designing-real-world-ai-agents-workshop: Skill for Claude Code

.agents/skills/developing-with-streamlit/skills/building-streamlit-dashboards/SKILL.md

building-streamlit-dashboards is a skill for Claude Code, Codex from iusztinpaul/designing-real-world-ai-agents-workshop. It costs 39 tokens per session (1,071 once invoked), scanned A, original, MIT.

A guide to building data dashboards in Streamlit, a Python tool for making interactive web apps. It covers KPI displays, metric cards, charts, tables, and layouts that adapt to smaller screens.

In plain words
What is it for?
Use it when creating Streamlit dashboards with cards, KPI rows, charts, tables, borders, and responsive layouts.
Why use it?
It helps you arrange business metrics and other data clearly without designing each dashboard section from scratch.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument; installed under .agents/ (shared by several agents).

This is iusztinpaul/designing-real-world-ai-agents-workshop's own configuration. It tells Claude Code and Codex how to work on designing-real-world-ai-agents-workshop itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything designing-real-world-ai-agents-workshop configures →

Reuse

Borrowing it

Nothing to install: this file belongs to iusztinpaul/designing-real-world-ai-agents-workshop. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/iusztinpaul/designing-real-world-ai-agents-workshop/main/.agents/skills/developing-with-streamlit/skills/building-streamlit-dashboards/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/iusztinpaul/designing-real-world-ai-agents-workshop

Made for: Claude Code, Codex.

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<a href="https://agentmods.dev/skills/iusztinpaul/designing-real-world-ai-agents-workshop/building-streamlit-dashboards"><img src="https://agentmods.dev/badge/skills/iusztinpaul/designing-real-world-ai-agents-workshop/building-streamlit-dashboards.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 39 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,071 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00039 $0.01071
Opus 5 $0.00019 $0.00535
Sonnet 5 $0.00008 $0.00214
Haiku 4.5 $0.00004 $0.00107

Measured 13d ago against content hash 7387d7e8ee39, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

building-streamlit-dashboards scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/developing-with-streamlit/skills/building-streamlit-dashboards/SKILL.md · 148 lines

How it starts

The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Streamlit dashboards

Compose metrics, charts, and data into clean dashboard layouts.

Cards with borders

Use border=True to create visual cards. Supported on st.container, st.metric, st.columns, and st.form:

# Container card
with st.container(border=True):
    st.subheader("Sales Overview")
    st.line_chart(sales_data)

# Metric card
st.metric("Revenue", "$1.2M", "+12%", border=True)

# Column cards
for col in st.columns(3, border=True):
    with col:
        st.metric("Users", "1.2k")

Card labels

Add context to cards with headers or bold text:

# With subheader
with st.container(border=True):
    st.subheader("Monthly Trends")
    st.line_chart(data)

# With bold label
with st.container(border=True):
    st.markdown("**Top Products**")
    st.dataframe(top_products)

KPI rows

Use horizontal containers for responsive metric rows:

with st.container(horizontal=True):
    st.metric("Revenue", "$1.2M", "-7%", border=True)
    st.metric("Users", "762k", "+12%", border=True)
    st.metric("Orders", "1.4k", "+5%", border=True)

Horizontal containers wrap on smaller screens. Prefer them over st.columns for metric rows.

Metrics with sparklines

Add trend context with chart_data:

weekly_values = [700, 720, 715, 740, 762, 755, 780]

st.metric(
    "Active Users",
    "780k",
    "+3.2%",
    border=True,
    chart_data=weekly_values,
    chart_type="line",  # or "bar"
)

Sparklines show y-values only—use for evenly-spaced data like daily/weekly snapshots.

Dashboard layout

Combine cards into a dashboard:

# KPI row
with st.container(horizontal=True):
    st.metric("Revenue", "$1.2M", "-7%", border=True, chart_data=rev_trend, chart_type="line")
    st.metric("Users", "762k", "+12%", border=True, chart_data=user_trend, chart_type="line")
    st.metric("Orders", "1.4k", "+5%", border=True, chart_data=order_trend, chart_type="bar")

# Charts row
col1, col2 = st.columns(2)
with col1:
    with st.container(border=True):
        st.subheader("Revenue by Region")
        st.bar_chart(region_data, x="region", y="revenue")

with col2:
    with st.container(border=True):
        st.subheader("Monthly Trend")
        st.line_chart(monthly_data, x="month", y="value")

# Data table
with st.container(border=True):
    st.subheader("Recent Orders")
    st.dataframe(orders_df, hide_index=True)

Read the full file on GitHub · 148 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 13d ago First seen · 148 lines · 39 tokens per session scan A 7387d7e8ee39

Subscribe to this mod's changes

building-streamlit-dashboards is a skill published in the GitHub repository iusztinpaul/designing-real-world-ai-agents-workshop (505 stars, last pushed 3mo ago), licensed MIT. It adds 39 tokens to every session and 1,071 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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