AgentsAvançado

Thought-Based Reasoning Techniques

porneolabhq·neolabhq· v1.0.0 · atualizado em 2026-04-10

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Use this skill for complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, or symbolic manipulation where simple prompting is insufficient. It provides a comprehensive guide to Chain-of-Thought and related prompting techniques (Zero-shot CoT, Self-Consistency, Tree of Thoughts, Least-to-Most, ReAct, PAL, Reflexion), including templates, decision matrices, and research-backed patterns.

prompt-engineeringchain-of-thoughtreasoningllmlarge-language-modelszero-shot-cottree-of-thoughts

Linguagens

TypeScriptJavaScriptPythonJavaC#

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Documento do Skill

SKILL.mdcustomaize-agent-thought-based-reasoning/workflow

Identify Task — : Determine the type of reasoning task (e.g., arithmetic, commonsense, symbolic).

Select Technique — : Choose the appropriate prompting technique (e.g., Zero-shot CoT, Tree of Thoughts, ReAct) based on task complexity and available resources.

Craft Prompt — : Create a prompt using the selected technique, including relevant examples or instructions.

Generate Reasoning — : Use the LLM to generate intermediate reasoning steps.

Extract Answer — : Extract the final answer from the generated reasoning.

Evaluate — : Evaluate the accuracy and consistency of the reasoning and answer.

Refine — : Refine the prompt or technique based on the evaluation results.

Iterate — : Repeat steps 4-7 until satisfactory results are achieved.

Telemetria de Agentes

Execuções

total

Taxa de Sucesso

últimos 30d

Latência Média

0.0s

p50

Alucinação

0.0%

detecção

Tokens Entrada

avg 0/exec

Tokens Saída

avg 0/exec

Uso por Plataforma

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Árvore do Skill

Thought-Based Reasoning Techniques

customaize-agent-thought-based-reasoning

Fases Cognitivas6

1.SENSE

2.CONTEXTUALIZE

3.HYPOTHESIZE

4.ACT

5.EVALUATE

6.REFLECT

Triggers10

improve reasoning with chain of thoughtuse zero-shot chain of thoughtimplement tree of thoughtsapply self-consistency to reasoninguse least-to-most promptingimplement react for reasoning and actingapply reflexion for iterative improvementimprove llm reasoninguse program-aided language modelsimplement chain of thought prompting

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Score Breakdown

⭐Avaliação Humana0%

🤖Sucesso de Agentes0%

🕐Atualidade100%

🔗Saúde de Dependências100%

🕸️Centralidade no Grafo0%

🛡️Segurança50%

CompositeScore = α·Humano + β·Agente + γ·Recência + δ·Deps + ε·Centralidade + ζ·Segurança

Instalação

$ synaptic mcp download customaize-agent-thought-based-reasoning

$ synaptic skills detail customaize-agent-thought-based-reasoning

$ synaptic skills live customaize-agent-thought-based-reasoning

Dependências

Python

Links

GitHub Repository