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full-output-enforcement

Enforces complete code generation by banning placeholder patterns and truncation shortcuts.

Updated Jul 14, 2026
Search Tags:#code generation#llm behavior#output enforcement#anti-truncation#placeholder ban#complete output
One-click install
npx skills add https://github.com/quanthubbr/tron-claude-config --skill full-output-enforcement-quanthubbr
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: full-output-enforcement
Source: https://github.com/quanthubbr/tron-claude-config/tree/main/managed/skills/leonxlnx/full-output-enforcement
Command: npx skills add https://github.com/quanthubbr/tron-claude-config --skill full-output-enforcement-quanthubbr

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? LLMs often truncate long outputs, insert placeholder comments like "// rest of code", or ask "want me to continue?" instead of delivering complete work. This Skill overrides that default behavior so every requested deliverable is generated in full. ## Core Features & Use Cases - Placeholder Ban: Hard-fails on patterns like // ..., // TODO, "for brevity", and skeleton-only implementations. - Scope Locking: Counts expected deliverables before generating and cross-checks completeness before responding. - Token-Limit Handling: Pauses at clean breakpoints with a resumable marker instead of compressing or skipping content. - Use Case: When asking an AI to generate five full React components or a complete configuration file, this Skill ensures all five arrive fully implemented with runnable code, not abbreviated stubs. ## Quick Start Apply the full-output-enforcement skill and generate the complete implementation of all requested files with no placeholders or omissions.

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 an AI from truncating code output?▼

Use output enforcement rules that ban placeholder patterns like "// rest of code" and require the model to count deliverables before generating. The model cross-checks its output against the original request and completes anything missing before responding.

How to handle token limits when generating long code files?▼

Write at full quality up to a clean breakpoint such as the end of a function or file, then end with a pause marker stating progress. On "continue", resume exactly where you stopped without recap or repetition.

What placeholder patterns should be banned in AI code generation?▼

Ban patterns like "// ...", "// TODO", "// implement here", "for brevity", and "the rest follows the same pattern". Also ban structural shortcuts like skeleton-only output or describing code instead of writing it.

When should I not use full-output enforcement?▼

Avoid it for exploratory questions, quick prototypes, or when you only need a code snippet illustrating one concept. Enforcing exhaustive output on small questions wastes tokens and slows down iterative discussion.

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