unit-analyst

Standardize analysis of research units with evidence-bound prepare-fill-verify workflows.

5|Updated Apr 13, 2026
One-click install
npx skills add https://github.com/caozx1110/ResearchLab --skill unit-analyst
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: unit-analyst
Source: https://github.com/caozx1110/ResearchLab/tree/main/skills/unit-analyst
Command: npx skills add https://github.com/caozx1110/ResearchLab --skill unit-analyst

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill solves the problem of inconsistent and unverified analysis of research materials by providing a standardized, evidence-first workflow for papers, repositories, datasets, and blogs.

Core Features & Use Cases

  • Evidence-Bound Analysis: Ensures every claim made by the AI is backed by a verbatim quote and locator from the source material.
  • Kind-Specific Routing: Automatically routes analysis tasks to specialized implementations for different content types like papers or code repositories.
  • Verification Gate: Enforces a strict prepare-fill-verify cycle that prevents unverified or hollow content from entering the knowledge base.

Quick Start

Use the unit-analyst skill to prepare a deep-read analysis for the paper unit identified by the provided ID.

Frequently Asked Questions about unit-analyst

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

FAQPage Schema
How do I verify research analysis claims against source material?

To verify research analysis claims, an evidence-bound workflow enforces a strict prepare-fill-verify cycle that requires every AI-generated claim to be backed by a verbatim quote and source locator, preventing unverified content from entering the knowledge base.

What is the best way to standardize analysis across papers, datasets, and repositories?

Standardizing analysis across diverse research units is achieved through kind-specific routing, which automatically directs papers, repositories, datasets, and blogs to specialized implementations for structured, consistent evidence extraction.

How does an agent-led filling workflow ensure research integrity?

An agent-led filling workflow ensures research integrity by coordinating structured content generation within a managed runtime environment, validating all generated analysis claims against source parse-caches before final verification.

Do I need a managed runtime environment to execute research analysis scripts?

Yes, a managed runtime environment is required to execute the implementation scripts and validate analysis claims against source parse-caches during the verification gate phase of the research workflow.

Why does my research unit analysis output contain unverified or hollow content?

Unverified or hollow content appears when the verification gate is bypassed, meaning the strict prepare-fill-verify cycle was not completed to validate claims against the source parse-caches.

Can I use evidence-bound analysis for different content types like blogs?

Yes, evidence-bound analysis supports different content types like blogs through kind-specific routing, which automatically matches each research unit to a specialized implementation for accurate standardized processing.