ejentum-reasoning

Inject structured causal and temporal reasoning checks via the Ejentum Logic endpoint.

3|Updated Apr 19, 2026
One-click install
npx skills add https://github.com/ejentum/integrations --skill ejentum-reasoning
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ejentum-reasoning
Source: https://github.com/ejentum/integrations/tree/main/claude-code/skills/ejentum-reasoning
Command: npx skills add https://github.com/ejentum/integrations --skill ejentum-reasoning

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps you avoid incorrect conclusions caused by missing the real failure mode, causality, or multi-step consequences in analytical work.

Core Features & Use Cases

  • Failure-mode aware reasoning injection: Shapes how you think with constraints, procedures, and blockers so you don’t stop at the first plausible answer.
  • Causal, temporal, spatial, simulation, abstraction, and metacognition coverage: Routes your task to the most relevant reasoning dimension(s) to prevent category errors and reasoning drift.
  • When-to-call guidance and guardrails: Encourages calling only for tasks where the first answer is likely to be wrong, and includes validation and failure recovery behavior.

Quick Start

Ask your AI to analyze the task “Trace the root cause of our deployment failure after the config change last Thursday” using the ejentum-reasoning skill so it can model causal mechanisms and verify its conclusion.

Frequently Asked Questions about ejentum-reasoning

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

FAQPage Schema
How do I improve root cause analysis accuracy when the first plausible answer is wrong?

Causal analysis validates conclusions by routing tasks through specific reasoning dimensions like simulation, abstraction, and metacognition. This structured injection shapes analytical thinking with constraints and blockers to prevent category errors and reasoning drift.

Can I use structured reasoning to model multi-step consequences for risk assessment?

Planning with dependencies requires structured reasoning that applies temporal, spatial, and simulation checks to model multi-step consequences. This approach blocks common failure modes and verifies consistency to prevent missing hidden risks in complex analytical work.

When do I need metacognitive checks for tradeoff evaluation?

Metacognitive checks are needed for tradeoff evaluation when first-pass answers are likely to miss the true issue. They enforce abstraction and consistency verification to prevent reasoning drift and incorrect conclusions in complex analytical work.

What are the limitations of automated reasoning for complex analysis?

Automated reasoning should be limited to tasks where the first answer is likely to be wrong. It requires a POST request to a specific endpoint with an authorization bearer key, task query, and reasoning mode, meaning it cannot function offline or without API access.