full-output-enforcement

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

7|Updated May 13, 2026
One-click install
npx skills add https://github.com/DawnMoon1542/agents-skills --skill full-output-enforcement-dawnmoon1542
Or copy as Structured Prompt for Agent
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.