How does the Parallel Research skill work?

It spawns five independent sub-agents that simultaneously investigate technical specs, ecosystem tools, expert patterns, pitfalls, and implementation paths, then synthesizes their findings into one structured report.

Where are the research findings stored during the process?

Temporary findings are saved in a local .research/ directory during the investigation phase and are automatically cleaned up once the final synthesis is generated.

Can I specify the research topic via command line?

Yes, you can provide the topic directly as an argument (e.g., 'research TypeScript performance') or simply run the command and provide the topic when prompted.

Does this skill use multiple model invocations?

Yes, by spawning parallel sub-agents, it performs multiple concurrent invocations to provide a more comprehensive and faster analysis than a single linear prompt could achieve.

Parallel Research

Name: Parallel Research
Author: potenlab

bypotenlab

生产力与工作流

Conducts multi-dimensional research using parallel sub-agents to synthesize technical insights and implementation paths.

关于

The Research skill accelerates technical decision-making by deploying five specialized sub-agents to investigate any topic across technical, ecosystem, architectural, and practical dimensions simultaneously. It automates the complex process of gathering expert patterns, identifying common pitfalls, and recommending a clear path forward, ultimately consolidating diverse findings into a single, actionable report with confidence scores for every insight.

主要功能

Automatic synthesis of findings to resolve cross-dimensional contradictions
Structured analysis across technical, ecosystem, and pattern dimensions
Confidence-graded insights for reliable technical decision making
Multi-agent parallel execution for rapid data gathering
Actionable recommendations with specific next steps and source tracking
0 GitHub stars

使用场景

Conducting deep-dive analysis on complex technical pitfalls and gotchas
Evaluating new libraries or frameworks before project adoption
Investigating architectural patterns for specific technical challenges

关于

主要功能

Automatic synthesis of findings to resolve cross-dimensional contradictions
Structured analysis across technical, ecosystem, and pattern dimensions
Confidence-graded insights for reliable technical decision making
Multi-agent parallel execution for rapid data gathering
Actionable recommendations with specific next steps and source tracking
0 GitHub stars

使用场景

Conducting deep-dive analysis on complex technical pitfalls and gotchas
Evaluating new libraries or frameworks before project adoption
Investigating architectural patterns for specific technical challenges