engineering-report-generator-ex1-production-report
Sub-skill of engineering-report-generator: Example 1: Production Analysis Report (+2).
Best use case
engineering-report-generator-ex1-production-report is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of engineering-report-generator: Example 1: Production Analysis Report (+2).
Teams using engineering-report-generator-ex1-production-report 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/example-1-production-analysis-report/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How engineering-report-generator-ex1-production-report Compares
| Feature / Agent | engineering-report-generator-ex1-production-report | 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 engineering-report-generator: Example 1: Production Analysis Report (+2).
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.
Related Guides
SKILL.md Source
# Example 1: Production Analysis Report (+2)
## Example 1: Production Analysis Report
```python
# Configuration
report_config = {
'title': 'Monthly Production Analysis',
'project': 'Field A Development',
'summary': '''
<div class="summary-grid">
<div class="metric-card">
<div class="metric-value">125,000</div>
<div class="metric-label">Total Oil (bbl)</div>
</div>
<div class="metric-card">
<div class="metric-value">98.5%</div>
<div class="metric-label">Uptime</div>
</div>
</div>
''',
'charts': [
{'type': 'line', 'x': 'date', 'y': 'production', 'title': 'Daily Production'},
{'type': 'bar', 'x': 'well', 'y': 'cumulative', 'title': 'Well Performance'}
]
}
# Generate
generate_report(
data_path='../data/processed/production.csv',
output_path='../reports/production_report.html',
**report_config
)
```
## Example 2: Structural Analysis Report
```python
report_config = {
'title': 'Structural Analysis Results',
'methodology': '<p>Analysis performed per DNV-RP-C201 using finite element method.</p>',
'charts': [
{'type': 'heatmap', 'x': 'x_coord', 'y': 'y_coord', 'values': 'stress', 'title': 'Stress Distribution'},
{'type': 'scatter', 'x': 'load', 'y': 'displacement', 'title': 'Load-Displacement Curve'}
],
'conclusions': '<p>All structural elements satisfy design criteria with safety factor > 1.5</p>'
}
```
## Example 3: Multi-Panel Dashboard
```python
from plotly.subplots import make_subplots
def create_dashboard(df: pd.DataFrame, output_path: str):
"""Create multi-panel analysis dashboard."""
fig = make_subplots(
rows=2, cols=2,
subplot_titles=('Trend', 'Distribution', 'Comparison', 'Correlation')
)
# Add traces to each panel
fig.add_trace(go.Scatter(x=df['date'], y=df['value'], mode='lines'), row=1, col=1)
fig.add_trace(go.Histogram(x=df['value']), row=1, col=2)
fig.add_trace(go.Bar(x=df['category'], y=df['count']), row=2, col=1)
fig.add_trace(go.Scatter(x=df['x'], y=df['y'], mode='markers'), row=2, col=2)
fig.update_layout(height=800, title_text="Analysis Dashboard")
fig.write_html(output_path)
return output_path
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