# Planning

> A high-level cognitive pattern where an agent formulates a structured sequence of actions (a plan) before executing any of them, ensuring goal-directed behavior. Use when user asks to "add planning to my agent", "task planning", "agent planning", or mentions plan generation, plan execution, or step-by-step planning.

- Skill: `lauraflorentin/planning` (Agent Skill)
- Install (CLI): `npx skillmds@latest add lauraflorentin/planning`
- Raw SKILL.md: https://api.skillmd.com/api/skills/lauraflorentin/planning/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: lauraflorentin (https://skillmd.com/u/lauraflorentin)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/lauraflorentin/planning

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# Planning

Planning (sometimes called "Reasoning & Acting") decouples the strategy from the execution. Instead of reacting immediately to a user request, the agent pauses to decompose the goal into sub-goals, identifies dependencies, and creates an ordered list of steps. This allows agents to tackle complex, multi-step problems that require foresight.

## When to Use

-   **Multi-Step Tasks**: "Research X, then Y, then write a report comparing them."
-   **Dependency Management**: When step B cannot start until step A is finished (e.g., compile code -> run tests).
-   **Resource Constraints**: To optimize the usage of expensive tools or API calls.
-   **Error Recovery**: If a step fails, the plan can be adjusted dynamically without restarting from scratch.

## Use Cases

-   **Travel Itinerary**: Searching for flights, hotels, and activities, then checking availability, then booking.
-   **Software Development**: Designing a system -> Writing code -> Writing documentation.
-   **Data Analysis**: Plan -> Data Collection -> Cleaning -> Analysis -> Visualization.

## Implementation Pattern

```python
def planning_workflow(goal):
    # Step 1: Create Plan
    # The planner generates a list of steps, not the actual work.
    plan = planner_agent.run(
        prompt="Create a step-by-step plan to achieve this goal...",
        input=goal
    )
    
    results = {}
    
    # Step 2: Execute Plan
    for step in plan.steps:
        # Check dependencies
        if not check_dependencies(step, results):
            raise DepedencyError(f"Cannot execute {step.id}")
            
        # Execute the specific step using a worker agent
        result = worker_agent.run(
            prompt=f"Execute this step: {step.description}",
            context=results # Pass context from previous steps
        )
        
        results[step.id] = result
        
    # Step 3: Summarize
    return synthesizer_agent.run(results)
```