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ccg-latent

Apply latent chain reasoning to minimize token usage in multi-step development tasks.

6|3|Updated Dec 4, 2025
Search Tags:#code review#development workflow#bug fix#code refactoring#token optimization#multi-step tasks#latent chain
One-click install
npx skills add https://github.com/phuongrealmax/claude-code-guardian --skill ccg-latent
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ccg-latent
Source: https://github.com/phuongrealmax/claude-code-guardian/tree/main/plugins/ccg-guardian/skills/ccg-latent
Command: npx skills add https://github.com/phuongrealmax/claude-code-guardian --skill ccg-latent

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Complex development tasks often involve many steps, leading to high token usage and difficulty in maintaining context and progress. This Skill provides a structured, token-efficient workflow to manage multi-phase projects, reducing costs and improving clarity.

Core Features & Use Cases

  • 4-Phase Workflow: Guides you systematically through Analysis, Plan, Implementation, and Review for any development task, ensuring thoroughness and reducing oversight.
  • Token Efficiency: Reduces token usage by 70-80% through hidden-state reasoning and sending only context deltas, significantly lowering operational costs.
  • Structured Output: Provides clear, human-readable progress updates in your editor, making it easy to follow the AI's reasoning and actions.
  • Integrated Validation: Automatically runs guard_validate and testing_run_affected after each patch, ensuring code quality and functionality throughout the implementation phase.
  • Use Case: Imagine fixing a complex bug that spans multiple files. Use Latent Chain Mode to systematically analyze the problem, plan your patches, implement them with integrated validation, and review the final changes, all while minimizing token costs and ensuring a robust solution.

Quick Start

Fix the login timeout bug using Latent Chain Mode.

Frequently Asked Questions about ccg-latent

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

FAQPage Schema
How do I reduce token usage when fixing bugs across multiple files?▼

Token usage in multi-step bug fixes drops 70-80% using latent chain reasoning, which applies hidden-state analysis and sends only context deltas instead of full files. The four-phase workflow (analysis, plan, implementation, review) maintains progress while minimizing context overhead.

What's the best way to handle code refactoring that spans multiple files?▼

Structured refactoring across multiple files uses a systematic four-phase approach: analyze dependencies, plan patches, implement with integrated validation (guard_validate and testing_run after each patch), then review. This ensures thoroughness while reducing token costs.

How do I automate code review and validation for complex changes?▼

Automated validation runs guard_validate and testing_run_affected after each patch application, catching quality issues immediately. The workflow produces human- and machine-readable output tracking each phase, making review progress transparent and reducing manual oversight.

Can I use latent reasoning to manage feature implementations across codebases?▼

Yes. Latent chain reasoning applies to feature implementations by structuring them through analysis, planning, patched implementation with validation, and review phases. This approach maintains context efficiency for multi-file feature work while providing delta-context output.

When should I use structured multi-phase workflows instead of ad-hoc development?▼

Use structured workflows for bug fixes, feature implementations, refactors, and code reviews spanning multiple files. Ad-hoc approaches accumulate token costs and lose progress context; systematic phases prevent oversight and reduce operational costs by 70-80%.

What output formats does the workflow provide for tracking progress?▼

The workflow delivers both human-readable progress updates in your editor and machine-readable outputs, enabling clear visibility into reasoning and actions across all four phases while supporting integration with downstream tools.

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