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parallel-search

@otrebu/agents
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Comprehensive web research via Parallel Search API. Use when user requests parallel search for deep multi-source research, technical analysis, learning new topics, current events, or comparative studies. Returns LLM-ready ranked URLs with extended excerpts (up to 30K chars). Single API call handles multiple query angles with automatic deduplication.

Install Skill

1Download skill
2Enable skills in Claude

Open claude.ai/settings/capabilities and find the "Skills" section

3Upload to Claude

Click "Upload skill" and select the downloaded ZIP file

Note: Please verify skill by going through its instructions before using it.

SKILL.md

name parallel-search
description Comprehensive web research via Parallel Search API. Use when user requests parallel search for deep multi-source research, technical analysis, learning new topics, current events, or comparative studies. Returns LLM-ready ranked URLs with extended excerpts (up to 30K chars). Single API call handles multiple query angles with automatic deduplication.
allowed-tools Bash(pnpm tsx*)

Parallel Search

Web research using Parallel's Search API with extended excerpts (up to 30K chars per result).

When to Use

Use for comprehensive research on:

  • Technical topics requiring multiple perspectives
  • New frameworks, libraries, technologies
  • Comparative analysis
  • Current events
  • Documentation synthesis

Prerequisites

Required:

Dependencies: Auto-installed via pnpm

Workflow

When user requests research:

  1. Analyze question to identify main objective
  2. Generate 3-5 targeted query angles for multi-perspective coverage
  3. Execute single bash command with --objective and --queries parameters
  4. API returns deduplicated results from parallel execution
  5. Analyze extended excerpts and synthesize findings
  6. Save report to docs/research/parallel/TIMESTAMP-topic.md

Usage

Comprehensive Research (Recommended)

cd plugins/knowledge-work/skills/parallel-search
pnpm tsx scripts/search.ts \
  --objective "Production RAG system architecture" \
  --queries \
    "RAG chunking strategies" \
    "RAG evaluation metrics" \
    "RAG deployment challenges" \
    "RAG vector database selection"

The API executes all queries in parallel and returns deduplicated results automatically.

Quick Single Query

pnpm tsx scripts/search.ts --objective "When was the UN founded?"

Processor Levels

# Default: pro (balanced quality/speed)
pnpm tsx scripts/search.ts --objective "..."

# Ultra: maximum quality for critical research
pnpm tsx scripts/search.ts --objective "..." --processor ultra

Parameters

  • --objective (required): Main search objective (natural language, be specific)
  • --queries: Additional query angles (max 5, 200 chars each)
  • --processor: lite/base/pro/ultra (default: pro)
  • --max-results: Results per search (default: 15)
  • --max-chars: Excerpt length per result (default: 5000, max: 30000)

Output Format

Returns markdown with:

  • Search metadata (objective, result count, execution time)
  • Top domains distribution
  • Ranked results:
    • Title and URL
    • Domain
    • Extended excerpts (joined with double newlines)
    • Rank

Query Generation Strategy

For broad topics: Generate queries covering different aspects

Example: "RAG systems"

  • Objective: "Production RAG system architecture overview"
  • Queries: "chunking strategies", "evaluation metrics", "deployment patterns", "vector databases"

For comparisons: Generate queries for each option plus general comparison

Example: "PostgreSQL vs MongoDB"

  • Objective: "PostgreSQL vs MongoDB comparison"
  • Queries: "PostgreSQL use cases", "MongoDB use cases", "relational vs document databases"

For current events: Use temporal and source diversity

Example: "Latest AI developments"

  • Objective: "Recent AI model releases and benchmarks"
  • Queries: "GPT-4 updates", "open source LLMs", "AI safety research", "industry adoption"

Research Persistence

After synthesis, save report:

  1. Get timestamp: Use timestamp skill for YYYYMMDDHHMMSS format
  2. Sanitize topic: Use sanitizeForFilename from formatter.ts (kebab-case, 50 char limit)
  3. Save to: docs/research/parallel/TIMESTAMP-topic.md
  4. Include: Findings, sources with URLs, analysis

Error Handling

Missing API key:

export PARALLEL_API_KEY="your-key-here"

Rate limit exceeded: Wait for reset time (shown in error message)

Network errors: Retry with --processor lite for faster response

Validation errors: Check constraints (max 5 queries, 200 chars each)

Constraints

  • Max 5 queries per request
  • Max 200 chars per query
  • Max 30K chars per excerpt (not guaranteed above 30K)
  • Rate limits depend on API plan tier
  • Requires internet connection

Best Practices

  • Use specific objectives: "Production RAG architecture" > "RAG systems"
  • Leverage all 5 query slots for comprehensive coverage
  • Use --max-chars up to 30000 for deep content analysis
  • Adapt processor level to urgency: pro for most, ultra for critical
  • Save multi-query research for future reference

Implementation

Files:

  • types.ts - Interfaces and error types
  • parallel-client.ts - API client with validation
  • formatter.ts - Markdown output formatting
  • log.ts - CLI logging
  • search.ts - CLI entry point

Testing:

pnpm test