full-output-enforcement

Name: full-output-enforcement
Availability: InStock
Author: DawnMoon1542

Enforces complete, unabridged LLM output by banning placeholder patterns and truncation.

7|Updated May 13, 2026

Search Tags:#code generation #token limit #llm output #placeholder detection #anti-truncation #complete output

Ready to run

npx skills add https://github.com/DawnMoon1542/agents-skills --skill full-output-enforcement-dawnmoon1542

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: full-output-enforcement
Source: https://github.com/DawnMoon1542/agents-skills/tree/main/full-output-enforcement
Command: npx skills add https://github.com/DawnMoon1542/agents-skills --skill full-output-enforcement-dawnmoon1542

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? LLMs often truncate long responses, insert placeholder comments like "// rest of code", or skip deliverables to save tokens. This Skill overrides that default behavior so every requested file, function, or section is delivered in full. ## Core Features & Use Cases - Banned Pattern Enforcement: Blocks placeholder comments, ellipsis shortcuts, and prose like "for brevity" or "the rest follows the same pattern". - Scope Locking and Cross-Check: Counts expected deliverables before generating and verifies the count before responding. - Clean Token-Limit Handling: Pauses at a clean breakpoint with a resume marker instead of compressing or skipping content. - Use Case: Ask for a full multi-file project scaffold or five complete React components, and receive every item fully implemented with no skeletons or omissions. ## 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 LLM from truncating code output?▼

Use explicit anti-truncation instructions that ban placeholder patterns like "// rest of code" and require every requested deliverable in full. This Skill locks the deliverable count before generating and cross-checks it before responding.

How to handle LLM responses that hit the token limit?▼

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

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

Ban comments like "// ...", "// TODO", "// implement here", and "// similar to above", plus prose like "for brevity" and "the rest follows the same pattern". These patterns indicate omitted content that breaks runnable output.

When should I not use full-output enforcement?▼

Avoid it for exploratory questions, quick prototypes, or when a concise summary is genuinely preferred. Forcing exhaustive output on simple questions wastes tokens and slows iteration.