python-pptx-data-driven-presentation-from-database
Sub-skill of python-pptx: Data-Driven Presentation from Database (+1).
Best use case
python-pptx-data-driven-presentation-from-database is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of python-pptx: Data-Driven Presentation from Database (+1).
Teams using python-pptx-data-driven-presentation-from-database should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/data-driven-presentation-from-database/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How python-pptx-data-driven-presentation-from-database Compares
| Feature / Agent | python-pptx-data-driven-presentation-from-database | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of python-pptx: Data-Driven Presentation from Database (+1).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Data-Driven Presentation from Database (+1)
## Data-Driven Presentation from Database
```python
"""
Generate presentations from database queries.
"""
from pptx import Presentation
from pptx.util import Inches, Pt
from pptx.chart.data import CategoryChartData
from pptx.enum.chart import XL_CHART_TYPE
import sqlite3
from datetime import datetime
def generate_database_presentation(
db_path: str,
output_path: str
) -> None:
"""Generate presentation from database data."""
conn = sqlite3.connect(db_path)
prs = Presentation()
# Title slide
slide = prs.slides.add_slide(prs.slide_layouts[0])
slide.shapes.title.text = "Sales Analysis Report"
slide.placeholders[1].text = f"Generated: {datetime.now().strftime('%Y-%m-%d')}"
# Query 1: Sales by region
cursor = conn.execute("""
SELECT region, SUM(amount) as total
FROM sales
GROUP BY region
ORDER BY total DESC
""")
regions = cursor.fetchall()
# Create chart slide
slide = prs.slides.add_slide(prs.slide_layouts[5])
slide.shapes.title.text = "Sales by Region"
chart_data = CategoryChartData()
chart_data.categories = [r[0] for r in regions]
chart_data.add_series('Sales', [r[1] for r in regions])
slide.shapes.add_chart(
XL_CHART_TYPE.BAR_CLUSTERED,
Inches(1), Inches(1.5), Inches(11), Inches(5.5),
chart_data
)
# Query 2: Monthly trend
cursor = conn.execute("""
SELECT strftime('%Y-%m', date) as month, SUM(amount)
FROM sales
GROUP BY month
ORDER BY month
""")
monthly = cursor.fetchall()
slide = prs.slides.add_slide(prs.slide_layouts[5])
slide.shapes.title.text = "Monthly Sales Trend"
chart_data = CategoryChartData()
chart_data.categories = [m[0] for m in monthly]
chart_data.add_series('Sales', [m[1] for m in monthly])
slide.shapes.add_chart(
XL_CHART_TYPE.LINE_MARKERS,
Inches(1), Inches(1.5), Inches(11), Inches(5.5),
chart_data
)
conn.close()
prs.save(output_path)
print(f"Database presentation saved: {output_path}")
```
## Pandas DataFrame to Presentation
```python
"""
Generate presentations from pandas DataFrames.
"""
import pandas as pd
from pptx import Presentation
from pptx.util import Inches, Pt
from pptx.dml.color import RgbColor
from pptx.enum.text import PP_ALIGN, MSO_ANCHOR
def dataframe_to_slide(
prs: Presentation,
df: pd.DataFrame,
title: str,
max_rows: int = 15
) -> None:
"""Add DataFrame as table to presentation."""
slide = prs.slides.add_slide(prs.slide_layouts[5])
slide.shapes.title.text = title
# Limit rows if necessary
display_df = df.head(max_rows)
rows, cols = display_df.shape
rows += 1 # For header
# Calculate dimensions
table_width = min(cols * 1.5, 12)
left = Inches((13.333 - table_width) / 2)
table = slide.shapes.add_table(
rows, cols,
left, Inches(1.5),
Inches(table_width), Inches(0.4 * rows)
).table
# Headers
for i, col_name in enumerate(display_df.columns):
cell = table.cell(0, i)
cell.text = str(col_name)
cell.fill.solid()
cell.fill.fore_color.rgb = RgbColor(0x2F, 0x54, 0x96)
para = cell.text_frame.paragraphs[0]
para.font.bold = True
para.font.color.rgb = RgbColor(255, 255, 255)
para.font.size = Pt(10)
para.alignment = PP_ALIGN.CENTER
# Data
for row_idx, (_, row) in enumerate(display_df.iterrows(), start=1):
for col_idx, value in enumerate(row):
cell = table.cell(row_idx, col_idx)
cell.text = str(value)
para = cell.text_frame.paragraphs[0]
para.font.size = Pt(9)
para.alignment = PP_ALIGN.CENTER
def create_dataframe_presentation(
dataframes: dict,
output_path: str
) -> None:
"""Create presentation from multiple DataFrames."""
prs = Presentation()
# Title slide
slide = prs.slides.add_slide(prs.slide_layouts[0])
slide.shapes.title.text = "Data Analysis Report"
for title, df in dataframes.items():
dataframe_to_slide(prs, df, title)
prs.save(output_path)
print(f"DataFrame presentation saved: {output_path}")
```Related Skills
data-validation-reporter
Generate interactive validation reports with quality scoring, missing data analysis, and type checking. Combines Pandas validation, Plotly visualization, and YAML configuration for comprehensive data quality reporting.
worldenergydata-source-readiness
Route agents to the canonical worldenergydata source-readiness skill and summary script. Use when asked for worldenergydata data completeness, data locations, latest known data dates, scheduler freshness, source-readiness status, or acceptance-criteria inputs across the repo ecosystem.
sodir-data-extractor
Extract and process Norwegian Petroleum Directorate field and production data from SODIR
metocean-data-fetcher
Fetch real-time and historical metocean data from NDBC, CO-OPS, Open-Meteo, ERDDAP, and MET Norway. Use for buoy data retrieval, tidal observations, marine forecasts, and multi-source data fusion.
energy-data-visualizer
Interactive visualization for oil and gas production data analysis using Plotly dashboards
bsee-data-extractor
Extract and process BSEE (Bureau of Safety and Environmental Enforcement) data including production, WAR (Well Activity Reports), and APD (Application for Permit to Drill) data. Use for querying production data, well activities, drilling permits, completions, and workovers by API number, block, lease, or field with automatic data normalization and caching.
test-driven-hook-debugging
Debugging and fixing shell hooks by writing isolated test suites first, then using test failures to pinpoint logic bugs
tax-return-data-capture-and-archival
Capture structured tax return summaries as YAML for year-over-year comparison, with fallback to manual PDF download and relocation when automation fails
repo-separation-for-sensitive-data
Architecture pattern for splitting confidential data and reusable algorithms across repos
python-import-path-mismatch-debugging
Diagnose and fix ModuleNotFoundError when a package is installed but imports still fail due to environment/path mismatches
python-import-path-debugging
Diagnose ModuleNotFoundError when a package is installed but still fails to import
metadata-only-wiki-sweep-workflow
Disciplined inventory process for cataloging documents by filename/path without content claims, using parent-centric grouping to prevent stub proliferation