full-output-enforcement

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

Updated Oct 7, 2022
One-click install
npx skills add https://github.com/tamagusko/linux-cfg --skill full-output-enforcement-tamagusko
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: full-output-enforcement
Source: https://github.com/tamagusko/linux-cfg/tree/main/dotfiles/claude/skills/full-output-enforcement
Command: npx skills add https://github.com/tamagusko/linux-cfg --skill full-output-enforcement-tamagusko

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 - Placeholder Ban: Hard-fails on patterns like // ..., // TODO, "for brevity", and "the rest follows the same pattern" in code and prose. - Scope Locking: Counts the requested deliverables before generating and cross-checks the output against that count before responding. - Token-Limit Handling: Pauses at clean breakpoints with a resumable [PAUSED — X of Y complete] 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 one of them fully implemented with no skeletons or omissions. ## Quick Start Activate this skill and ask the assistant to generate the complete implementation of all requested files with no placeholders or omitted sections.

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 to be generated in full. This skill enforces that behavior with a scope-count and cross-check process before responding.

How to handle LLM responses that hit the token limit?

Instead of compressing remaining content, pause at a clean breakpoint such as the end of a function or file, emit a resumable marker like "[PAUSED — X of Y complete]", and continue exactly from that point when the user replies "continue".

What placeholder patterns should be banned in AI code generation?

Ban patterns like "// ...", "// TODO", "// implement here", "// similar to above", and prose such as "for brevity" or "the rest follows the same pattern". These indicate omitted content and produce non-runnable code.

Does this skill work with any LLM or coding assistant?

Yes, it is a prompt-level instruction set with no code dependencies, so it applies to any LLM that accepts system or skill instructions. It changes generation behavior rather than integrating with a specific API.

When should I not enforce full-output generation?

Avoid it for exploratory brainstorming, quick prototypes, or when the user explicitly asks for a sketch or outline. Enforcing exhaustive output in those cases wastes tokens and slows iteration.