prompt-chain-of-thought

name	prompt-chain-of-thought
kind	prompt
description	A battle-tested chain-of-thought prompt template that guides LLMs through multi-step reasoning. Supports zero-shot CoT, few-shot CoT, and Tree-of-Thought variants. Reuses the OpenAI prompt engineering and Anthropic prompt library standards.
tags	["prompt","chain-of-thought","reasoning","llm","openai","anthropic"]
version	0.1.0
author	agentverse
license	MIT
metadata	{"openclaw":{"homepage":"https://github.com/loonghao/agentverse","emoji":"💭"}}

Prompt: Chain-of-Thought Reasoning

A prompt artifact stores optimized prompt templates — reusable building blocks for LLM-based reasoning, instructions, and structured outputs.

What is a Prompt?

Field	Description
system	The system-level instruction injected before the dialogue
user	Template for the user turn (supports {{variables}})
examples	Few-shot examples (input/output pairs)
output_format	Expected output schema (JSON, Markdown, plain text, etc.)
model_hints	Preferred models or parameter settings

Prompt Manifest (prompt.toml)

[package]
kind        = "prompt"
namespace   = "agentverse"
name        = "chain-of-thought"
description = "Multi-step reasoning prompt — zero-shot, few-shot, and Tree-of-Thought variants"

[prompt]
template_engine = "jinja2"      # jinja2 | handlebars | mustache | plain
input_variables = ["problem", "domain", "style"]

[prompt.system]
text = """
You are an expert problem solver. When given a problem, break it down into clear,
numbered reasoning steps before arriving at a final answer.
Think step by step. Show your work.
"""

[prompt.user]
text = """
Problem: {{problem}}
Domain: {{domain}}
Reasoning style: {{style | default("zero-shot-cot")}}

Let's work through this step by step:
"""

[[prompt.examples]]
input  = { problem = "Is 37 a prime number?", domain = "mathematics" }
output = """
Step 1: Check divisibility by 2. 37 is odd, so not divisible by 2.
Step 2: Check divisibility by 3. 3+7=10, not divisible by 3.
Step 3: Check divisibility by 5. Doesn't end in 0 or 5.
Step 4: Check up to √37 ≈ 6.1. Check 7? 7 > 6.1, stop.
Answer: 37 is prime. ✓
"""

[prompt.output_format]
type   = "markdown"
schema = "numbered-steps + final-answer"

[prompt.model_hints]
preferred    = ["gpt-4o", "claude-3-5-sonnet", "gemini-1.5-pro"]
temperature  = 0.2
   = 


     = [, , , , ]
 = 
  = 

AgentVerse Content (content.json)

{
  "schema_version": "1.0",
  "kind": "prompt",
  "template_engine": "jinja2",
  "input_variables": ["problem", "domain", "style"],
  "system": "You are an expert problem solver. Break down problems into clear numbered reasoning steps before your final answer. Think step by step. Show your work.",
  "user": "Problem: {{problem}}\nDomain: {{domain}}\nReasoning style: {{style|default('zero-shot-cot')}}\n\nLet's work through this step by step:",
  "examples": [
    {
      "input": { "problem": "Is 37 a prime number?", "domain": "mathematics" },
      "output": "Step 1: 37 is odd → not divisible by 2.\nStep 2: 3+7=10 → not divisible by 3.\nStep 3: Doesn't end 0/5 → not divisible by 5.\nStep 4: √37≈6.1, checked all primes ≤6.\nAnswer: 37 is prime. ✓"
    
  
        
         

Usage

Publish

agentverse publish --file prompt.toml

Retrieve and use in code

agentverse get --kind prompt --namespace agentverse --name chain-of-thought

import json, subprocess

result = subprocess.check_output([
    "agentverse", "get",
    "--kind", "prompt",
    "--namespace", "agentverse",
    "--name", "chain-of-thought",
    "--format", "json"
])
prompt = json.loads(result)

# Render template
from jinja2 import Template
user_msg = Template(prompt["content"]["user"]).render(
    problem="Explain why the sky is blue",
    domain="physics"
)

Variants

Style	Description
zero-shot-cot	"Let's think step by step" suffix only
few-shot-cot	Include worked examples before the question
tot	Tree-of-Thought: explore multiple paths
self-ask	Agent asks and answers sub-questions itself

Standards Compatibility

Standard	Compatible?	Notes
OpenAI Chat format	✅	Maps system/user to chat roles
Anthropic messages API	✅	Same role mapping
LangChain PromptTemplate	✅	input_variables aligns exactly
LlamaIndex	✅	Can be loaded as PromptTemplate
DSPy	✅	Use as dspy.ChainOfThought signature

Notes

• Variables use {{double-brace}} Jinja2 syntax by default.
• Use --format json with the CLI to get machine-readable output.
• Combine with a soul artifact to add persona on top of reasoning style.