Ptd

# Skill: Predictive Task Decomposition (PTD)

**Version:** 1.0
**Author:** Manus AI

---

## 1. Description

This skill implements **Predictive Task Decomposition (PTD)**, a powerful technique for accelerating project completion by maximizing parallel execution. Instead of following a rigid, sequential plan, PTD analyzes a high-level goal and breaks it down into a directed acyclic graph (DAG) of smaller, independent tasks. By understanding the dependencies between these tasks from the outset, the system can identify and execute multiple tasks concurrently, significantly reducing overall development time.

This skill is a direct implementation of the fifth of the 10 breakthrough LLM innovations.

### Key Features:

- **Graph-Based Planning:** Represents the entire project as a task dependency graph, providing a clear and holistic view of the workflow.
- **Parallel Execution:** Identifies all tasks that can be worked on simultaneously, enabling maximum throughput.
- **Dynamic Scheduling:** Provides a method to get the next set of executable tasks as prior tasks are completed.
- **Progress Tracking:** Allows the system to know exactly which tasks are done and what remains, leading to accurate progress monitoring.

## 2. How to Use

### 2.1. Installation

This skill is a self-contained Python module. To use it, import the `TaskDecomposer` class.

```python
from skills.ptd.src.ptd_engine import TaskDecomposer
```

### 2.2. Decomposing a Goal

Start by instantiating the `TaskDecomposer`. Then, based on a high-level goal, add all the necessary sub-tasks and their dependencies to build the graph.

```python
ptd = TaskDecomposer()

# Decompose the goal: "Build and deploy a web app"

# Level 1 (No dependencies)
task_db_schema = ptd.add_task(
    name="Design Database Schema", 
    description="Define the tables and relationships for user and product data."
)
task_ui_mockup = ptd.add_task(
    name="Create UI Mockups", 
    description="Design the look and feel of the main application pages."
)

# Level 2 (Depends on Level 1)
task_backend_api = ptd.add_task(
    name="Develop Backend API", 
    description="Create API endpoints for CRUD operations.",
    dependencies={task_db_schema}
)
task_frontend_dev = ptd.add_task(
    name="Develop Frontend Components", 
    description="Build React components based on the UI mockups.",
    dependencies={task_ui_mockup}
)

# Level 3 (Depends on Level 2)
task_integration = ptd.add_task(
    name="Integrate Frontend and Backend",
    description="Connect the React frontend to the backend API.",
    dependencies={task_backend_api, task_frontend_dev}
)
```

### 2.3. Executing Tasks in Parallel

The `get_executable_tasks` method returns all tasks that are ready to be worked on (i.e., all their dependencies are complete). This allows you to dispatch multiple tasks to different agents simultaneously.

```python
# Initially, only tasks with no dependencies are executable
executable_tasks = ptd.get_executable_tasks()
print(f"Tasks ready for parallel execution: {[t.name for t in executable_tasks]}")

# Simulate completing the first two tasks
for task in executable_tasks:
    print(f"Executing task: {task.name}")
    ptd.mark_as_complete(task.task_id)

# Now, the next level of tasks becomes available
next_executable_tasks = ptd.get_executable_tasks()
print(f"\nNext tasks for parallel execution: {[t.name for t in next_executable_tasks]}")
```

### 2.4. Checking for Goal Completion

You can check at any time if the entire project is complete.

```python
if ptd.is_goal_complete():
    print("Project is complete!")
```

## 3. Development Roadmap

PTD is essential for efficient, large-scale AI-driven development. Future work will focus on making the decomposition process itself more intelligent.

- **v1.1: Predictive Decomposition Agent:**
    - **Goal:** Create an LLM-based agent that can take a high-level user prompt and automatically generate the entire task dependency graph, predicting all necessary steps and their relationships.
    - **Timeline:** 4 weeks

- **v1.2: Critical Path Analysis:**
    - **Goal:** Implement an algorithm to identify the "critical path" in the task graph—the longest sequence of dependent tasks. This will allow the system to prioritize resources for tasks that are bottlenecks.
    - **Timeline:** 3 weeks

- **v1.3: Resource Allocation:**
    - **Goal:** Integrate with the **Dynamic Agent Composition (DAC)** skill. When a set of tasks becomes executable, PTD will query DAC to assemble the right agents for those specific tasks.
    - **Timeline:** 4 weeks

- **v2.0: Dynamic Graph Adaptation:**
    - **Goal:** Allow the task graph to be modified during execution. If an agent discovers that a task requires an additional, unforeseen step, it can dynamically insert a new node into the graph, and the PTD engine will adjust the plan accordingly.
    - **Timeline:** 8 weeks