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.
curl -O https://raw.githubusercontent.com/iusztinpaul/designing-real-world-ai-agents-workshop/main/.agents/skills/developing-with-streamlit/skills/building-streamlit-dashboards/SKILL.mdgit clone --depth 1 https://github.com/iusztinpaul/designing-real-world-ai-agents-workshopWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/iusztinpaul/designing-real-world-ai-agents-workshop/building-streamlit-dashboards)<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/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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)
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.
- 13d ago First seen · 148 lines · 39 tokens per session scan A 7387d7e8ee39
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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