full-output-enforcement

Enforces complete code generation by banning placeholder patterns and managing token-limit splits.

1|Updated Jun 3, 2026
One-click install
npx skills add https://github.com/tanveerriaz/Skillz --skill full-output-enforcement-tanveerriaz
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: full-output-enforcement
Source: https://github.com/tanveerriaz/Skillz/tree/main/skills/full-output-enforcement
Command: npx skills add https://github.com/tanveerriaz/Skillz --skill full-output-enforcement-tanveerriaz

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? LLMs often truncate long outputs, insert placeholder comments like "// rest of code", or skip deliverables when generating multi-file or multi-part responses. This Skill overrides that default truncation behavior so every requested artifact is delivered in full. ## Core Features & Use Cases - Placeholder Ban: Hard-fails on patterns like "// ...", "// TODO", "for brevity", and skeleton-only outputs, forcing complete implementations. - Scope Locking & Cross-Check: Counts expected deliverables before generating, then verifies the count before responding so nothing is silently dropped. - Clean Continuation Protocol: When output hits token limits, it pauses at a clean boundary with a resumable marker and continues exactly where it stopped on "continue". - Use Case: Ask for five complete React components for a dashboard. Instead of receiving three files plus "repeat the pattern for the rest", you get all five fully implemented files, or a clean pause-and-resume split if the output exceeds one response. ## Quick Start Ask the agent to generate the complete implementation of all requested files with no placeholders, abbreviations, 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 AI from truncating code output?▼

Apply explicit anti-truncation rules that ban placeholder patterns like "// rest of code" and "for brevity", lock the expected deliverable count before generation, and cross-check the output against the original request before responding.

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

Split output at a clean boundary such as the end of a function or file, then emit a pause marker stating progress and the next section. On "continue", resume exactly from that point without recapping or repeating content.

What placeholder patterns indicate incomplete AI-generated code?▼

Common signs include "// ...", "// TODO", "// implement here", "// similar to above", bare ellipses replacing code, and prose like "the rest follows the same pattern" or "let me know if you want me to continue".

Does forcing complete output override safety restrictions?▼

No. Completeness rules do not authorize exposing secrets, reproducing copyrighted bulk content, performing destructive actions, or doing work outside the user's requested scope. Safety boundaries take priority over exhaustive output.

When should I not use full-output enforcement?▼

Avoid it for exploratory questions, quick prototypes, or brainstorming where brevity is preferred. It is designed for tasks where partial output is unusable, such as production files or complete multi-part deliverables.