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Agent skill

Displaying Streamlit Data

by iusztinpaul in iusztinpaul/designing-real-world-ai-agents-workshop

Displaying charts, dataframes, and metrics in Streamlit. An agent skill from iusztinpaul/designing-real-world-ai-agents-workshop.

Apache-2.0Auto-check passedData & Analytics

Install Displaying Streamlit Data

skills CLI
$ npx skills add iusztinpaul/designing-real-world-ai-agents-workshop --skill displaying-streamlit-data -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install iusztinpaul/designing-real-world-ai-agents-workshop displaying-streamlit-data --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/iusztinpaul/designing-real-world-ai-agents-workshop.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/developing-with-streamlit/skills/displaying-streamlit-data .claude/skills/displaying-streamlit-data && rm -rf skills-src

Use ~/.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/

Facts

Skill name
displaying-streamlit-data
GitHub stars
513
Token cost
~1.6k tokens
SKILL.md length
403 words
Files
1
Skills in repo
23
Repo updated
First seen
Licence
Apache-2.0

At a glance

Displaying charts, dataframes, and metrics in Streamlit. An agent skill from iusztinpaul/designing-real-world-ai-agents-workshop.

  • Visualizing data
  • SKILL.md covers Choosing display elements, Native charts first, Human-readable labels and Altair for complex charts, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Configuring dataframe columns

What it does

Displaying Streamlit Data is an agent skill from iusztinpaul/designing-real-world-ai-agents-workshop. Displaying charts, dataframes, and metrics in Streamlit. Use when visualizing data, configuring dataframe columns, or adding sparklines to metrics. Covers native charts, Altair, and column configuration.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Data & Analytics, covering DataFrames. It works with Streamlit. The repository describes itself as: Hands-on workshop: Build a multi-agent AI system from scratch — Deep Research Agent + Writing Workflow served as MCP servers. Includes code, slides, and video. The licence is Apache-2.0.

When your agent uses it

  • Visualizing data
  • Configuring dataframe columns
  • Adding sparklines to metrics

Example prompts

  • “/displaying-streamlit-data”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit ea4f6e9. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • docs.streamlit.io

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Displaying Streamlit Data loads about 1.6k tokens when it runs. Until then it costs about 57 tokens; SKILL.md has 403 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~57
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

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.

Safety

Auto-check passed

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from iusztinpaul/designing-real-world-ai-agents-workshop at commit ea4f6e9, republished under its Apache-2.0 licence (© iusztinpaul). 403 words, ~1,630 tokens.

Download SKILL.mdSave it as .claude/skills/displaying-streamlit-data/SKILL.md (or your agent's skills folder).
name
displaying-streamlit-data
description
Displaying charts, dataframes, and metrics in Streamlit. Use when visualizing data, configuring dataframe columns, or adding sparklines to metrics. Covers native charts, Altair, and column configuration.
license
Apache-2.0

Streamlit charts & data

Present data clearly.

Choosing display elements

ElementUse Case
st.dataframeInteractive exploration, sorting, filtering
st.data_editorUser-editable tables
st.tableStatic display, no interaction needed
st.metricKPIs with delta indicators
st.jsonStructured data inspection

Native charts first

Prefer Streamlit's native charts for simple cases.

python
st.line_chart(df, x="date", y="revenue")
st.bar_chart(df, x="category", y="count")
st.scatter_chart(df, x="age", y="salary")
st.area_chart(df, x="date", y="value")

Native charts support additional parameters: color for series grouping, stack for bar/area stacking, size for scatter point sizing, horizontal for horizontal bars. See the chart API reference for full options.

Human-readable labels

Use clear labels—not column names or abbreviations. Skip x_label/y_label if the column names are already readable.

python
# BAD: cryptic column names without labels
st.line_chart(df, x="dt", y="rev")

# GOOD: readable columns, no labels needed
st.line_chart(df, x="date", y="revenue")

# GOOD: cryptic columns, add labels
st.line_chart(df, x="dt", y="rev", x_label="Date", y_label="Revenue")

Altair for complex charts

Use Altair when you need more control. Altair is bundled with Streamlit (no extra install), while Plotly requires an additional package. Pick one and stay consistent throughout your app.

python
import altair as alt

chart = alt.Chart(df).mark_line().encode(
    x=alt.X("date:T", title="Date"),
    y=alt.Y("revenue:Q", title="Revenue ($)"),
    color="region:N"
)
st.altair_chart(chart)

When to use Altair:

  • Custom axis formatting
  • Multiple series with legends
  • Interactive tooltips
  • Layered visualizations

Dataframe column configuration

Use column_config where it adds value—formatting currencies, showing progress bars, displaying links or images. Don't add config just for labels or tooltips that don't meaningfully improve readability. Works with both st.dataframe and st.data_editor.

python
st.dataframe(
    df,
    column_config={
        "revenue": st.column_config.NumberColumn(
            "Revenue",
            format="$%.2f"
        ),
        "completion": st.column_config.ProgressColumn(
            "Progress",
            min_value=0,
            max_value=100
        ),
        "url": st.column_config.LinkColumn("Website"),
        "logo": st.column_config.ImageColumn("Logo"),
        "created_at": st.column_config.DatetimeColumn(
            "Created",
            format="MMM DD, YYYY"
        ),
        "internal_id": None,  # Hide non-essential columns
    },
    hide_index=True,
)

Note on hiding columns: Setting a column to None hides it from the UI, but the data is still sent to the frontend. For truly sensitive data, pre-filter the DataFrame before displaying.

Dataframe best practices:

  • Hide useless index: hide_index=True
  • Or make index meaningful: df = df.set_index("customer_name") before displaying
  • Hide internal/technical columns: Set column to None in config (but pre-filter for sensitive data)
  • Use visual column types where they help: sparklines for trends, progress bars for completion, images for logos
Show full SKILL.md (148 more words)Show less

Column types:

  • AreaChartColumn → Area sparklines
  • BarChartColumn → Bar sparklines
  • CheckboxColumn → Boolean as checkbox
  • DateColumn → Date only (no time)
  • DatetimeColumn → Dates with formatting
  • ImageColumn → Images
  • JSONColumn → Display JSON objects
  • LineChartColumn → Sparkline charts
  • LinkColumn → Clickable links
  • ListColumn → Display lists/arrays
  • MultiselectColumn → Multi-value selection
  • NumberColumn → Numbers with formatting
  • ProgressColumn → Progress bars
  • SelectboxColumn → Editable dropdown
  • TextColumn → Text with formatting
  • TimeColumn → Time only (no date)

Pinned columns

Keep important columns visible while scrolling horizontally:

python
st.dataframe(
    df,
    column_config={
        "Title": st.column_config.TextColumn(pinned=True),  # Always visible
        "Rating": st.column_config.ProgressColumn(min_value=0, max_value=10),
    },
    hide_index=True,
)

Data editor

Use st.data_editor when users need to edit data directly:

python
edited_df = st.data_editor(
    df,
    num_rows="dynamic",  # Allow adding/deleting rows
    column_config={
        "status": st.column_config.SelectboxColumn(
            "Status",
            options=["pending", "approved", "rejected"]
        ),
    },
)

# React to edits
if not edited_df.equals(df):
    save_changes(edited_df)

JSON display

For structured data inspection. Accepts dicts, lists, or any JSON-serializable object:

python
st.json({"name": "John", "scores": [95, 87, 92]})

Sparklines in metrics

Add chart_data and chart_type to metrics for visual context.

python
values = [700, 720, 715, 740, 762, 755, 780]

st.metric(
    label="Developers",
    value="762k",
    delta="-7.42% (MoM)",
    delta_color="inverse",
    chart_data=values,
    chart_type="line"  # or "bar"
)

Note: Sparklines only show y-values and ignore x-axis spacing. Use them for evenly-spaced data (like daily or weekly snapshots). For irregularly-spaced time series, use a proper chart instead.

See building-streamlit-dashboards for composing metrics into dashboard layouts.

References

© iusztinpaul, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/developing-with-streamlit/skills/displaying-streamlit-data of iusztinpaul/designing-real-world-ai-agents-workshop.

Open the folder on GitHubat commit ea4f6e9

Compare with similar skills

Displaying Streamlit Data 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.

Displaying Streamlit Data compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Displaying Streamlit Data this skilliusztinpaul/designing-real-world-ai-agents-workshop513—~1.6kAutomated safety check: PassApache-2.0
Paper FiguresEvoScientist/EvoSkills4751 repos~4.4kAutomated safety check: PassApache-2.0
Chdb Datastorevemetric/vemetric3942 repos~1.4kAutomated safety check: PassApache-2.0
Polar Python SDKpolarsource/polar10k—~1.8kAutomated safety check: PassApache-2.0
Polar Typescript SDKpolarsource/polar10k—~2kAutomated safety check: PassApache-2.0
CSV Data Summarizercoffeefuelbump/csv-data-summarizer-claude-skill4682 repos~1.4kAutomated safety check: PassNone

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Works with

Categories

Questions about Displaying Streamlit Data

What does Displaying Streamlit Data do?

Displaying charts, dataframes, and metrics in Streamlit. An agent skill from iusztinpaul/designing-real-world-ai-agents-workshop. Displaying Streamlit Data is an agent skill from iusztinpaul/designing-real-world-ai-agents-workshop. Displaying charts, dataframes, and metrics in Streamlit.

When should I use Displaying Streamlit Data?

Displaying Streamlit Data fits situations like: visualizing data; configuring dataframe columns; adding sparklines to metrics.

How do I install Displaying Streamlit Data in Claude Code?

Run `npx skills add iusztinpaul/designing-real-world-ai-agents-workshop --skill displaying-streamlit-data -a claude-code`. Or copy the skill folder (.agents/skills/developing-with-streamlit/skills/displaying-streamlit-data in iusztinpaul/designing-real-world-ai-agents-workshop) into .claude/skills/displaying-streamlit-data in your project. Claude Code loads it when a task matches its description.

How do I install Displaying Streamlit Data in Codex?

Run `npx skills add iusztinpaul/designing-real-world-ai-agents-workshop --skill displaying-streamlit-data -a codex`. Or copy the skill folder (.agents/skills/developing-with-streamlit/skills/displaying-streamlit-data in iusztinpaul/designing-real-world-ai-agents-workshop) into .agents/skills/displaying-streamlit-data in your project. Codex loads it when a task matches its description.

Can I use Displaying Streamlit Data in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add iusztinpaul/designing-real-world-ai-agents-workshop --skill displaying-streamlit-data -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/displaying-streamlit-data, .gemini/skills/displaying-streamlit-data, .github/skills/displaying-streamlit-data and .opencode/skills/displaying-streamlit-data in your project.

What does Displaying Streamlit Data need to run?

SKILL.md names no scripts, command-line tools or credentials: Displaying Streamlit Data is instructions for the agent only. Our summary lists: Python 3.

Does Displaying Streamlit Data access the network?

SKILL.md names 1 domain. As links in the text: docs.streamlit.io. This is read from the text; nothing was executed.

Is Displaying Streamlit Data safe to install?

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. Review the folder before installing.

What licence does Displaying Streamlit Data use?

Displaying Streamlit Data is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Displaying Streamlit Data use?

About 1.6k tokens (SKILL.md is roughly 6.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Displaying Streamlit Data?

Skills that share tags, products or a category with Displaying Streamlit Data: Paper Figures (EvoScientist/EvoSkills, 475 stars), Chdb Datastore (vemetric/vemetric, 394 stars), Polar Python SDK (polarsource/polar, 10k stars) and Polar Typescript SDK (polarsource/polar, 10k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Displaying Streamlit Data?

iusztinpaul (a GitHub user) maintains it in iusztinpaul/designing-real-world-ai-agents-workshop, which has 513 GitHub stars. The repository holds 23 skills in this directory. The repository was last updated on June 3, 2026.

Source: iusztinpaul/designing-real-world-ai-agents-workshop on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.