skeptic-protocol

Enforce Skeptic protocol rules for question-first data analysis with YAML artifacts.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Skeptic-protocol enforces auditable, constraint-driven management of analysis stages, preventing arbitrary decisions and ensuring reproducible workflows across the Skeptic lifecycle.

Core Features & Use Cases

  • Locks data-usage modes, leakage rules, validation requirements, prohibitions, and backtracking triggers for each cycle.
  • Generates canonical YAML stage memory and compact cycle-evidence artifacts to support traceability and auditing.
  • Enables autonomous cycle execution with --auto when appropriate, reducing manual prompts while maintaining governance.

Quick Start

Start skeptic-protocol after formulate to enforce protocol rules and drive Cycles A–D with deterministic, auditable outputs.

Frequently Asked Questions about skeptic-protocol

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

FAQPage Schema
How do I enforce auditable rules for data analysis cycles?

Auditable data analysis requires locking data-usage modes, validation requirements, and prohibitions across cycles. This constraint-driven protocol generates canonical YAML artifacts and cycle-evidence records to ensure reproducible workflows and prevent arbitrary analytical decisions.

How do I automate question-first data analysis workflows?

Automating question-first data analysis involves invoking autonomous execution modes to drive analysis cycles. This reduces manual prompts while maintaining governance through locked validation requirements, leakage rules, and backtracking triggers across the lifecycle.

What is the best way to prevent data leakage during reproducible analysis?

To prevent data leakage in reproducible analysis, lock data-usage modes and leakage rules after formulating questions. Enforcing these constraints via canonical YAML stage memory ensures traceability and prevents leakage across backtracking cycles.

How do you track backtracking across data analysis cycles?

Track backtracking by generating compact cycle-evidence artifacts that record validation states and prohibitions. This constraint-driven management ensures every stage transition and backtracking trigger across the analysis lifecycle is traceable and auditable.

When should I lock validation requirements in a data analysis protocol?

Lock validation requirements immediately after the formulate stage to enforce constraint-driven management. Applying these prohibitions and backtracking triggers early ensures deterministic, reproducible execution across all subsequent analysis cycles.

Do I need YAML artifacts to audit data analysis workflows?

Canonical YAML stage memory artifacts are required to support traceability and auditing in data analysis workflows. They lock data-usage modes and evidence logic, ensuring the entire analysis lifecycle remains reproducible and auditable.