pua-en

Enforce exhaustive problem-solving and end-to-end delivery with evidence verification.

Updated Apr 3, 2026

Search Tags:#automation #verification #ownership #end-to-end #proactivity #dive-deep

Ready to run

npx skills add https://github.com/handsomelong922/my-codex-skills --skill pua-en-handsomelong922

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pua-en
Source: https://github.com/handsomelong922/my-codex-skills/tree/main/skills/pua-en
Command: npx skills add https://github.com/handsomelong922/my-codex-skills --skill pua-en-handsomelong922

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

AI assistants and teams often stall when tasks fail or stall, leading to partial fixes and repeated handoffs. This skill enforces a disciplined mindset that requires exhaustive option exploration, proactive action, and ownership to deliver complete results end-to-end.

Core Features & Use Cases

Enforces Non-Negotiables: exhaustive problem solving, action before asking, and taking initiative.
Structured failure handling: follows a 5-step methodology and 7-item checklist to diagnose, verify, and complete tasks.
End-to-end verification: requires evidence (logs, outputs, tests) before declaring completion across code, data analysis, research, or writing tasks.

Quick Start

Describe a task and instruct the AI to apply the three non-negotiables plus the Dive Deep steps to complete it end-to-end.

Frequently Asked Questions about pua-en

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I enforce proactive problem-solving and end-to-end verification in AI agents?▼

End-to-end verification enforces AI agents to gather evidence like logs and tests before completion. It applies structured failure handling through a five-step methodology and seven-item checklist to ensure proactive ownership across code and data tasks.

Why does my AI assistant stall and provide partial fixes when code automation tasks fail?▼

AI assistants stall on failures without structured handling, causing partial fixes. Applying exhaustive problem-solving non-negotiables forces the agent to explore all options, take proactive action, and complete the task end-to-end instead of handing it back.

What is the best way to automate exhaustive failure handling for data analysis and research tasks?▼

Automate exhaustive failure handling by applying a five-step methodology and seven-item checklist. This mechanism requires the AI to diagnose issues, verify outputs automatically, and gather evidence before declaring any research or data analysis task complete.

Does this proactive AI problem-solving skill work for writing and research verification?▼

Yes, proactive problem-solving applies to writing and research verification. It requires the AI to take initiative, handle failures using structured methodologies, and provide end-to-end evidence of completion across code, data analysis, research, and writing tasks.

How to stop AI agents from asking for help before exhausting all possible solutions?▼

To stop premature help requests, enforce the action-before-asking non-negotiable. This requires the AI agent to take proactive initiative and exhaustively explore all available options for code or analysis tasks before requesting human intervention.

What are the limitations of using structured checklists for AI task completion verification?▼

Structured checklists require the AI to gather specific evidence like logs and outputs before claiming completion. While effective for code and data tasks, this verification process demands strict adherence to the seven-item checklist, ensuring no partial results are delivered.