What is the Prompt150 formula?

Prompt150 is a structured framework consisting of Context150 (core facts + supporting details), a specific Query, a Method of approach, and a defined Output Style to maximize external AI response quality.

Does this skill send data to external AI automatically?

No. The protocol includes a mandatory User Approval step where you must review the justification, the target model, and the prompt preview before any external request is executed.

When should I use the Ask-AI 150 skill?

Use this skill when internal confidence in an answer is below 75%, when local searches (grep/find) yield no results, or when you need real-world precedents and current data from outside the workspace.

How does Ask-AI 150 handle unreliable AI responses?

The skill includes a 'Response Validation' step that assesses source credibility, verifies claims against known facts, and separates assumptions from confirmed data before integrating it into your workflow.

Ask-AI 150 Protocol

Name: Ask-AI 150 Protocol
Author: MykhailoDmytriakha

byMykhailoDmytriakha

生产力与工作流

Consults external AI models using a structured prompting formula when internal project knowledge and local sources are exhausted.

关于

Ask-AI 150 is a sophisticated framework designed to bridge the gap between local project data and external world knowledge. By implementing the proprietary 'Prompt150' formula—which combines high-density context, specific queries, rigorous methodology, and structured styling—this skill ensures that any external AI consultation is high-quality and verifiable. It is particularly useful for developers who need real-world precedents, current industry benchmarks, or complex reasoning that exceeds the information available in the local repository, all while maintaining a strict validation loop to prevent hallucinations and ensure data integrity.

主要功能

Response Validation Protocol: Rigorously checks external claims for credibility and confidence levels.
0 GitHub stars
Prompt150 Formula: Builds high-context prompts (Context + Query + Method + Style) for superior results.
Intelligent Model Selection: Recommends specific models like GPT-4, Perplexity, or specialized Claude versions based on the task.
Confidence Tracking: Monitors the improvement in certainty from internal search to external integration.
Internal Exhaustion Verification: Ensures local searches are completed before invoking external models.

使用场景

Finding real-world legal or technical precedents when internal documentation is insufficient.
Accessing current statistical data or industry benchmarks released after the internal model's knowledge cutoff.
Executing complex scenario modeling or decision-tree reasoning using specialized external 'thinking' models.

关于

主要功能

Response Validation Protocol: Rigorously checks external claims for credibility and confidence levels.
0 GitHub stars
Prompt150 Formula: Builds high-context prompts (Context + Query + Method + Style) for superior results.
Intelligent Model Selection: Recommends specific models like GPT-4, Perplexity, or specialized Claude versions based on the task.
Confidence Tracking: Monitors the improvement in certainty from internal search to external integration.
Internal Exhaustion Verification: Ensures local searches are completed before invoking external models.

使用场景

Finding real-world legal or technical precedents when internal documentation is insufficient.
Accessing current statistical data or industry benchmarks released after the internal model's knowledge cutoff.
Executing complex scenario modeling or decision-tree reasoning using specialized external 'thinking' models.