prompt-engineering-3-chain-of-thought-prompting
Sub-skill of prompt-engineering: 3. Chain-of-Thought Prompting.
Best use case
prompt-engineering-3-chain-of-thought-prompting is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of prompt-engineering: 3. Chain-of-Thought Prompting.
Teams using prompt-engineering-3-chain-of-thought-prompting should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/3-chain-of-thought-prompting/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How prompt-engineering-3-chain-of-thought-prompting Compares
| Feature / Agent | prompt-engineering-3-chain-of-thought-prompting | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of prompt-engineering: 3. Chain-of-Thought Prompting.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 3. Chain-of-Thought Prompting
## 3. Chain-of-Thought Prompting
**Basic Chain-of-Thought:**
```python
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:**
```python
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:**
```python
STRUCTURED_COT_TEMPLATE = """
Analyze this engineering problem using structured reasoning.
Problem: {problem}Related Skills
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