Agent skill · neolabhq
thought-based-reasoning
Use when tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where simple prompting fails - provides comprehensive guide to Chain-of-Thought and related prompting techniques (Zero-shot CoT, Self-Consistency, Tree of Thoughts, Least-to-Most, ReAct, PAL, Reflexion) with templates, decision matrices, and research-backed patterns
What it needs
About 12k tokens when loaded.
What this skill does
Thought-Based Reasoning Techniques for LLMs Overview Chain-of-Thought (CoT) prompting and its variants encourage LLMs to generate intermediate reasoning steps before arriving at a final answer, significantly improving performance on complex reasoning tasks. These techniques transform how models approach problems by making implicit reasoning explicit. Quick Reference Technique When to Use Complexity Accuracy Gain ----------- ------------- ------------ --------------- Zero-shot CoT Quick reasoning, no examples available Low +20-60% Few-shot CoT Have good examples, consistent format needed Medium +30-70% Self-Consistency High-stakes decisions, need confidence Medium +10-20% over CoT Tree of Thoughts Complex problems requiring exploration High +50-70% on hard tasks Least-to-Most Multi-step problems with subproblems Medium +30-80% ReAct Tasks requiring external information Medium +15-35% PAL Mathematical/computational problems Medium +10-15% Reflexion Iterative improvement, learning from errors High +10-20% --- Core Techniques 1. Chain-of-Thought (CoT) Prompting Paper: "Chain of Thought Prompting Elicits Reasoning in Large Language Models" (Wei et al., 2022) Citations: 14,255+ When to Use Multi-step arithmetic or math word problems Commonsense reasoning requiring logical deduction Symbolic reasoning tasks When you have good exemplars showing reasoning How It Works Provide few-shot examples that include intermediate reasoning steps, not just question-answer pairs. The model learns to generate similar step-by-step reasoning. Prompt Template Strengths Significant accuracy improvements on reasoning tasks Interpretable intermediate steps Works well with large models (>100B parameters) Limitations Requires crafting good exemplars Less effective on smaller models Can still make calculation errors --- 2. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills neolabhq/thought-based-reasoning--cb2bd5