engineering-report-generator
Generate engineering analysis reports with interactive Plotly visualizations, standard report sections, and HTML export. Use for creating dashboards, analysis summaries, and technical documentation with charts.
Best use case
engineering-report-generator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate engineering analysis reports with interactive Plotly visualizations, standard report sections, and HTML export. Use for creating dashboards, analysis summaries, and technical documentation with charts.
Teams using engineering-report-generator 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/engineering-report-generator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How engineering-report-generator Compares
| Feature / Agent | engineering-report-generator | 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?
Generate engineering analysis reports with interactive Plotly visualizations, standard report sections, and HTML export. Use for creating dashboards, analysis summaries, and technical documentation with charts.
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
# Engineering Report Generator
## Quick Start
```python
import plotly.express as px
import pandas as pd
from pathlib import Path
from datetime import datetime
# Load data
df = pd.read_csv("../data/processed/results.csv")
# Create visualization
fig = px.line(df, x="date", y="value", title="Analysis Results")
# Generate HTML report
html = f"""<!DOCTYPE html>
<html>
<head><title>Engineering Report</title></head>
<body>
<h1>Analysis Report - {datetime.now().strftime('%Y-%m-%d')}</h1>
{fig.to_html(full_html=False, include_plotlyjs="cdn")}
</body>
</html>"""
Path("../reports/analysis.html").write_text(html)
print("Report generated: reports/analysis.html")
```
## When to Use
- Creating analysis reports with charts and visualizations
- Building interactive dashboards from CSV/data sources
- Generating technical documentation with plots
- Producing client-deliverable HTML reports
- Summarizing engineering calculations with graphics
## Report Structure
### Standard Sections
1. **Header** - Title, date, project info, version
2. **Executive Summary** - Key findings and metrics at a glance
3. **Methodology** - Analysis approach and assumptions
4. **Results** - Data tables and interactive visualizations
5. **Discussion** - Interpretation of results
6. **Conclusions** - Summary and recommendations
7. **Appendix** - Supporting data, references
## Implementation Pattern
### Basic Report Generation
```python
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
from pathlib import Path
from datetime import datetime
def generate_report(
data_path: str,
*See sub-skills for full details.*
### Visualization Patterns
```python
def create_visualizations(df: pd.DataFrame, chart_configs: list) -> list:
"""Create Plotly figures from configuration."""
figures = []
for config in chart_configs:
chart_type = config.get('type', 'line')
if chart_type == 'line':
fig = px.line(
*See sub-skills for full details.*
### HTML Template
```python
def build_html_report(title: str, sections: dict, figures: list) -> str:
"""Build complete HTML report."""
# Convert figures to HTML
chart_html = '\n'.join([
f'<div class="chart-container">{fig.to_html(full_html=False, include_plotlyjs="cdn")}</div>'
for fig in figures
])
*See sub-skills for full details.*
## Integration
### With YAML Workflow
```yaml
task: generate_report
input:
data_path: data/processed/results.csv
output:
report_path: reports/analysis.html
config:
title: "Analysis Report"
charts:
- type: line
x: time
y: value
```
### With Data Pipeline
```python
# Pipeline output -> Report input
pipeline_results = process_data(raw_data)
pipeline_results.to_csv('data/processed/results.csv')
generate_report(
data_path='data/processed/results.csv',
output_path='reports/analysis.html',
title='Pipeline Results'
)
```
## Related Skills
- [xlsx](../../document-handling/xlsx/SKILL.md) - Excel data handling
- [pdf](../../document-handling/pdf/SKILL.md) - PDF report generation
- [data-pipeline-processor](../data-pipeline-processor/SKILL.md) - Data preparation
- [yaml-workflow-executor](../yaml-workflow-executor/SKILL.md) - Workflow automation
---
## Version History
- **1.1.0** (2026-01-02): Upgraded to SKILL_TEMPLATE_v2 format with Quick Start, Error Handling, Metrics, Execution Checklist, additional examples
- **1.0.0** (2024-10-15): Initial release with Plotly visualizations, HTML templates, responsive design
## Sub-Skills
- [Example 1: Production Analysis Report (+2)](example-1-production-analysis-report/SKILL.md)
- [Do (+4)](do/SKILL.md)
## Sub-Skills
- [Error Handling](error-handling/SKILL.md)
- [Execution Checklist](execution-checklist/SKILL.md)
- [Metrics](metrics/SKILL.md)Related Skills
interactive-report-generator
Generate interactive HTML reports with Plotly visualizations from data analysis results. Supports dashboards, charts, and professional styling.
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.
orcaflex-reporting-fixture-proof-pattern
Build and extend fixture-backed OrcaFlex reporting proof paths in digitalmodel using stable metadata baselines, normalized HTML snapshots, and reusable reporting test helpers.
blocker-reporting-outcome-validation
Pattern for closing issues where the deliverable is documented blockers rather than feature completion
engineering-solver-domain-recon
Deep reconnaissance of an engineering solver domain (OrcaWave, OrcaFlex, CalculiX, OpenFOAM, etc.) across a multi-repo ecosystem — map infrastructure, issues, skills, data artifacts, machine constraints, and solver queue state before planning work.
orcaflex-model-generator
Generate OrcaFlex modular models from spec.yml using builder registry pattern with conditional generation and cross-builder context sharing.
engineering-domain-reconnaissance
Class-level external engineering domain reconnaissance: field development, external drive ingest planning, and source-to-artifact conversion.
cad-engineering
Expert CAD Engineering Specialist with comprehensive knowledge of CAD systems, file formats, and conversion technologies. Use for CAD software guidance, file format conversions, technical drawings, 3D modeling, PDF to CAD conversions, and interoperability between open-source and proprietary CAD systems.
reporting-workflow
End-to-end workflow for generating reports from data, validating outputs, running report-focused tests, and iterating on HTML/interactive reporting artifacts.
pytest-fixture-generator
Generate standardized pytest configuration with fixtures, markers, and coverage settings. Creates conftest.py and pytest.ini for workspace-hub compliant testing.
semantic-taxonomy-reporting-consistency
Keep semantic-diff taxonomy summaries consistent with evidence tables when adding richer categories to legacy comparison/reporting pipelines.
calculation-report
Generate professional engineering calculation reports from YAML — renders LaTeX formulas, interactive charts, and pass/fail outputs as HTML using the warm-parchment design system