Subagent-Driven Literature Review

Parallel screening dispatches multiple AI subagents simultaneously to review large batches of papers, drastically reducing review time compared to sequential single-agent screening. This approach handles 50+ papers efficiently by distributing the workload across concurrent agents.

Yes. Deep-dive analysis assigns individual subagents to extract tables, methods, and key findings from priority papers in parallel. Batch dispatch with consolidation checkpoints manages results even when a single context window reaches capacity limits.

Citation-network exploration uses subagents to efficiently traverse forward and backward citation links across your research set. Parallel processing lets you expand research scope faster than manual citation tracking while maintaining structured JSON result formats.

Use parallel subagent screening for literature reviews of 50+ papers, especially when you need deep-dive extraction from multiple relevant articles or must explore citation networks. It prevents context overflow and consolidates results via rubric-based prompts and structured output.

Dispatch batches by providing a paper list and relevance query to subagents. The skill implements batch dispatch, rate-limit guidance, and consolidation checkpoints to organize results. Start with abstracts; subagents retrieve full texts and extract structured data as needed.

Context management through subagent orchestration delegates overflow papers to parallel agents, preserving your main context. Results consolidate via JSON formats and consolidation checkpoints, allowing you to continue screening and analysis without context constraints.