web-scraper-energy
Web scraping workflows for energy data collection from BSEE and BOEM using Scrapy
Best use case
web-scraper-energy is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Web scraping workflows for energy data collection from BSEE and BOEM using Scrapy
Teams using web-scraper-energy 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/web-scraper-energy/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How web-scraper-energy Compares
| Feature / Agent | web-scraper-energy | 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?
Web scraping workflows for energy data collection from BSEE and BOEM using Scrapy
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
# Web Scraper Energy
## When to Use This Skill
Use this skill when you need to:
- Scrape BSEE/BOEM websites for data not in APIs
- Collect lease sale results and bid data
- Extract platform and facility information
- Build automated data collection pipelines
- Parse HTML tables into structured data
## Core Pattern
```python
"""
ABOUTME: Web scraping utilities for energy data collection
ABOUTME: Supports Scrapy spiders and BeautifulSoup parsing
"""
from dataclasses import dataclass
from typing import List, Dict, Optional
from bs4 import BeautifulSoup
import requests
import time
@dataclass
class ScrapingConfig:
"""Configuration for web scraping."""
base_url: str
rate_limit_sec: float = 1.0
max_retries: int = 3
timeout_sec: int = 30
user_agent: str = "WorldEnergyData/1.0"
cache_enabled: bool = True
class BOEMScraper:
*See sub-skills for full details.*
## YAML Configuration Template
```yaml
# config/input/scraping-config.yaml
metadata:
feature_name: "energy-scraping"
created: "2025-01-15"
scraping:
rate_limit_sec: 1.5
max_retries: 3
timeout_sec: 30
cache_enabled: true
cache_ttl_hours: 24
targets:
- name: "lease_sales"
source: "boem"
sale_numbers: [257, 258, 259]
output: "data/lease_sales/"
- name: "platforms"
source: "boem"
areas: ["GC", "WR", "MC"]
output: "data/platforms/"
*See sub-skills for full details.*
## CLI Usage
```bash
# Scrape lease sale results
python -m worldenergydata.scraper \
--source boem \
--type lease-sale \
--sale 259 \
--output data/lease_sale_259.csv
# Scrape platform data
python -m worldenergydata.scraper \
--source boem \
--type platforms \
--area GC \
--output data/gc_platforms.csv
```
## Web Crawling & MCP Assessment (2026-03-14)
**No external MCP or paid service needed for energy data scraping.**
This skill's `requests` + `BeautifulSoup` pattern is sufficient for BSEE/BOEM/EIA targets.
For async fetching at scale (WRK-1202 Tier 3), upgrade to `httpx` (async) + `beautifulsoup4`.
For JS-rendered pages, use `claude-in-chrome` browser automation (already available).
See `doc-research-download` skill for the full assessment.
## Sub-Skills
- [Best Practices](best-practices/SKILL.md)Related Skills
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.
energy-data-visualizer
Interactive visualization for oil and gas production data analysis using Plotly dashboards
financial-analysis-energy-market-analysis
Sub-skill of financial-analysis: Energy Market Analysis (+3).
product-roadmap-energy-og
Sub-skill of product-roadmap: Energy & O&G (+2).
test-oversized-skill
A test fixture skill that exceeds 200 lines with multiple H2/H3 sections for split testing.
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.
agent-os-framework
Generate standardized .agent-os directory structure with product documentation, mission, tech-stack, roadmap, and decision records. Enables AI-native workflows.
OrcaFlex Specialist Skill
```yaml
repo-ecosystem-hygiene
Interpret the daily read-only repo ecosystem hygiene audit and route remediation through approved workflows.
domain-knowledge-sweep
Systematic multi-source research of an engineering domain. Spawns parent issue → 6 research subissues (Standards, Academic, Industry, LinkedIn-marketing, Code-audit, Synthesis) → gap implementation subissues. Replaces LinkedIn-only extraction with defensible comprehensive sourcing.
subagent-write-verification
Independently verify subagent-claimed file writes with filesystem and git checks before treating the artifact as real, before committing it, and before referencing the path in downstream prompts.