Chain-of-Thought Designer
Design effective chain-of-thought prompting strategies that elicit step-by-step reasoning from large language models for complex problem-solving, analysis, and decision-making tasks.
SupaScore
84.05Best for
- ▸Design zero-shot CoT prompts that trigger systematic reasoning in GPT-4/Claude for complex analysis tasks
- ▸Create few-shot exemplars that teach LLMs specific reasoning patterns for domain-specific problem solving
- ▸Build self-consistency frameworks that generate multiple reasoning paths to improve answer accuracy
- ▸Architect tree-of-thoughts prompting for strategic planning and multi-step decision making
- ▸Design structured reasoning templates for auditable AI decision-making in enterprise applications
What you'll get
- ●Zero-shot CoT prompt with specific reasoning triggers and verification steps for mathematical word problems
- ●Few-shot template with 3-5 detailed exemplars showing reasoning patterns for business case analysis
- ●Self-consistency framework with multiple reasoning path generation and answer aggregation logic
Not designed for ↓
- ×Basic prompt engineering without step-by-step reasoning requirements
- ×Fine-tuning language models or adjusting model weights
- ×Creating conversational chatbots or dialogue systems
- ×General content generation that doesn't require logical reasoning chains
A complex reasoning task description with clear input-output requirements and desired reasoning style.
Complete chain-of-thought prompt templates with reasoning triggers, exemplars, and evaluation criteria specific to the task type.
Evidence Policy
Enabled: this skill cites sources and distinguishes evidence from opinion.
Research Foundation: 7 sources (4 academic, 2 official docs, 1 books)
This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.
Version History
Initial release
Works well with
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Common Workflows
Advanced LLM Application Development
Design reasoning prompts, structure outputs, then build evaluation frameworks for production LLM applications requiring systematic reasoning.
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