skeptic-examine

Maps distributions, anomalies and fragility of cleaned, protocol-visible data for analysis handoff.

Updated Apr 9, 2026
One-click install
npx skills add https://github.com/Filivignaga/skeptic --skill skeptic-examine
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: skeptic-examine
Source: https://github.com/Filivignaga/skeptic/tree/main/codex/skeptic-examine
Command: npx skills add https://github.com/Filivignaga/skeptic --skill skeptic-examine

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Skeptic examine helps analysts understand the real evidentiary support in the cleaned, protocol-visible data after formulate, protocol, and clean, by mapping distributions, structure, anomalies, and fragility, while keeping exploratory observations distinct from confirmed claims.

Core Features & Use Cases

  • Verify data visibility per protocol and route.
  • Characterize distributions, structures, anomalies, and fragilities to guide the subsequent analyze stage.
  • Provide a concise handoff to analyze detailing which aspects are well-supported and where caution is needed.

Quick Start

Run skeptic examine to characterize what the cleaned, protocol-visible data can support within the approved route and protocol.

Frequently Asked Questions about skeptic-examine

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

FAQPage Schema
How do I determine what my cleaned data can actually support before analysis?

Examine cleaned data by mapping its distributions, relationships, anomalies, and fragility to identify evidentiary support. This protocol-visible assessment keeps exploratory observations distinct from confirmed claims, providing a concrete handoff to detail which aspects are well-supported for analysis.

Why should I verify protocol visibility before analyzing cleaned data?

Verifying protocol visibility ensures your cleaned data aligns with the approved route and protocol before analysis. This assessment maps data fragility and anomalies, keeping exploratory observations distinct from confirmed claims to prevent unsupported analytical conclusions.

What is the difference between exploratory observations and confirmed claims in data examination?

Exploratory observations are patterns mapped during data examination, while confirmed claims require verified evidentiary support. Keeping them distinct ensures your cleaned data handoff accurately details which aspects are well-supported and where caution is needed for the analyze stage.

Can I run autonomous data examination with protocol visibility checks?

Yes, you can run autonomous data examination with protocol visibility checks using the --auto flag. This mode is permitted when the active route and protocol allow it, enabling the tool to map distributions and anomalies without manual intervention.

How do I map data fragility and anomalies for an auditable handoff?

Map data fragility and anomalies by examining the protocol-visible portion of the dataset to characterize its structure. This auditable process identifies evidentiary support limits and provides a concise handoff detailing where caution is needed before the analyze stage.