Agent Skills: 🪄 Skill: Prompt Pro (v1.1.0)

🪄 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:

• Reasoning Models (o3): Objective-based prompting.
• Tree-of-Thoughts: Exploring multiple logic branches.
• ReAct Patterns: Reasoning and acting in unison.
• Thinking Protocols: Designing cognitive behavior.

Updated: January 22, 2026 - 21:00