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sciomc

Orchestrate parallel scientist agent workflows to decompose research goals and synthesize verified reports.

1|Updated Mar 30, 2026
Search Tags:#code analysis#research#verification#parallel agents#report generation#session management#AUTO mode
One-click install
npx skills add https://github.com/Leap0920/Clean-Portfolio --skill sciomc-leap0920
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sciomc
Source: https://github.com/Leap0920/Clean-Portfolio/tree/main/%25USERPROFILE%25/.openclaude/plugins/cache/omc/oh-my-claudecode/4.14.0/skills/sciomc
Command: npx skills add https://github.com/Leap0920/Clean-Portfolio --skill sciomc-leap0920

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It solves slow, incomplete research by coordinating multiple specialized scientist agents to decompose a complex question, run investigations in parallel, verify results, and synthesize a comprehensive report.

Core Features & Use Cases

  • Parallel research execution: Break a goal into 3–7 independent stages and run scientist agents concurrently to gather and analyze evidence efficiently.
  • Verification and conflict checking: Cross-validate findings across stages to catch contradictions, gaps, and missing coverage before reporting.
  • AUTO mode for hands-off workflows: Continue through decomposition, execution, verification, and synthesis autonomously until completion or a blocking condition occurs.
  • Report generation: Produce a structured final report from verified findings, including an executive summary, methodology, key findings, and limitations.

Quick Start

Orchestrate parallel research for a specific goal by running /oh-my-claudecode:sciomc AUTO: your research question.

Frequently Asked Questions about sciomc

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run parallel research agents to investigate a complex topic?▼

Parallel research agents are orchestrated by decomposing a complex goal into 3–7 independent stages, running scientist agents concurrently to gather and analyze evidence, and synthesizing verified results into a structured report.

What is the best way to automate end-to-end codebase analysis and report generation?▼

Automated codebase analysis and report generation is handled by AUTO mode, which autonomously continues through decomposition, execution, cross-validation, and synthesis until completion or a blocking condition occurs.

How does cross-validation work when gathering evidence across multiple research stages?▼

Cross-validation checks findings gathered across parallel stages to catch contradictions, identify coverage gaps, and resolve conflicts before synthesizing the final structured report.

Do I need to provide a specific research question to start a parallel agent session?▼

Yes, you must pass a specific research goal or an AUTO mode directive to initiate the session, which the system uses to decompose the question and route tasks to specialized scientist agents.

What limitations should I expect when using parallel agents for hypothesis testing?▼

Limitations of parallel hypothesis testing include potential blocking conditions during autonomous AUTO mode execution and the necessity of explicit model routing, with any coverage gaps or contradictions documented in the final report's limitations section.

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