oil-and-gas-core-libraries

Sub-skill of oil-and-gas: Core Libraries (+1).

5 stars

Best use case

oil-and-gas-core-libraries is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Sub-skill of oil-and-gas: Core Libraries (+1).

Teams using oil-and-gas-core-libraries 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

$curl -o ~/.claude/skills/core-libraries/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/_archive/engineering/oil-and-gas/core-libraries/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/core-libraries/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How oil-and-gas-core-libraries Compares

Feature / Agent	oil-and-gas-core-libraries	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 oil-and-gas: Core Libraries (+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

# Core Libraries (+1)

## Core Libraries


```python
import pandas as pd
import numpy as np
import scipy.optimize

import matplotlib.pyplot as plt
import plotly.graph_objects as go

from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler

import lasio      # LAS file reading
import welly      # Well log analysis
import striplog   # Lithology and stratigraphy
```

## worldenergydata Custom Modules


```python
from worldenergydata.decline_curves import arps_decline
from worldenergydata.pvt import standing_correlation
from worldenergydata.material_balance import tank_model
from worldenergydata.economics import npv_analysis
```

Related Skills

provider-utilization-scorecard

from vamseeachanta/workspace-hub

Refresh provider quota snapshots and generate a weekly Codex/Codex/Gemini utilization scorecard grounded in quota data when available and session-activity fallback when not.