Coordinate parallel research agents to decompose topics and synthesize findings.
npx skills add https://github.com/DSado88/squall --skill squall-research
Please help me install this Agent Skill. Skill: squall-research Source: https://github.com/DSado88/squall/tree/main/.claude/skills/squall-research Command: npx skills add https://github.com/DSado88/squall --skill squall-research
Squall Research coordinates a team of parallel agents to decompose topics, perform web searches, and synthesize findings into a coherent report, enabling faster, multi-perspective analysis.
Ask Squall to run a multi-vector research on a topic and store outputs under .squall/research/.
High-intent search queries and answers about installing and using this skill.
Multi-agent research coordinates parallel agents to investigate broad topics by decomposing them into 3-5 independent vectors, performing web searches, and synthesizing findings into a coherent report.
Topic decomposition for strategic analysis splits broad subjects into independent vectors like method comparisons or architecture reviews, assigning parallel agents to investigate each angle before synthesis.
To start a multi-vector research workflow and save outputs to disk, ask the system to run research on a topic, and it will automatically store generated outputs under the .squall/research/ directory.
Yes, multi-agent research supports multi-model review by aggregating diverse perspectives from different models and writing disk-based outputs, ensuring findings remain reproducible for later synthesis.
Use parallel research agents instead of a single agent when your topic requires diverse perspectives across 3-5 independent angles, such as method comparisons, architecture reviews, or strategic analyses.
Yes, you can reuse research patterns from previous multi-agent synthesis tasks because the system memorizes patterns during the final synthesis compilation, enabling faster analysis on future topics.
jiesouStructure engineering research with broad searches and deep technical dives.
Baphomet480Execute structured multi-phase research to produce cited reports with visual evidence.
gracefullightOrchestrates parallel AI subagents via Gemini CLI with MCP Memory-coordinated workflows.
phuonghxOrchestrate multi-agent task execution with decomposition, parallel reads, and sequential writes.
