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.