Parallel Web Search

Parallel Web Search

Agentic web search optimized for LLM workflows using Parallel AI API.

When to Use

• User needs current/real-time information
• User asks about recent events or news
• User needs to fact-check or verify claims
• User wants to research a topic with citations
• User needs information beyond training data cutoff

Requirements

Environment variable must be set:

• PARALLEL_API_KEY - Parallel AI API key

Command

./scripts/parallel_search search -o "objective" [-q "query"] [-n limit]

Options

Output Format

Default JSON for LLM parsing:

{
  "objective": "Find recent AI regulation news",
  "queries": ["AI regulation 2024", "EU AI Act"],
  "results": [
    {
      "title": "EU AI Act Implementation Timeline",
      "url": "https://example.com/article",
      "excerpt": "The European Union's AI Act...",
      "publish_date": "2024-12-15"
    }
  ]
}

Examples

Basic search:

./scripts/parallel_search search -o "What are the latest developments in fusion energy?"

With keyword queries (improves results):

./scripts/parallel_search search \
  -o "Recent breakthroughs in quantum computing" \
  -q "quantum computing 2024" \
  -q "quantum supremacy"

Restrict to specific domains:

./scripts/parallel_search search \
  -o "Climate change research findings" \
  -d "nature.com" \
  -d "science.org" \
  -n 5

Human-readable table output:

./scripts/parallel_search search -o "AI safety news" -f table

Best Practices

1. Use both objective AND queries - Objective provides context, queries ensure keyword coverage
2. Be specific - Include timeframes, sources preferences, or content types
3. Limit results for efficiency - Use -n 5 for quick lookups
4. Domain filtering - Use -d for authoritative sources on specific topics

Source: nc9/skills — distributed by TomeVault.