Streamlit Agent Skills Developing With Streamlit

Developing with Streamlit

This is a routing skill that directs you to specialized sub-skills for Streamlit development.

When to Use

Invoke this skill when the user's request involves:

• Creating a new Streamlit app
• Editing or modifying an existing Streamlit app
• Debugging Streamlit issues (errors, session state bugs, performance problems)
• Beautifying or improving the visual design of a Streamlit app
• Optimizing Streamlit performance (caching, fragments, reruns)
• Deploying Streamlit apps (locally or to Snowflake)
• Styling widgets (button colors, backgrounds, CSS customization)
• Any question about Streamlit widgets, layouts, or components

Trigger phrases: "streamlit", "st.", "dashboard", "app.py", "beautify app", "make it look better", "style", "CSS", "color", "background", "theme", "button", "slow rerun", "session state", "performance", "faster", "cache", "deploy"

Workflow

Step 1: Locate the Streamlit source code
    ↓
Step 2: Identify task type and load appropriate sub-skill(s)
    ↓
Step 3: Apply guidance from sub-skill to edit code
    ↓
Step 4: Check if app is running and offer to run it

Step 1: Locate the Streamlit Source Code (if needed)

Goal: Identify the app file(s) to edit. Skip this step if already clear from context.

When to skip:

• User mentioned a specific file path (e.g., "edit src/app.py")
• User has file(s) already in conversation context
• Working directory has an obvious single entry point (app.py, streamlit_app.py)

When to search:

• User says "my streamlit app" without specifying which file
• Multiple Python files exist and it's unclear which is the entry point

If searching is needed:

1. Quick scan for Streamlit files:

   find . -name "*.py" -type f | xargs grep -l "import streamlit\|from streamlit" 2>/dev/null | head -10
   

2. Apply entry point heuristics (in priority order):

   • streamlit_app.py at root → this is the entry point (canonical name)
   • app.py at root → likely entry point
   • File using st.navigation → entry point for multi-page apps
   • Single .py file at root with streamlit import → entry point
   • Files in pages/ or app_pages/ subdirectory → NOT entry points (these are sub-pages)

3. If entry point is obvious → use it, no confirmation needed

   Example: Found streamlit_app.py and pages/metrics.py → use streamlit_app.py

4. Only ask if genuinely ambiguous (e.g., multiple root-level candidates, none named streamlit_app.py):

   Found multiple potential entry points:
   - dashboard.py
   - main.py
   
   Which is your main app?
   

Output: Path to the main Streamlit source file(s)

Step 2: Identify Task Type and Route to Sub-Skill

Goal: Determine what the user needs and load the appropriate guidance.

Use this routing table to select sub-skill(s). Always read the sub-skill file before making changes:

Fallback — "this widget doesn't exist in Streamlit":

If the user asks for a UI element or interaction that has never been part of Streamlit's API and cannot be built with any combination of native widgets (e.g., drag-and-drop, canvas drawing, custom interactive visualizations), route to the CCv2 sub-skill (skills/building-streamlit-custom-components-v2/SKILL.md). Do not route to CCv2 for features that exist in newer Streamlit versions (e.g., st.connection, st.segmented_control) — suggest upgrading instead.

Common combinations:

For beautifying/improving an app, read in order:

1. skills/improving-streamlit-design/SKILL.md
2. skills/using-streamlit-layouts/SKILL.md
3. skills/choosing-streamlit-selection-widgets/SKILL.md

For building a dashboard, read:

1. skills/building-streamlit-dashboards/SKILL.md
2. skills/displaying-streamlit-data/SKILL.md

IMPORTANT - Use templates:

When creating a new dashboard app, prefer starting from a template in templates/apps/:

• If a template closely matches the request, copy it and adapt:
  • dashboard-metrics / dashboard-metrics-snowflake — KPI cards with time-series charts
  • dashboard-companies — company/entity comparison
  • dashboard-compute / dashboard-compute-snowflake — resource/credit monitoring
  • dashboard-feature-usage — feature adoption tracking
  • dashboard-seattle-weather — public dataset exploration (local only)
  • dashboard-stock-peers / dashboard-stock-peers-snowflake — financial peer analysis
• If no template is a close match, start from scratch but borrow relevant patterns from the templates (e.g., caching with @st.cache_data, filter_by_time_range(), st.set_page_config(), chart utilities, layout structure)
• See templates/apps/README.md for template descriptions

When editing an existing app, use templates as reference for best practices:

• Check templates/apps/ for caching patterns, layout structure, and Snowflake integration
• Apply consistent patterns from templates to improve the existing code

When applying a custom theme, use a template from templates/themes/:

• Copy a theme directory (snowflake, dracula, nord, stripe, solarized-light, spotify, github, minimal)
• Themes use Google Fonts for easy setup
• See templates/themes/README.md for theme previews

For performance optimization, read:

1. skills/optimizing-streamlit-performance/SKILL.md

Step 3: Apply Guidance to Edit Code

Goal: Make changes to the Streamlit app following sub-skill best practices.

Actions:

1. Apply the patterns and recommendations from the loaded sub-skill(s)
2. Make edits to the source file(s) identified in Step 1
3. Preserve existing functionality while adding improvements

Step 4: Check Running Apps and Offer to Run

Goal: Help the user see their changes by checking if their app is running.

Actions:

1. Check for running Streamlit apps on ports 850*:

   lsof -nP -iTCP -sTCP:LISTEN 2>/dev/null | grep -i python | awk '{print $2, $9}' | grep ':85' || echo "No Streamlit apps detected on ports 850*"
   

2. Present findings to user:

   If app is running:

   Found Streamlit app running:
   - PID: [pid] at http://localhost:[port]
   
   Your changes should be visible after a page refresh (Streamlit hot-reloads on file save).
   

   If no app is running:

   No Streamlit app detected on ports 850*.
   
   Would you like me to run the app? I can start it with:
     streamlit run [app_file.py]
   

3. If user wants to run the app, start it:

   streamlit run [path/to/app.py] --server.port 8501
   

Stopping Points

• Step 2: If multiple sub-skills seem relevant, ask user which aspect to focus on first
• Step 4: Ask before starting the Streamlit app

Skill map

Resources

• Streamlit API Reference
• Streamlit Gallery

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