full-output-enforcement

Enforce complete, untruncated generation of code and content.

1|Updated Oct 27, 2025
One-click install
npx skills add https://github.com/sandrasocial/sselfie-9g --skill full-output-enforcement-sandrasocial
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: full-output-enforcement
Source: https://github.com/sandrasocial/sselfie-9g/tree/main/.agents/skills/full-output-enforcement
Command: npx skills add https://github.com/sandrasocial/sselfie-9g --skill full-output-enforcement-sandrasocial

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It guarantees that large‑language‑model responses are complete, eliminating truncated or placeholder content that breaks downstream workflows.

Core Features & Use Cases

  • Scope Locking: Counts expected deliverables and ensures each is fully produced.
  • Banned Pattern Enforcement: Detects and forbids placeholder comments or omitted sections.
  • Token‑Limit Handling: Provides clean split messages with pause markers instead of cutting off content.
  • Use Cases: Ideal for generating full source files, multi‑component codebases, lengthy documentation, or any task where missing pieces cause failures.

Quick Start

Ask the AI to apply full-output enforcement for a task requiring the entire code or content without any truncation.

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 LLM code generation from truncating or using placeholder comments?

To stop LLM code generation from truncating, you can apply strict output enforcement that bans placeholder comments, scope-locks expected deliverables, and cross-checks that all content is fully produced.

What is the best way to generate full source files without missing components?

The best way to generate full source files is to use scope locking, which counts expected deliverables upfront and cross-checks each component to guarantee no sections are omitted during LLM generation.

How does token-limit handling work for long LLM outputs?

Token-limit handling for long LLM outputs works by providing clean split messages with pause markers, allowing content generation to continue seamlessly instead of cutting off abruptly.

Can I enforce complete output for multi-component codebases and lengthy documentation?

Yes, you can enforce complete output for multi-component codebases and lengthy documentation by implementing strict omission checks and scoped counting across all requested deliverables.

Why does my LLM output omit sections when generating extensive explanations?

LLM output omits sections during extensive explanations because it hits token limits or defaults to summarization, requiring strict enforcement rules that detect and forbid omitted sections.