用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/vamseeachanta/workspace-hub --skill prompt-engineering-3-chain-of-thought-prompting命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
Write outbound email and external messages in Vamsee Achanta's voice — a subtle offer to help, never bold or rash claims. Load before drafting ANY email, LinkedIn/Collide reply, proposal note, or outreach sent under his name.
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正在显示 SKILL.md
基于 SOC 职业分类
| name | prompt-engineering-3-chain-of-thought-prompting |
| description | Sub-skill of prompt-engineering: 3. Chain-of-Thought Prompting. |
| version | 1.0.0 |
| category | ai-prompting |
| type | reference |
| scripts_exempt | true |
Basic Chain-of-Thought:
COT_TEMPLATE = """
Solve this problem step by step.
Problem: {problem}
Let me think through this carefully:
Step 1: First, I'll identify the key information...
Step 2: Next, I'll determine the approach...
Step 3: Then, I'll perform the calculations...
Step 4: Finally, I'll verify and state the answer...
Solution:
"""
def chain_of_thought_prompt(problem: str) -> str:
return COT_TEMPLATE.format(problem=problem)
# Usage
prompt = chain_of_thought_prompt(
problem="""
A mooring line has a breaking load of 5000 kN.
The maximum tension is 2800 kN.
What is the safety factor, and does it meet the API RP 2SK
requirement of 1.67 for intact conditions?
"""
)
Zero-Shot Chain-of-Thought:
def zero_shot_cot(question: str) -> str:
"""
Zero-shot CoT: Simply append "Let's think step by step"
Surprisingly effective for many reasoning tasks.
"""
return f"{question}\n\nLet's think step by step."
# Usage
prompt = zero_shot_cot(
"If a vessel offsets 50m from its mean position, and the "
"mooring stiffness is 100 kN/m, what is the restoring force?"
)
Structured Chain-of-Thought:
STRUCTURED_COT_TEMPLATE = """
Analyze this engineering problem using structured reasoning.
Problem: {problem}