Create visualizations for construction data. Generate charts, graphs, heatmaps, and interactive dashboards using Matplotlib, Seaborn, and Plotly for project analysis and reporting.
SkillScore55Maintained
Automatic
◆Data Analyst pack
Last verified 8/23/2026
What it changes
Passed 12 of 22 test cases with the skill, and 8 of 22 without it.
Without skill36%
With skill55%
Checks it passed that the model alone missed
Extracts top elements using df.nlargest(10, 'Cost').
Utilizes ax.barh to plot horizontal bars ordered by volume.
Uses plotly.express.line to plot phase progress over dates.
and 3 more in the full report
5 fixed7 passed either way9 still failing1 broke
Pass-rate lift
+18 pp
Tokens
+185%
Agent turns
0%
Response time
-9%
Model gemini-3.6-flashMeasured 8/23/2026v112 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 5
Prompt
Write a Python Matplotlib function to create a cost breakdown pie chart for building element categories. Base model defaults usually leave out center summaries, but I want the total cost formatted as currency placed in the center hole.
What was checked, without and with the skill
Checked
Without skill
With skill
Uses ax.pie with autopct='%1.1f%%' to display section labels.
Passed
Passed
Adds center text using ax.text at coordinates (0, 0) displaying summed total cost.
Missed
Passed
case-05 · gemini-3.6-flash · 8/23/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md590 linessingle file
Data Visualization for Construction
Overview
Based on DDC methodology (Chapter 4.1), this skill provides comprehensive data visualization techniques for construction analytics. Visual insights drive better decisions - from cost breakdowns to schedule analysis.
Book Reference: "Аналитика данных и принятие решений" / "Data Analytics and Decision Making"
"Визуализация данных превращает сложные наборы данных в понятные графики, которые могут использоваться для принятия решений на всех уровнях проекта."
— DDC Book, Chapter 4.1
Quick Start
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load construction data
df = pd.read_excel("project_data.xlsx")
# Quick bar chart - volumes by category
fig, ax = plt.subplots(figsize=(10, 6))
df.groupby('Category')['Volume_m3'].sum().plot(kind='bar', ax=ax)
ax.set_title('Volume by Category')
ax.set_ylabel('Volume (m³)')
plt.tight_layout()
plt.savefig('volume_by_category.png', dpi=150)
plt.show()
Matplotlib Fundamentals
Basic Charts for Construction
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np