Chain-of-Thought Reasoning
Explicit step-by-step instructions activate chain-of-thought reasoning, which dramatically improves accuracy on complex decisions. Asking for assumptions catches blind spots.
I need to decide: {{decision}}\n\nThink through this step by step:\n1. What are the key factors to consider?\n2. What are the possible options?\n3. For each option, what are the likely outcomes (best case, worst case, most likely)?\n4. What assumptions am I making that could be wrong?\n5. Based on this analysis, what do you recommend and why?\n\nShow your reasoning for each step before giving the final recommendation.Variables to customize
Why this prompt works
Explicit step-by-step instructions activate chain-of-thought reasoning, which dramatically improves accuracy on complex decisions. Asking for assumptions catches blind spots.
Save this prompt to your library
Organize, version, and access your best prompts across ChatGPT, Claude, and Cursor.
Related prompts
Forcing the agent to plan before acting prevents premature execution and wasted steps. Explicit dependency mapping enables parallel execution and catches logical gaps early.
Tool Selection AgentThe ReAct pattern (Reason + Act) creates an explicit reasoning trace that improves tool selection accuracy. The error-handling rule prevents infinite retry loops.
Prompt CompressorExplicitly requiring all functional requirements to be preserved prevents the model from over-compressing and losing critical instructions.
Memory Management AgentExplicit memory read/write instructions create agents that improve over time. Categorization keeps memories organized, and the deduplication rule prevents context bloat.