full-output-enforcement
Enforce Complete, Unabridged Output Every Time
LLMs often truncate or summarize code instead of delivering complete files. This skill enforces exhaustive output by banning placeholder patterns and managing token-limit splits cleanly.
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Using "full-output-enforcement". Write a user authentication module with registration, login, and password reset
Expected outcome:
Full implementation with all three features: registration function, login handler, password reset flow. No TODO comments. No 'implement similar pattern for X'. Complete code for each function.
Using "full-output-enforcement". Create 5 API endpoint handlers for a todo app
Expected outcome:
All 5 handlers fully implemented: create todo, get todos, get single todo, update todo, delete todo. Each handler includes input validation, error handling, and response formatting. No placeholder logic.
Using "full-output-enforcement". Generate a complete React component library with 10 components
Expected outcome:
All 10 components fully written: Button, Input, Modal, Card, Alert, Dropdown, Checkbox, Radio, Select, Textarea. Each with props interface, state management, event handlers, and styling. If interrupted at component 6, continuation marker shows [PAUSED — 6 of 10 complete. Send 'continue' to resume].
Security Audit
SafeStatic analysis flagged external_commands and weak_crypto patterns, but all are false positives. Line 16 contains Markdown code formatting backticks for banned patterns, not shell execution. Line 20 uses 'skeleton' to describe placeholder code, not cryptography. No executable code exists in this skill. The file is a text-based prompt instruction set with zero security risk.
Quality Score
What You Can Build
Get Complete Production Code
Request a full implementation of a feature, API, or module. The skill ensures no placeholder comments or skeleton code appears in the output.
Generate All Items in a List
Ask for 10 test cases, 5 components, or any specific number. The skill tracks count and ensures every requested item appears in the output.
Handle Long Outputs Seamlessly
For lengthy codebases or documentation, the skill manages continuation cleanly with markers so no content is lost or duplicated.
Try These Prompts
Write a complete [module name] with [specific requirements]. Apply the full-output-enforcement skill.
Create [number] of [item type]. Make sure every single one is fully written out with no shortcuts.
Generate the full [large feature] in its entirety. If you hit a limit, mark where you stopped and I will continue.
Check that the output contains all [specific items] requested. Flag anything missing before finalizing.
Best Practices
- Always specify the exact number of deliverables expected in your request
- Review the banned pattern list to understand what the skill prevents
- Use continuation markers when working with very large codebases
Avoid
- Requesting 'a typical example' instead of a specific implementation
- Accepting partial output when you requested a complete solution
- Using vague language that allows the LLM to summarize instead of detail