bsee-sodir-extraction-3-combined-analysis
Sub-skill of bsee-sodir-extraction: 3. Combined Analysis.
Best use case
bsee-sodir-extraction-3-combined-analysis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of bsee-sodir-extraction: 3. Combined Analysis.
Teams using bsee-sodir-extraction-3-combined-analysis 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/3-combined-analysis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How bsee-sodir-extraction-3-combined-analysis Compares
| Feature / Agent | bsee-sodir-extraction-3-combined-analysis | 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 bsee-sodir-extraction: 3. Combined Analysis.
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
# 3. Combined Analysis
## 3. Combined Analysis
**Cross-Basin Comparison:**
```python
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from pathlib import Path
def compare_gom_norway_production(
gom_data: pd.DataFrame,
norway_data: pd.DataFrame,
output_dir: Path = Path("reports")
) -> None:
"""
Create comparative analysis of GOM vs Norway production.
Args:
gom_data: BSEE production data
norway_data: SODIR production data
output_dir: Report output directory
"""
output_dir.mkdir(parents=True, exist_ok=True)
# Aggregate by year
gom_annual = gom_data.groupby("PRODUCTION_YEAR").agg({
"OIL_BBL": "sum",
"GAS_MCF": "sum"
}).reset_index()
gom_annual["REGION"] = "Gulf of Mexico"
gom_annual["OIL_MM_BBL"] = gom_annual["OIL_BBL"] / 1e6
gom_annual["GAS_BCF"] = gom_annual["GAS_MCF"] / 1e6
norway_annual = norway_data.groupby("year").agg({
"oilProduction": "sum",
"gasProduction": "sum"
}).reset_index()
norway_annual.columns = ["PRODUCTION_YEAR", "OIL_MM_BBL", "GAS_BCF"]
norway_annual["REGION"] = "Norway"
# Create comparison chart
fig = make_subplots(
rows=1, cols=2,
subplot_titles=["Oil Production (MM BBL)", "Gas Production (BCF)"]
)
# Oil production
fig.add_trace(
go.Bar(
x=gom_annual["PRODUCTION_YEAR"],
y=gom_annual["OIL_MM_BBL"],
name="GOM Oil",
marker_color="blue"
),
row=1, col=1
)
fig.add_trace(
go.Bar(
x=norway_annual["PRODUCTION_YEAR"],
y=norway_annual["OIL_MM_BBL"],
name="Norway Oil",
marker_color="red"
),
row=1, col=1
)
# Gas production
fig.add_trace(
go.Bar(
x=gom_annual["PRODUCTION_YEAR"],
y=gom_annual["GAS_BCF"],
name="GOM Gas",
marker_color="lightblue"
),
row=1, col=2
)
fig.add_trace(
go.Bar(
x=norway_annual["PRODUCTION_YEAR"],
y=norway_annual["GAS_BCF"],
name="Norway Gas",
marker_color="pink"
),
row=1, col=2
)
fig.update_layout(
title="Gulf of Mexico vs Norway: Offshore Production Comparison",
barmode="group",
height=500
)
fig.write_html(output_dir / "gom_norway_comparison.html")
print(f"Report saved to {output_dir / 'gom_norway_comparison.html'}")
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