exhaustive-crosscheck

Orchestrate multi-phase code analysis with cursor decomposition, LTM cross-check, and Neuro analysis.

3|Updated Jun 12, 2026

Search Tags:#code analysis #parallel processing #meta-skill #code hardening #code enhancement

Ready to run

npx skills add https://github.com/weebcoder101/dreamcode --skill exhaustive-crosscheck

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: exhaustive-crosscheck
Source: https://github.com/weebcoder101/dreamcode/tree/main/.dreamcode/skills/exhaustive-crosscheck
Command: npx skills add https://github.com/weebcoder101/dreamcode --skill exhaustive-crosscheck

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires neuro, code-hardener, lint-fixer, pieces-ltm, automated-learning, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses complex coding tasks by integrating and orchestrating various analysis and enhancement components into a cohesive workflow, ensuring robust and efficient code development.

Core Features & Use Cases

Multi-phase Analysis: Decomposes prompts into orthogonal cursors, then cross-checks with LTM and fires parallel Piece LTM searches.
Neuro Integration: Includes Neuro analysis for deep code insights, followed by code-hardener and lint-fixer phases for refinement.
Self-Evolution: Continuously improves itself based on run outcomes.
Use Case: Ideal for complex coding tasks that require comprehensive analysis, including feature development, bug fixes, and code reviews.

Quick Start

Use the exhaustive-crosscheck skill to analyze and enhance your code with full neuro analysis, followed by code-hardening and lint fixing.

Frequently Asked Questions about exhaustive-crosscheck

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

FAQPage Schema

What is comprehensive code analysis and how does it improve code quality?▼

Comprehensive code analysis decomposes prompts into orthogonal cursors, cross-checks with long-term memory, and applies neuro analysis to provide deep code insights. It improves code quality by systematically identifying vulnerabilities and refining logic across multiple processing stages.

How do I orchestrate parallel code analysis and enhancement for complex coding tasks?▼

You orchestrate parallel code analysis by decomposing tasks into independent cursors, firing parallel LTM searches, and sequentially applying neuro analysis, code-hardening, and lint fixing. This multi-phase workflow ensures robust code development for complex feature implementations and bug fixes.

Does exhaustive code cross-checking require specific dependencies to run?▼

Yes, exhaustive code cross-checking requires multiple dependencies including neuro for deep analysis, code-hardener for vulnerability patching, lint-fixer for syntax correction, and pieces-ltm for memory cross-checking. These skills must be installed to execute the orchestrated workflow.

Can I use automated code hardening and lint fixing for large-scale code reviews?▼

Yes, automated code hardening and lint fixing are ideal for large-scale code reviews. The system processes complex coding tasks by firing parallel LTM searches and applying sequential enhancement phases, ensuring robust and efficient code refinement across extensive codebases.

What is the best way to continuously evolve code analysis workflows based on outcomes?▼

The best way to continuously evolve code analysis workflows is through meta-skills that learn from run outcomes. This self-evolution mechanism improves future cursor decomposition, LTM cross-checks, and neuro analysis by integrating historical run data into subsequent processing logic.

Why does my code analysis workflow fail without long-term memory cross-checks?▼

Code analysis workflows fail without long-term memory cross-checks because they lose the ability to validate orthogonal cursors against historical context. LTM cross-checks provide essential memory integration required to accurately decompose prompts and fire parallel searches for deep code insights.