YuniorGlez

prompt-pro

3
3
# Install this skill:
npx skills add YuniorGlez/gemini-elite-core --skill "prompt-pro"

Install specific skill from multi-skill repository

# Description

Senior Prompt Engineer & Agentic Orchestrator. Expert in Reasoning Models (o3), Tree-of-Thoughts, and Structured Thinking Protocols for 2026.

# SKILL.md


name: prompt-pro
id: prompt-pro
version: 1.1.0
description: "Senior Prompt Engineer & Agentic Orchestrator. Expert in Reasoning Models (o3), Tree-of-Thoughts, and Structured Thinking Protocols for 2026."


🪄 Skill: Prompt Pro (v1.1.0)

Executive Summary

The prompt-pro is the master of the "Linguistic Core." In 2026, prompting has evolved from simple text instructions to Architectural Orchestration. This skill focuses on optimizing for Reasoning Models (o3, Gemini 3 Pro), implementing advanced logic frameworks like Tree-of-Thoughts, and building autonomous ReAct loops that allow agents to act and reason in unison. We don't just "talk" to AI; we design its cognitive behavior.


📋 Table of Contents

  1. Core Prompting Philosophies
  2. The "Do Not" List (Anti-Patterns)
  3. Optimizing for Reasoning Models (o3)
  4. Tree-of-Thoughts (ToT) Framework
  5. ReAct: Autonomous Loops
  6. Structured Thinking Protocols
  7. Reference Library

🏛️ Core Prompting Philosophies

  1. Intent is Deterministic: If the prompt is ambiguous, the result is hallucinated. Use rigid structures.
  2. Objective over Instruction: Tell the model "What" to achieve, not just "How" to do it.
  3. Few-Shot is the King: One perfect example is worth a hundred rules.
  4. Feedback Loops are Built-in: Design prompts that ask the model to critique its own output.
  5. Token Economy: Be concise. Every extra token is latency and cost.

🚫 The "Do Not" List (Anti-Patterns)

Anti-Pattern Why it fails in 2026 Modern Alternative
Instruction Overload Model loses track of priorities. Use Hierarchical Rules.
Fixed Step-by-Step Limits the model's reasoning power. Use Objective-Based Prompts.
Ignoring Reasoning Tokens Results in shallow, rushed answers. Increase maxOutputTokens.
Implicit Assumptions Leads to "Vibe Hallucinations." State Assumptions Explicitly.
Manual Parsing Inefficient and fragile. Use ResponseSchema (JSON).

🧠 Optimizing for Reasoning Models (o3/Pro)

We leverage the model's internal "Thought Layer":
- Deep Research Triggers: Commanding exhaustive source searches.
- Verification Loops: Asking the model to find flaws in its own strategy.
- Self-Correction: Enabling autonomous backtracking if a plan fails.

See References: Reasoning Optimization for details.


🌳 Tree-of-Thoughts (ToT) Framework

  • Parallel Generation: Proposing 3+ independent strategies.
  • Elimination Strategy: Removing the weakest branch via logic.
  • Final Synthesis: Merging the best elements of all branches.

📖 Reference Library

Detailed deep-dives into Prompt Engineering Excellence:


Updated: January 22, 2026 - 21:00

# Supported AI Coding Agents

This skill is compatible with the SKILL.md standard and works with all major AI coding agents:

Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.