skills/minerals-news-monitor/SKILL.md
Discover critical-minerals and materials signals from newspapers, blogs, and industry media using web search, with normalized policy/commodity tagging
npx skillsauth add lamm-mit/scienceclaw minerals-news-monitorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Discover relevant links from newspapers, blogs, and industry outlets for critical minerals and materials topics.
# Broad monitoring
python3 {baseDir}/scripts/news_monitor.py --query "critical minerals" --max-results 20
# Commodity/country targeted monitoring
python3 {baseDir}/scripts/news_monitor.py \
--commodity lithium --commodity cobalt \
--country china --country canada \
--format json
# Blogs only
python3 {baseDir}/scripts/news_monitor.py --query "battery materials" --source-type blogs
| Parameter | Description | Default |
|-----------|-------------|---------|
| --query | Base search query | critical minerals |
| --commodity | Commodity term (repeatable) | - |
| --country | Country term (repeatable) | - |
| --max-results | Max records returned | 20 |
| --source-type | all, news, blogs | all |
| --format | summary, detailed, json | summary |
Each record includes:
url, source, published_at, title, summary, commodity_tags, country_tags, policy_signal, confidence, retrieved_at, source_type.
minerals-web-ingest to fetch full content and persist hashes.tools
Onboard and manage Paperclip AI for research-paper knowledge and agent orchestration
development
Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web searches for current information, finding recent scientific literature, getting grounded answers with source citations, or accessing information beyond the model knowledge cutoff. Provides access to multiple Perplexity models including Sonar Pro, Sonar Pro Search (advanced agentic search), and Sonar Reasoning Pro through a single OpenRouter API key.
testing
Generate a structured scientific PDF report from a JSON description. Accepts a JSON file specifying title, authors, abstract, sections (headings, text, tables, figures), and inline data panels (heatmap, bar, scatter, line). Produces a publication-style A4 PDF using reportlab with no LaTeX dependency. All figures are either loaded from PNG paths or generated on-the-fly from inline data.
development
Execute arbitrary Python code and return stdout. NumPy, pandas, scipy, matplotlib, and other scientific libraries are available.