sciomc

Orchestrate parallel scientist agents to research complex goals and synthesize reports.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/mrsono0/lectures --skill sciomc
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: sciomc
Source: https://github.com/mrsono0/lectures/tree/main/.claude/skills/sciomc
Command: npx skills add https://github.com/mrsono0/lectures --skill sciomc

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates complex research and analysis tasks by orchestrating multiple AI scientist agents in parallel, significantly speeding up the discovery and synthesis of information.

Core Features & Use Cases

  • Parallel Agent Execution: Decomposes research goals into stages and runs scientist agents concurrently for faster results.
  • Automated Research Workflows: Supports both standard interactive research and fully autonomous "AUTO" mode.
  • Verification and Synthesis: Includes stages for cross-validating findings and generating comprehensive reports.
  • Use Case: Automatically analyze the security patterns across an entire codebase by breaking down the task into file enumeration, token handling analysis, session management review, and vulnerability identification, all executed in parallel.

Quick Start

Use the sciomc skill to orchestrate parallel scientist agents for comprehensive analysis of the authentication patterns in this codebase.

Frequently Asked Questions about sciomc

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

FAQPage Schema
How do I automate complex code analysis across an entire codebase in parallel?

Parallel research is orchestrated by decomposing complex goals into stages and executing scientist agents concurrently to synthesize comprehensive analysis reports. It speeds up discovery by running verification and synthesis workflows in parallel.

Can I run fully autonomous research workflows without manual intervention?

Fully autonomous research is supported via AUTO mode, which uses iterative loop control and promise tags for completion or blocking. This enables scientist agents to execute research stages independently without manual intervention.

How do parallel scientist agents verify findings during research?

Findings are verified through dedicated cross-validation stages within the orchestration workflow before synthesis. Scientist agents check results against each other to ensure comprehensive and accurate final reports.

Does parallel agent execution support different models for research tasks?

Parallel agent execution supports specified models including haiku, sonnet, and opus for executing research stages. Different models can be assigned to parallel tasks based on complexity and performance requirements.

What is the best way to analyze security patterns across a large codebase?

Security pattern analysis is best handled by decomposing the task into parallel stages such as file enumeration, token handling analysis, session management review, and vulnerability identification. Scientist agents execute these concurrently for comprehensive results.