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full-output-enforcement

Override LLM truncation to generate complete code and documentation without placeholders.

12|3|Updated Jun 17, 2026
Search Tags:#workflow#quality assurance#productivity#code generation#llm optimization
One-click install
npx skills add https://github.com/phuhao00/bony-agent --skill full-output-enforcement-phuhao00
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: full-output-enforcement
Source: https://github.com/phuhao00/bony-agent/tree/main/.agent/skills/full-output-enforcement
Command: npx skills add https://github.com/phuhao00/bony-agent --skill full-output-enforcement-phuhao00

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill prevents LLMs from truncating code, using placeholders like // ... or // TODO, or providing partial summaries when the user requires a full, production-ready implementation.

Core Features & Use Cases

  • Strict Completeness: Forces the model to generate every line of code and every requested component without skipping sections.
  • Pattern Banning: Actively suppresses common lazy-coding habits and conversational filler that degrades output quality.
  • Use Case: When generating a complex multi-file project, this Skill ensures the AI provides the full content of every file instead of stopping halfway or suggesting the user fill in the blanks.

Quick Start

Activate the full-output-enforcement skill to ensure the next code generation task is delivered in its entirety without any omitted sections 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 LLMs from truncating code and using placeholders like // TODO during code generation?▼

To stop LLMs from truncating code and using placeholders, enforce strict completeness protocols that override default token-limit behaviors and ban lazy-coding patterns for unabridged outputs.

Why does my LLM provide partial summaries instead of full multi-file project generation?▼

LLMs provide partial summaries instead of full multi-file project generation due to default truncation behaviors, requiring explicit non-omission protocols to force exhaustive code generation.

How can I get unabridged AI responses for complex coding tasks and exhaustive documentation?▼

You can get unabridged AI responses for complex coding tasks by activating non-omission enforcement, which requires the model to generate every line of code without stopping halfway.

What is the best way to ensure complete, production-ready code generation without omitted sections?▼

The best way to ensure complete, production-ready code generation is to apply pattern banning that actively suppresses conversational filler and forces the delivery of every requested component.

Does full-output-enforcement work for multi-file project generation when hitting token-limit breakpoints?▼

Full-output-enforcement works for multi-file project generation by applying explicit handling of token-limit breakpoints, ensuring the AI provides the full content of every file.

When should I not use strict completeness protocols for LLM output generation?▼

You should not use strict completeness protocols when conversational summaries are preferred over full implementations, as pattern banning actively suppresses partial summarization.

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