meow:elicit

Re-examine agent outputs through named reasoning methods to produce structured findings.

14|2|Updated Mar 25, 2026
One-click install
npx skills add https://github.com/ngocsangyem/MeowKit --skill meow-elicit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: meow:elicit
Source: https://github.com/ngocsangyem/MeowKit/tree/main/.claude/skills/meow%3Aelicit
Command: npx skills add https://github.com/ngocsangyem/MeowKit --skill meow-elicit

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Re-examines an existing output (verdict, plan, or analysis) through named reasoning methods to surface deeper insights.

Core Features & Use Cases

  • Supports multiple reasoning lenses: Pre-mortem, Inversion, Red Team, Socratic, First Principles, and more.
  • Guides users through a deterministic workflow: load context, select a method, apply a lens, and produce structured findings.
  • Integrates with the meow:review cycle to append analyses without altering the original verdict.
  • Use cases include post-review deep-dives, plan validation, and adversarial testing of outputs.

Quick Start

Provide a selected lens on the current output to generate structured findings.

Frequently Asked Questions about meow:elicit

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

FAQPage Schema
What is structured second-pass reasoning for analyzing agent outputs?▼

Structured second-pass reasoning re-examines existing outputs through named reasoning methods like pre-mortem or inversion to surface deeper insights. It applies a deterministic workflow to load context, select a method, and produce lens-based findings.

How do I apply reasoning lenses to validate a generated plan?▼

To validate a plan, provide a selected reasoning lens on the current output to generate structured findings. The workflow guides you through loading context, selecting a method like first principles, and applying the lens.

When do I need adversarial testing for agent outputs?▼

You need adversarial testing when deeper analysis is required after a review verdict, plan creation, or any agent output. It uses reasoning methods like red team and socratic lenses to surface deeper insights and validate outputs.

Can I append a post-review deep-dive analysis without altering the original verdict?▼

Yes, you can append analyses without altering the original verdict by integrating with the review cycle. This structured elicitation workflow loads existing context and produces separate lens-based findings for deeper analysis.

What reasoning methods are available for lens-based analysis?▼

Available reasoning methods for lens-based analysis include pre-mortem, inversion, red team, socratic, and first principles. These named lenses guide the structured elicitation workflow to re-examine existing outputs and surface deeper insights.

Does structured elicitation require any external dependencies or components?▼

No external dependencies or components are required for structured elicitation. The workflow operates independently to load context, select a reasoning method, apply a lens, and produce structured findings from existing outputs.