parallel-deep-researchDeep multi-source research via Parallel API. Use when user explicitly asks for thorough research, comprehensive analysis, or investigation of a topic. For quick lookups or news, use parallel-search instead.
Install via ClawdBot CLI:
clawdbot install NormallyGaussian/parallel-deep-researchDeep, multi-source research for complex topics requiring synthesis from many sources. Returns comprehensive reports with citations.
Trigger this skill when the user asks for:
NOT for:
--after-date)parallel-cli research run "your research question" --processor pro-fast --json -o ./reportparallel-cli research run "<question>" [options]| Flag | Description |
|------|-------------|
| -p, --processor | Processor tier (see table below) |
| --json | Output as JSON |
| -o, --output | Save results to file (creates .json and .md) |
| -f, --input-file | Read query from file (for long questions) |
| --timeout N | Max wait time in seconds (default: 3600) |
| --no-wait | Return immediately, poll later with research status |
| Processor | Time | Use Case |
|-----------|------|----------|
| lite-fast | 10-20s | Quick lookups |
| base-fast | 15-50s | Simple questions |
| core-fast | 15s-100s | Moderate research |
| pro-fast | 30s-5min | Exploratory research (default) |
| ultra-fast | 1-10min | Multi-source deep research |
| ultra2x-fast | 1-20min | Difficult deep research |
| ultra4x-fast | 1-40min | Very difficult research |
| ultra8x-fast | 1min-1hr | Most challenging research |
Non-fast variants (e.g., pro, ultra) take longer but use fresher data.
Basic research:
parallel-cli research run "What are the latest developments in quantum computing?" \
--processor pro-fast \
--json -o ./quantum-reportDeep competitive analysis:
parallel-cli research run "Compare Stripe, Square, and Adyen payment platforms: features, pricing, market position, and developer experience" \
--processor ultra-fast \
--json -o ./payments-analysisLong research question from file:
# Create question file
cat > /tmp/research-question.txt << 'EOF'
Investigate the current state of AI regulation globally:
1. What regulations exist in the US, EU, and China?
2. What's pending or proposed?
3. How do companies like OpenAI, Google, and Anthropic respond?
4. What industry groups are lobbying for/against regulation?
EOF
parallel-cli research run -f /tmp/research-question.txt \
--processor ultra-fast \
--json -o ./ai-regulation-reportNon-blocking research:
# Start research without waiting
parallel-cli research run "research question" --no-wait
# Check status later
parallel-cli research status <task-id>
# Poll until complete
parallel-cli research poll <task-id> --json -o ./reportWrite 2-5 sentences describing:
Good:
Compare the top 5 CRM platforms for B2B SaaS companies with 50-200 employees.
Focus on: pricing per seat, integration ecosystem, reporting capabilities.
Include recent 2024-2026 changes and customer reviews from G2/Capterra.Poor:
Tell me about CRMsReturns structured JSON with:
task_id — unique identifier for pollingstatus — pending, running, completed, failedresult — when complete:summary — executive summaryfindings[] — detailed findings with sourcessources[] — all referenced URLs with titlesWhen presenting research results:
For long conversations, save results and use sessions_spawn:
parallel-cli research run "<question>" --json -o /tmp/research-<topic>Then spawn a sub-agent:
{
"tool": "sessions_spawn",
"task": "Read /tmp/research-<topic>.json and present the executive summary and key findings with sources.",
"label": "research-summary"
}| Exit Code | Meaning |
|-----------|---------|
| 0 | Success |
| 1 | Unexpected error (network, parse) |
| 2 | Invalid arguments |
| 3 | API error (non-2xx) |
curl -fsSL https://parallel.ai/install.sh | bash
export PARALLEL_API_KEY=your-keyGenerated Mar 1, 2026
A startup planning to enter the competitive fintech market uses this skill to conduct deep research on existing players, regulatory landscapes, and customer pain points. They request a comprehensive report comparing features, pricing, and market share of top 10 fintech platforms in the US and EU over the past 3 years, focusing on integration capabilities and user reviews from trusted sources.
A venture capital firm evaluating an investment in a healthtech company employs this skill to perform thorough due diligence. They ask for a deep investigation into the company's technology stack, patent filings, competitive advantages, and regulatory compliance history across multiple jurisdictions, synthesizing data from 15+ sources including academic papers and industry reports.
A government agency tasked with developing new renewable energy policies uses this skill to research global best practices and economic impacts. They request a comprehensive analysis of solar and wind energy adoption rates, subsidy effectiveness, and environmental outcomes in countries like Germany, China, and the US over the last decade, with emphasis on conflicting data and source credibility.
An e-commerce retailer expanding into new markets leverages this skill for deep competitive analysis. They ask for a detailed report comparing logistics, pricing strategies, and customer service models of major players like Amazon, Alibaba, and Shopify in Southeast Asia, focusing on recent 2024-2026 trends and synthesis from over 12 sources including market research firms and user forums.
A tech ethics consultancy uses this skill to research the evolving landscape of AI regulation. They request a thorough investigation into proposed laws, industry lobbying efforts, and corporate responses from companies like OpenAI and Google in the US, EU, and China, with a scope on pending legislation and conflicting viewpoints from multiple authoritative sources.
Parallel.ai offers tiered subscription plans for its research API, charging based on usage volume and processor tiers (e.g., pro-fast to ultra8x-fast). Revenue is generated from monthly or annual fees paid by businesses and developers who integrate the skill into their workflows for automated deep research, with higher tiers for more complex queries.
The company provides enterprise licenses for large organizations needing high-volume, customized research capabilities. Revenue comes from one-time setup fees and ongoing support contracts, tailored to industries like finance or healthcare where deep due diligence and competitive analysis are critical, often including dedicated support and SLA guarantees.
Parallel.ai offers a free tier with limited research capabilities (e.g., lite-fast processor) to attract individual users and small teams. Revenue is generated by upselling to paid plans with advanced features like faster processors, JSON output, and priority support, targeting users who outgrow the free tier for more intensive research needs.
💬 Integration Tip
Use the --json flag and -o output option to save results for later analysis, and consider spawning sub-agents with sessions_spawn to handle long conversations efficiently.
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