full-output-enforcement

Enforce complete model outputs by rejecting placeholders and splitting at token limits.

Updated May 5, 2026

Search Tags:#code generation #token limits #llm output #quality enforcement #resume workflow #no placeholders

Ready to run

npx skills add https://github.com/XCmiaow/cc-deploy --skill full-output-enforcement-xcmiaow

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: full-output-enforcement
Source: https://github.com/XCmiaow/cc-deploy/tree/main/skills/full-output-enforcement
Command: npx skills add https://github.com/XCmiaow/cc-deploy --skill full-output-enforcement-xcmiaow

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It prevents truncated or placeholder-based responses when you need the full, final result—especially for complete files, full implementations, and exhaustive multi-part answers.

Core Features & Use Cases

Complete-output enforcement: Treats partial responses as failures and requires finishing every requested deliverable end-to-end.
Placeholder and skeleton prevention: Blocks common “continued/rest/TODO/…” patterns and structural shortcuts that reduce quality.
Token-limit safe splitting: When nearing the output limit, writes up to a clean breakpoint and pauses with a precise resume location.

Quick Start

Use it when you want a fully finished answer by asking your AI to generate the complete full implementation without omissions or placeholders.

Frequently Asked Questions about full-output-enforcement

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

FAQPage Schema

How do I stop AI code generation from using placeholders or truncating long source files?▼

To stop AI code generation from truncating, enforce complete outputs by overriding truncation behavior and rejecting placeholder patterns. This approach treats partial responses as failures and requires finishing every requested deliverable end-to-end without structural shortcuts.

Why does my LLM output get cut off when generating multi-component implementations?▼

Your LLM output gets cut off because it hits token limits during multi-component implementations. To resolve this, the generation workflow writes up to a clean breakpoint, pauses with a precise resume location, and performs a pre-send cross-check to ensure unabridged content.

What is the best way to guarantee exhaustive deliverables in long structured responses?▼

The best way to guarantee exhaustive deliverables in long structured responses is to apply quality enforcement by counting deliverables, generating all content fully, and performing a pre-send cross-check. This prevents skeleton shortcuts and ensures unabridged model outputs.

Can I safely resume code generation after reaching a token limit?▼

Yes, you can safely resume code generation after reaching a token limit by using a token-limit safe splitting mechanism. It writes up to a clean breakpoint, pauses with a precise resume location, and continues generating the remaining unabridged content safely.

How do I block common continued or TODO patterns during AI code generation?▼

To block common continued, TODO, or rest patterns during AI code generation, you can apply placeholder and skeleton prevention logic. This mechanism rejects structural shortcuts, ensuring the model finishes the complete full implementation without omissions.