skills\n\n| Skill | What It Does | Activates When You Say... |\n|-------|-------------|--------------------------|\n| `creating-streamlit-themes` | Theme configuration, colors, fonts, light/dark modes | \"change the theme,\" \"dark mode\" |\n| `improving-streamlit-design` | Icons, badges, spacing, text styling | \"make it look better,\" \"add icons\" |\n| `using-streamlit-markdown` | Colored text, badges, LaTeX, markdown features | \"colored text,\" \"add a badge\" |\n\n### Performance & Code Skills\n\n| Skill | What It Does | Activates When You Say... |\n|-------|-------------|--------------------------|\n| `optimizing-streamlit-performance` | Caching, fragments, forms, performance tuning | \"it's slow,\" \"add caching\" |\n| `organizing-streamlit-code` | Separating UI from logic, clean modules | \"organize the code,\" \"refactor\" |\n| `using-streamlit-session-state` | Session state, widget keys, callbacks | \"remember user input,\" \"persist state\" |\n\n### Components & Tooling Skills\n\n| Skill | What It Does | Activates When You Say... |\n|-------|-------------|--------------------------|\n| `building-streamlit-custom-components-v2` | Custom components with HTML/JS/CSS (v2 API) | \"custom component,\" \"embed JavaScript\" |\n| `using-streamlit-custom-components` | Third-party components from the community | \"is there a component for...,\" \"community widget\" |\n| `using-streamlit-cli` | CLI commands, running and configuring apps | \"how to run,\" \"streamlit config\" |\n| `setting-up-streamlit-environment` | Python environment setup | \"set up my environment,\" \"install streamlit\" |\n\nYou don't need to memorize this table. Just describe what you want, and the parent skill routes to the right sub-skill automatically.\n\n\u003C!-- ------------------------ --\u003E\n## Build a Dashboard\n\nLet's put the skills to work. We'll ask our AI assistant to build a dashboard and see how the skills improve the output.\n\n### The Prompt\n\nOpen your AI coding assistant and type:\n\n```\nBuild a Streamlit dashboard that shows sales metrics. Include KPI cards\nat the top, a line chart for trends over time, and sidebar filters for\ndate range. Use sample data so I can run it immediately.\n```\n\n### What the Skills Do Behind the Scenes\n\nWhen the AI receives this prompt, the parent skill routes to three sub-skills:\n\n1. **`building-streamlit-dashboards`** — Provides the KPI card pattern using `st.metric(border=True)` and `st.columns`\n2. **`displaying-streamlit-data`** — Provides chart patterns using `st.line_chart` with proper data formatting\n3. **`optimizing-streamlit-performance`** — Ensures `@st.cache_data` is added to the data loading function\n\n### What You Get\n\nThe AI generates something like this:\n\n```python\nimport streamlit as st\nimport pandas as pd\nimport numpy as np\nimport datetime\n\nst.set_page_config(\n    page_title=\"Sales Dashboard\",\n    page_icon=\":material/bar_chart:\",\n    layout=\"wide\"\n)\n\n# --- Sample Data ---\n@st.cache_data\ndef load_data():\n    dates = pd.date_range(\"2024-01-01\", periods=365, freq=\"D\")\n    np.random.seed(42)\n    return pd.DataFrame({\n        \"date\": dates,\n        \"revenue\": np.random.lognormal(9.2, 0.3, len(dates)),\n        \"orders\": np.random.poisson(50, len(dates)),\n        \"customers\": np.random.poisson(30, len(dates)),\n    })\n\ndf = load_data()\n\n# --- Sidebar Filters ---\nwith st.sidebar:\n    st.header(\"Filters\")\n    date_range = st.date_input(\n        \"Date range\",\n        value=(df[\"date\"].min(), df[\"date\"].max()),\n        min_value=df[\"date\"].min(),\n        max_value=df[\"date\"].max(),\n    )\n\n# Apply filter\nif len(date_range) == 2:\n    mask = (df[\"date\"] \u003E= pd.Timestamp(date_range[0])) & (\n        df[\"date\"] \u003C= pd.Timestamp(date_range[1])\n    )\n    filtered = df[mask]\nelse:\n    filtered = df\n\n# --- KPI Cards ---\nst.title(\"Sales Dashboard\")\n\ncols = st.columns(3)\ncols[0].metric(\"Total Revenue\", f\"${filtered['revenue'].sum():,.0f}\", border=True)\ncols[1].metric(\"Total Orders\", f\"{filtered['orders'].sum():,}\", border=True)\ncols[2].metric(\"Unique Customers\", f\"{filtered['customers'].sum():,}\", border=True)\n\n# --- Trend Chart ---\nst.subheader(\"Revenue Over Time\")\nst.line_chart(filtered.set_index(\"date\")[\"revenue\"])\n```\n\n### What the Skills Added\n\nHere's what changed compared to what an unaided AI would generate. Each comparison below shows the \"without skills\" version first, followed by the \"with skills\" version:\n\n**Data loading — without vs. with skills:**\n\nCaching tells Streamlit to store the result of a function so it doesn't re-run every time the page refreshes. The `@st.cache_data` line below is a Python decorator — a one-line annotation you place above a function to change its behavior.\n\n```python\n# WITHOUT skills: no caching, data reloads on every widget click\ndef load_data():\n    dates = pd.date_range(\"2024-01-01\", periods=365, freq=\"D\")\n    return pd.DataFrame({...})\n\n# WITH skills: cached, loads once and stays in memory\n@st.cache_data\ndef load_data():\n    dates = pd.date_range(\"2024-01-01\", periods=365, freq=\"D\")\n    return pd.DataFrame({...})\n```\n\n**Page config — without vs. with skills:**\n```python\n# WITHOUT skills: missing entirely, or minimal\nst.title(\"Dashboard\")\n\n# WITH skills: full config with wide layout and material icons\nst.set_page_config(\n    page_title=\"Sales Dashboard\",\n    page_icon=\":material/bar_chart:\",\n    layout=\"wide\"\n)\n```\n\n**Date filter — without vs. with skills:**\n```python\n# WITHOUT skills: unbounded, users can pick impossible dates\ndate_range = st.date_input(\"Date range\")\n\n# WITH skills: bounded to actual data range\ndate_range = st.date_input(\n    \"Date range\",\n    value=(df[\"date\"].min(), df[\"date\"].max()),\n    min_value=df[\"date\"].min(),\n    max_value=df[\"date\"].max(),\n)\n```\n\nEach change is small on its own, but they compound fast. An app without caching feels sluggish. An app without bounded date inputs breaks when a user selects a date in the future like December 2030. An app without `layout=\"wide\"` wastes half the screen on a dashboard. The skills prevent all of these issues because the AI follows proven patterns instead of improvising.\n\n### Run It\n\nSave the code as `app.py` and run:\n\n```bash\nstreamlit run app.py\n```\n\nYou should see Streamlit start up in your terminal:\n\n```\n  You can now view your Streamlit app in your browser.\n\n  Local URL: http://localhost:8501\n  Network URL: http://192.168.1.100:8501\n```\n\nOpen `http://localhost:8501` in your browser. You should see a dashboard with three KPI cards, a line chart, and a sidebar date filter — all working out of the box.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-streamlit-apps-with-agent-skills/dashboard-app.png?v=d4e4cfb8)\n\n\u003C!-- ------------------------ --\u003E\n## Build a Chat App\n\nNow let's build something completely different — a chatbot with streaming responses.\n\n### The Prompt\n\n```\nCreate a Streamlit chat app with streaming responses. Use a simple\necho bot for now (repeat back what the user types, word by word, to\nshow the streaming effect). Include a sidebar with a clear-chat button.\n```\n\n### What the Skills Do\n\nThe parent skill routes to:\n\n1. **`building-streamlit-chat-ui`** — Provides the `st.chat_message` + `st.chat_input` pattern with message history\n2. **`using-streamlit-session-state`** — Ensures proper initialization with `st.session_state.setdefault()`\n\n### What You Get\n\n```python\nimport streamlit as st\nimport time\n\nst.set_page_config(\n    page_title=\"Chat App\",\n    page_icon=\":material/chat:\",\n    layout=\"centered\"\n)\n\n# --- Sidebar ---\nwith st.sidebar:\n    st.header(\"Settings\")\n    if st.button(\"Clear chat\", width=\"stretch\"):\n        st.session_state.messages = []\n        st.rerun()\n\n# --- Initialize State ---\nst.session_state.setdefault(\"messages\", [])\n\nst.title(\"Chat App\")\n\n# --- Display History ---\nfor msg in st.session_state.messages:\n    st.chat_message(msg[\"role\"]).write(msg[\"content\"])\n\n# --- Chat Input ---\nif prompt := st.chat_input(\"Type a message...\"):\n    # Add user message\n    st.session_state.messages.append({\"role\": \"user\", \"content\": prompt})\n    st.chat_message(\"user\").write(prompt)\n\n    # Stream the response word by word\n    def response_stream():\n        for word in f\"You said: {prompt}\".split():\n            yield word + \" \"\n            time.sleep(0.05)\n\n    with st.chat_message(\"assistant\"):\n        full_response = st.write_stream(response_stream())\n\n    st.session_state.messages.append(\n        {\"role\": \"assistant\", \"content\": full_response}\n    )\n```\n\n### Key Patterns the Skills Taught the AI\n\nLet's look at the specific patterns the skills provided:\n\n**Session state initialization:**\n```python\n# The skills teach this pattern:\nst.session_state.setdefault(\"messages\", [])\n\n# Instead of this older pattern:\nif \"messages\" not in st.session_state:\n    st.session_state.messages = []\n```\n\nBoth work, but `setdefault` is more concise and is the pattern the skills teach.\n\n**Streaming with `st.write_stream`:**\n```python\n# The skills teach this clean pattern:\nfull_response = st.write_stream(response_stream())\n\n# Instead of manually building the response:\nplaceholder = st.empty()\nfull_response = \"\"\nfor chunk in response_stream():\n    full_response += chunk\n    placeholder.markdown(full_response)\n```\n\nThe `st.write_stream` function handles all the complexity of displaying streaming text. It replaces several lines of manual placeholder logic with a single call.\n\n**Chat message pattern:**\n```python\n# Walrus operator for clean flow:\nif prompt := st.chat_input(\"Type a message...\"):\n```\n\nThis assigns the input to `prompt` and checks if it's non-empty in one line.\n\n### Run It\n\n```bash\nstreamlit run app.py\n```\n\nType a message and watch it stream back word by word. Click \"Clear chat\" in the sidebar to reset the conversation.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-streamlit-apps-with-agent-skills/chatbot-app.png?v=d4e4cfb8)\n\n\u003C!-- ------------------------ --\u003E\n## Apply a Theme\n\nEvery Streamlit app starts with the default theme, but the skills include 8 ready-made theme templates you can apply instantly.\n\n### Available Themes\n\nThe `creating-streamlit-themes` skill provides these templates:\n\n| Theme | Style | Best For |\n|-------|-------|----------|\n| `snowflake` | Blue and white, professional | Snowflake-branded apps |\n| `dracula` | Dark purple with green/pink accents | Developer tools |\n| `nord` | Cool blue-gray, muted palette | Clean, minimal apps |\n| `stripe` | Crisp white with purple accents | Financial, business apps |\n| `solarized-light` | Warm light with teal accents | Reading-heavy apps |\n| `spotify` | Dark with bright green | Media, entertainment apps |\n| `github` | Light, high contrast | Documentation, code tools |\n| `minimal` | Pure black and white | Content-first apps |\n\n### Apply a Theme\n\nTo apply a theme, ask your AI assistant:\n\n```\nApply the dracula theme to my app\n```\n\nThe `creating-streamlit-themes` skill activates and the AI creates a `.streamlit/config.toml` file:\n\n```toml\n[theme]\nprimaryColor = \"#bd93f9\"\nbackgroundColor = \"#282a36\"\nsecondaryBackgroundColor = \"#44475a\"\ntextColor = \"#f8f8f2\"\nfont = \"sans serif\"\n```\n\nThat's it. Restart your app and the theme is applied. No CSS, no custom components — just a config file.\n\nStreamlit reads `config.toml` on startup and applies these five settings (four colors and a font) to every widget, chart, and layout element. This means a single file controls the look of your entire app — buttons, sliders, DataFrames, charts, sidebar, and text all inherit from these settings automatically.\n\n### Light and Dark Mode\n\nStreamlit supports both light and dark modes. The theme in `config.toml` sets the default, but users can toggle between modes from the app menu (the \"**⋮**\" icon in the top right corner, then **Settings**).\n\nIf you want to force a specific mode, add:\n\n```toml\n[theme]\nbase = \"dark\"   # or \"light\"\n```\n\n### Using Google Fonts\n\nThe theme templates also include Google Font configurations for a more polished look. Ask your AI:\n\n```\nApply the stripe theme with the Inter font\n```\n\nThe skill knows to configure the font in the theme settings for a clean, professional appearance.\n\n\u003C!-- ------------------------ --\u003E\n## Use the App Templates\n\nSometimes you don't want to start from scratch. The skills include 9 ready-to-use app templates that give you a working starting point.\n\n### Available Templates\n\n**Local Templates (6):**\n\n| Template | What It Includes |\n|----------|-----------------|\n| Basic Dashboard | KPIs, line chart, sidebar filters, sample data |\n| Advanced Dashboard | Multiple chart types, data tables, export |\n| Chat App | Chat UI with message history, streaming |\n| Data Explorer | File upload, data profiling, column stats |\n| Multipage App | Navigation, multiple pages, shared state |\n| Form App | Input forms, validation, submission handling |\n\n**Snowflake Templates (3):**\n\n| Template | What It Includes |\n|----------|-----------------|\n| Snowflake Dashboard | `st.connection`, KPIs from live queries |\n| Snowflake Chat | Cortex AI-powered chatbot |\n| Snowflake Explorer | Browse tables, run queries, view results |\n\n### Use a Template\n\nAsk your AI assistant:\n\n```\nStart a new app from the basic dashboard template\n```\n\nThe AI reads the template from the skills directory and creates your project files. Templates include sample data so you can run them immediately without setting up a database.\n\n### When to Use Templates vs. Prompting\n\n- **Use a template** when you want a quick starting point and plan to customize from there\n- **Use a prompt** when you have a specific vision that doesn't match any template\n- **Combine both** — start from a template, then ask the AI to modify it (\"change this dashboard to show inventory data instead of sales\")\n\n\u003C!-- ------------------------ --\u003E\n## Connect to Snowflake\n\nIf your data lives in Snowflake, the `connecting-streamlit-to-snowflake` skill teaches the AI how to set up the connection properly.\n\n### The Prompt\n\n```\nConnect my dashboard to Snowflake and load data from the ORDERS table\n```\n\n### What the Skill Provides\n\nThe AI creates two files:\n\n**`.streamlit/secrets.toml.example`** (rename to `secrets.toml` and fill in your credentials):\n\n```toml\n[connections.snowflake]\naccount = \"myorg-myaccount\"\nuser = \"myuser\"\npassword = \"mypassword\"\nwarehouse = \"COMPUTE_WH\"\ndatabase = \"MY_DB\"\nschema = \"PUBLIC\"\nrole = \"MY_ROLE\"\n```\n\n**`app.py`** with a Snowflake connection:\n\n```python\nimport streamlit as st\n\nst.set_page_config(page_title=\"Snowflake Dashboard\", layout=\"wide\")\n\n# Connect to Snowflake\nconn = st.connection(\"snowflake\")\n\n# Load data with caching\n@st.cache_data(ttl=600)\ndef load_orders():\n    return conn.query(\"SELECT * FROM orders LIMIT 1000\")\n\ndf = load_orders()\n\n# Display\nst.title(\"Orders Dashboard\")\nst.dataframe(df, width=\"stretch\")\n```\n\n### Key Things the Skill Ensures\n\n1. **`st.connection(\"snowflake\")`** — The modern way to connect. The skill prevents the AI from using older patterns like `snowflake.connector.connect()` directly, which requires more boilerplate code and doesn't integrate with Streamlit's caching or secrets management.\n\n2. **`@st.cache_data(ttl=600)`** — Caches query results for 10 minutes. Without this, every widget interaction would re-run the query.\n\n3. **Parameterized queries** — When user input is involved, the skill teaches the AI to use parameterized queries instead of f-strings, which prevents SQL injection:\n\n```python\n# UNSAFE — f-string opens you to SQL injection:\ndf = conn.query(f\"SELECT * FROM orders WHERE status = '{user_input}'\")\n\n# SAFE — parameterized query the skill teaches:\ndf = conn.query(\n    \"SELECT * FROM orders WHERE status = :status\",\n    params={\"status\": user_input},\n)\n```\n\n4. **Secrets in `.streamlit/secrets.toml`** — Credentials stay out of your code. The skill also creates a `.example` file so you know what to fill in.\n\n### Important: Snowflake Returns Uppercase Column Names\n\nOne gotcha the skill handles: when you query Snowflake, pandas DataFrames come back with UPPERCASE column names. So `SELECT order_id FROM orders` returns a column named `ORDER_ID`, not `order_id`. The skill ensures the AI references columns in uppercase.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\nYou've learned how agent skills transform your AI coding assistant from a general-purpose code generator into a Streamlit specialist. By loading domain-specific knowledge about current APIs, caching patterns, and design best practices, the skills ensure your AI writes Streamlit code that works correctly from the start.\n\n### What You Learned\n- Agent skills are instruction sets that give AI assistants specialized, up-to-date knowledge\n- The `developing-with-streamlit` skill collection contains 17 sub-skills that cover the full Streamlit development workflow\n- Skills activate automatically based on your prompt — you describe what you want and the right skill loads\n- The skills ensure modern patterns: `@st.cache_data`, `st.write_stream`, `width=\"stretch\"`, `st.session_state.setdefault()`\n- Ready-made templates (9 apps, 8 themes) give you polished starting points\n- Snowflake connections use `st.connection(\"snowflake\")` with proper caching and parameterized queries\n\n### Next Steps\n\nNow that you have the skills installed, here are concrete things to try:\n\n1. **Swap in real data** — Take the dashboard you built and replace the sample data with a live Snowflake query using `st.connection(\"snowflake\")`\n2. **Combine apps** — Ask the AI to \"add a chat sidebar to my dashboard\" and watch the routing load both `building-streamlit-chat-ui` and `building-streamlit-dashboards` simultaneously\n3. **Contribute a skill** — The [streamlit/agent-skills](https://github.com/streamlit/agent-skills) repository accepts pull requests. If you find a pattern the skills don't cover, add it\n\n### Related Resources\n\nDocumentation:\n- [streamlit/agent-skills on GitHub](https://github.com/streamlit/agent-skills) — The full skill collection\n- [Agent Skills Specification](https://agentskills.io/specification) — How skills are structured\n- [Streamlit Documentation](https://docs.streamlit.io) — Official Streamlit docs\n- [Streamlit LLM Docs](https://docs.streamlit.io/llms.txt) — Docs in LLM-optimized format\n\nAI Coding Assistants:\n- [Snowflake CoCo](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code) — Snowflake's AI coding CLI (skills pre-installed)\n- [Claude Code](https://docs.anthropic.com/en/docs/claude-code) — Anthropic's terminal coding tool\n- [Cursor](https://cursor.com) — AI-native code editor\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-a8b8ad2ca2","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-846fd4ccba",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-3cf2779e80","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-03-24",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-6ab615acd7","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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