prompt-improver

Generate 1–3 clarifying questions before executing complex tasks.

Updated Apr 2, 2026

Search Tags:#planning #ai-safety #clarifying-questions #team-communication #prompt-improver #workflow-guardrails #ambiguity-management

Ready to run

npx skills add https://github.com/oteroweb/OWFINANCE2026 --skill prompt-improver-oteroweb

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prompt-improver
Source: https://github.com/oteroweb/OWFINANCE2026/tree/main/.agents/skills/prompt-improver
Command: npx skills add https://github.com/oteroweb/OWFINANCE2026 --skill prompt-improver-oteroweb

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Meta-Skill ensures agents don't proceed with unclear tasks by enforcing high-value clarifying questions before diving into code or plans.

Core Features & Use Cases

Clarification before action: forces 1-3 targeted questions to reduce ambiguity.
Scope definition: helps set boundaries and requirements for complex tasks.
Decision support: provides recommended defaults if the user doesn't respond within a range.

Quick Start

Ask 1–3 targeted clarifying questions before proceeding with any complex task to eliminate ambiguity.

Frequently Asked Questions about prompt-improver

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

FAQPage Schema

How do I reduce ambiguity in AI task planning before execution?▼

To reduce ambiguity in AI task planning, you need a protocol that compels agents to ask 1–3 high-value clarifying questions before proceeding with complex requests, ensuring requirements are fully specified.

What are the best ways to force AI agents to ask clarifying questions for coding tasks?▼

Forcing AI agents to ask clarifying questions involves implementing workflow guardrails that pause execution and generate targeted inquiries to set boundaries and define scope for complex coding tasks.

How do I manage underspecified requirements in AI team communication?▼

Managing underspecified requirements in AI team communication requires applying a clarification protocol that generates decision support and recommended defaults if the user remains silent.

Does this approach work for planning and design tasks across different teams?▼

Yes, this clarification protocol works for planning, design, and coding tasks across teams where requirements are often underspecified, ensuring scope definition and decision support before action.

What happens if the user does not answer the clarifying questions?▼

If the user remains silent, the protocol provides recommended defaults to guide the agent, ensuring the workflow proceeds with decision support rather than halting indefinitely or acting on assumptions.