How does the skill handle conversation context?

It organizes the original goal, a timeline of all failed attempts, concrete error messages, and current system constraints into a structured format for GPT-5 to analyze.

Does this skill require specific setup?

Yes, it requires the gpt5-mcp-server to be installed and running locally, providing access to tools like gpt5_generate and gpt5_messages.

Can I use this for simple documentation questions?

It is not recommended for simple questions easily answered by documentation; it is best reserved for complex issues where standard approaches have failed.

What is 'Reasoning Effort' in the context of this skill?

Reasoning effort is a setting that determines the depth of analysis. Use 'high' for novel architecture or complex logic, and 'medium' for standard debugging or implementation details.

When is the best time to use the GPT-5 Consultant skill?

You should activate this skill when you've tried multiple debugging approaches without success, when you're stuck in a loop of failing solutions, or when a user expresses frustration with progress.

GPT-5 Technical Consultant

Name: GPT-5 Technical Consultant
Author: Activer007

byActiver007

Herramientas para Desarrolladores

Breaks through complex technical impasses and circular debugging by leveraging GPT-5's advanced analytical reasoning.

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The GPT-5 Consultant skill is a specialized framework designed to rescue developers from circular debugging cycles and high-stakes architectural roadblocks. When standard troubleshooting fails or logic becomes too tangled for standard LLM interactions, this skill gathers comprehensive conversation context—including failed attempts, stack traces, and system constraints—and presents it to a GPT-5 instance via an MCP server. It provides a fresh, high-reasoning perspective on obscure edge cases, novel technical challenges, and multi-system architectural decisions, serving as a third-party expert to help break through technical deadlocks.

Características Principales

Adjustable reasoning effort levels (low, medium, high) for complex tasks
0 GitHub stars
Comprehensive context synthesis from full conversation history
Structured synthesis of architectural recommendations and risk assessments
Automated circular debugging detection and impasse intervention
Multi-turn consultant conversations for iterative problem solving

Casos de Uso

Debugging complex TypeScript type inference issues in deeply nested generic systems
Resolving persistent unhandled promise rejections or errors that bypass standard try-catch blocks
Making high-stakes architectural decisions between competing paradigms like Microservices vs. Monoliths

Acerca de

Características Principales

Adjustable reasoning effort levels (low, medium, high) for complex tasks
0 GitHub stars
Comprehensive context synthesis from full conversation history
Structured synthesis of architectural recommendations and risk assessments
Automated circular debugging detection and impasse intervention
Multi-turn consultant conversations for iterative problem solving

Casos de Uso

Debugging complex TypeScript type inference issues in deeply nested generic systems
Resolving persistent unhandled promise rejections or errors that bypass standard try-catch blocks
Making high-stakes architectural decisions between competing paradigms like Microservices vs. Monoliths