pdca-tracker

What this skill does

# pdca-tracker

You are **pdca-tracker** - a specialized skill for tracking PDCA (Plan-Do-Check-Act) cycles and improvement management.

## Overview

This skill enables AI-powered PDCA tracking including:
- PDCA cycle setup and management
- Hypothesis development
- Experiment planning
- Results verification
- Standard work updates
- Cycle iteration tracking
- Learning documentation
- Multi-project portfolio view

## Capabilities

### 1. PDCA Cycle Setup

```python
from dataclasses import dataclass
from typing import List, Dict, Optional
from datetime import datetime, timedelta
from enum import Enum
import uuid

class PDCAPhase(Enum):
    PLAN = "plan"
    DO = "do"
    CHECK = "check"
    ACT = "act"

@dataclass
class PDCACycle:
    id: str
    title: str
    owner: str
    start_date: datetime
    current_phase: PDCAPhase
    iteration: int = 1

def create_pdca_cycle(title: str, owner: str, hypothesis: str,
                     success_criteria: Dict):
    """
    Create new PDCA cycle

    hypothesis: What we believe will happen
    success_criteria: Measurable criteria for success
    """
    cycle_id = str(uuid.uuid4())[:8]

    cycle = {
        "id": cycle_id,
        "title": title,
        "owner": owner,
        "created_date": datetime.now().strftime("%Y-%m-%d"),
        "iteration": 1,
        "current_phase": "PLAN",
        "phases": {
            "PLAN": {
                "status": "in_progress",
                "hypothesis": hypothesis,
                "success_criteria": success_criteria,
                "planned_actions": [],
                "resources_needed": [],
                "timeline": None,
                "completed_date": None
            },
            "DO": {
                "status": "not_started",
                "actions_taken": [],
                "observations": [],
                "data_collected": [],
                "issues_encountered": [],
                "completed_date": None
            },
            "CHECK": {
                "status": "not_started",
                "results": {},
                "hypothesis_validated": None,
                "learnings": [],
                "completed_date": None
            },
            "ACT": {
                "status": "not_started",
                "decision": None,  # standardize, adjust, abandon
                "standard_work_updates": [],
                "next_cycle_needed": None,
                "completed_date": None
            }
        },
        "history": []
    }

    return cycle
```

### 2. Plan Phase Management

```python
def develop_plan(cycle: Dict, plan_details: Dict):
    """
    Develop the Plan phase

    plan_details: {
        'actions': [{'description': str, 'owner': str, 'due_date': str}],
        'timeline': {'start': str, 'end': str},
        'resources': [str],
        'risks': [str]
    }
    """
    cycle['phases']['PLAN']['planned_actions'] = plan_details.get('actions', [])
    cycle['phases']['PLAN']['timeline'] = plan_details.get('timeline')
    cycle['phases']['PLAN']['resources_needed'] = plan_details.get('resources', [])
    cycle['phases']['PLAN']['risks'] = plan_details.get('risks', [])

    # Validate plan completeness
    validation = validate_plan(cycle['phases']['PLAN'])

    if validation['is_complete']:
        cycle['phases']['PLAN']['status'] = 'complete'
        cycle['phases']['PLAN']['completed_date'] = datetime.now().strftime("%Y-%m-%d")
        cycle['current_phase'] = 'DO'
        cycle['phases']['DO']['status'] = 'in_progress'

        # Log transition
        cycle['history'].append({
            'timestamp': datetime.now().isoformat(),
            'event': 'phase_transition',
            'from': 'PLAN',
            'to': 'DO'
        })

    return {
        'cycle': cycle,
        'validation': validation
    }

def validate_plan(plan: Dict):
    """Validate plan completeness"""
    issues = []

    if not plan.get('hypothesis'):
        issues.append("Missing hypothesis")
    if not plan.get('success_criteria'):
        issues.append("Missing success criteria")
    if not plan.get('planned_actions'):
        issues.append("No actions planned")
    if not plan.get('timeline'):
        issues.append("No timeline defined")

    return {
        'is_complete': len(issues) == 0,
        'issues': issues
    }
```

### 3. Do Phase Tracking

```python
def track_do_phase(cycle: Dict, execution_data: Dict):
    """
    Track execution in Do phase

    execution_data: {
        'action_id': str,
        'status': str,
        'observations': [str],
        'data_points': [{'metric': str, 'value': float, 'timestamp': str}],
        'issues': [str]
    }
    """
    do_phase = cycle['phases']['DO']

    # Update action status
    for action in cycle['phases']['PLAN']['planned_actions']:
        if action.get('id') == execution_data.get('action_id'):
            action['status'] = execution_data['status']
            action['actual_completion'] = datetime.now().strftime("%Y-%m-%d")

    # Record observations
    if execution_data.get('observations'):
        do_phase['observations'].extend(execution_data['observations'])

    # Collect data
    if execution_data.get('data_points'):
        do_phase['data_collected'].extend(execution_data['data_points'])

    # Record issues
    if execution_data.get('issues'):
        do_phase['issues_encountered'].extend(execution_data['issues'])

    # Check if Do phase is complete
    planned_actions = cycle['phases']['PLAN']['planned_actions']
    completed = sum(1 for a in planned_actions if a.get('status') == 'complete')

    if completed == len(planned_actions):
        do_phase['status'] = 'complete'
        do_phase['completed_date'] = datetime.now().strftime("%Y-%m-%d")
        cycle['current_phase'] = 'CHECK'
        cycle['phases']['CHECK']['status'] = 'in_progress'

        cycle['history'].append({
            'timestamp': datetime.now().isoformat(),
            'event': 'phase_transition',
            'from': 'DO',
            'to': 'CHECK'
        })

    return {
        'cycle': cycle,
        'do_phase_progress': {
            'actions_completed': completed,
            'actions_total': len(planned_actions),
            'data_points_collected': len(do_phase['data_collected']),
            'issues_count': len(do_phase['issues_encountered'])
        }
    }
```

### 4. Check Phase Analysis

```python
import numpy as np

def analyze_check_phase(cycle: Dict):
    """
    Analyze results in Check phase
    """
    check_phase = cycle['phases']['CHECK']
    plan_phase = cycle['phases']['PLAN']
    do_phase = cycle['phases']['DO']

    results = {}

    # Compare results to success criteria
    success_criteria = plan_phase['success_criteria']
    data_collected = do_phase['data_collected']

    criteria_results = []
    for criterion, target in success_criteria.items():
        # Get data for this metric
        metric_data = [d['value'] for d in data_collected if d['metric'] == criterion]

        if metric_data:
            actual = np.mean(metric_data)
            met = (actual >= target if isinstance(target, (int, float))
                   else str(actual) == str(target))

            criteria_results.append({
                'criterion': criterion,
                'target': target,
                'actual': round(actual, 2) if isinstance(actual, float) else actual,
                'met': met
            })

    # Validate hypothesis
    criteria_met = sum(1 for c in criteria_results if c['met'])
    total_criteria = len(criteria_results)

    hypothesis_validated = criteria_met == total_criteria if total_criteria > 0 else None

    check_phase['results'] = {
        'criteria_results': criteria_results,
        'criteria_met': criteria_met,
        'total_criteria': total_criteria
    }
    check_phase['hypothesis_validated'] = hypothesis_validated

    # Generate learnings
    learnings = generate_learnings(criteria_results, do_phase['observations'],