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xleliberty
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setup

Standardize research experiment initialization with domain parameters and evaluation criteria.

Updated Nov 3, 2016
Search Tags:#automation#research#benchmarking#evaluation#experiment#autoresearch
One-click install
npx skills add https://github.com/xleliberty/mydotfiles --skill setup-xleliberty
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: setup
Source: https://github.com/xleliberty/mydotfiles/tree/main/.config/.claude/plugins/cache/claude-code-skills/engineering-advanced-skills/2.1.2/autoresearch-agent/skills/setup
Command: npx skills add https://github.com/xleliberty/mydotfiles --skill setup-xleliberty

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill removes the friction of manually configuring research experiments by providing a standardized, interactive interface to define parameters, evaluators, and project scopes.

Core Features & Use Cases

  • Interactive Configuration: Guides users through a step-by-step setup process for domain, target files, and metrics.
  • Built-in Evaluators: Provides pre-configured evaluators for common tasks like benchmark speed, memory usage, and LLM-based content quality assessment.
  • Experiment Management: Allows users to list existing experiments and available evaluators to maintain project consistency.

Quick Start

Initiate the interactive setup process by running the setup command to configure your new research experiment.

Frequently Asked Questions about setup

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

FAQPage Schema
How do I configure parameters for an automated research experiment?▼

To configure automated research experiments, use an interactive setup process that standardizes initialization by collecting domain-specific parameters, target files, and evaluation criteria for consistent tracking.

What evaluation metrics can I set up for engineering benchmarking?▼

For engineering benchmarking, you can configure performance metrics using built-in evaluators that assess common criteria like benchmark speed and memory usage to ensure consistent experiment execution.

Does the autoresearch setup support LLM-based content evaluation?▼

Yes, the autoresearch setup supports LLM-based content evaluation by providing pre-configured evaluators specifically designed for content quality assessment within engineering and research workflows.

Can I list existing experiments and available evaluators during setup?▼

Yes, you can list existing experiments and available evaluators during setup to maintain project consistency and ensure standardized tracking across your automated research workflows.

What's the best way to standardize initialization for multiple research workflows?▼

The best way to standardize research workflow initialization is using a guided setup that defines project scopes, integrates built-in evaluation scripts, and enforces consistent experiment tracking.

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