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SKILL verified MIT Self-run

Parallel Web Search

skill-nc9-skills-parallel-web-search · by nc9

Performs agentic web search using Parallel AI. Use when user needs current web information, research, fact-checking, news, or real-time data beyond training cutoff.

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Install

$ agentstack add skill-nc9-skills-parallel-web-search

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
0 installs to date
no reviews yet
3mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

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

| Option | Description | |--------|-------------| | -o, --objective | Natural language search goal (required) | | -q, --query | Additional keyword queries (can repeat) | | -n, --limit | Max results 1-20 (default: 10) | | -c, --max-chars | Max chars per excerpt (default: 500) | | -d, --domain | Restrict to domains (can repeat) | | -f, --format | Output: json (default) or table |

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 & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.