Multi-Step Planning with Dependencies
Opus handles complex dependency reasoning that trips up smaller models. Asking for exit criteria and go/no-go points produces plans that work in reality, not just on paper. The critical path and contingency requirements force the model to think about sequencing and failure modes.
Create a detailed execution plan for: {{projectGoal}} **Current state:** {{currentState}} **Target state:** {{targetState}} **Timeline:** {{timeline}} **Resources available:** {{resources}} **Known risks:** {{knownRisks}} Build the plan with these requirements: 1. Break into phases with clear milestones and exit criteria for each phase 2. For each task, specify: owner role, estimated effort, dependencies (what must complete first), and deliverable 3. Identify the critical path — which sequence of tasks determines the minimum timeline? 4. Build in contingency: for each high-risk task, provide a fallback approach 5. Define go/no-go decision points where the plan should be re-evaluated Format as a structured plan I can hand to a project manager. Include a dependency graph showing which tasks block which.
Variables to customize
Why this prompt works
Opus handles complex dependency reasoning that trips up smaller models. Asking for exit criteria and go/no-go points produces plans that work in reality, not just on paper. The critical path and contingency requirements force the model to think about sequencing and failure modes.
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.