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PDCA cycle tracking skill for plan-do-check-act improvement management.
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npx skills add https://github.com/a5c-ai/babysitter --skill pdca-trackerコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
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Reference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)
Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)
This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
SKILL.md を表示中
SOC 職業分類に基づく
| name | pdca-tracker |
| description | PDCA cycle tracking skill for plan-do-check-act improvement management. |
| allowed-tools | Bash(*) Read Write Edit Glob Grep WebFetch |
| metadata | {"author":"babysitter-sdk","version":"1.0.0","category":"continuous-improvement","backlog-id":"SK-IE-041"} |
| graph | {"domains":["domain:industrial-engineering"],"skillAreas":["skill-area:statistical-analysis","skill-area:organizational-design","skill-area:data-analysis"],"roles":["role:operations-analyst","role:research-engineer"]} |
You are pdca-tracker - a specialized skill for tracking PDCA (Plan-Do-Check-Act) cycles and improvement management.
This skill enables AI-powered PDCA tracking including:
from dataclasses import dataclass
typing , ,
datetime datetime, timedelta
enum Enum
uuid
():
PLAN =
DO =
CHECK =
ACT =
:
:
title:
owner:
start_date: datetime
current_phase: PDCAPhase
iteration: =
():
cycle_id = (uuid.uuid4())[:]
cycle = {
: cycle_id,
: title,
: owner,
: datetime.now().strftime(),
: ,
: ,
: {
: {
: ,
: hypothesis,
: success_criteria,
: [],
: [],
: ,
:
},
: {
: ,
: [],
: [],
: [],
: [],
:
},
: {
: ,
: {},
: ,
: [],
:
},
: {
: ,
: ,
: [],
: ,
:
}
},
: []
}
cycle
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
}
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'])
}
}
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'],
do_phase['issues_encountered'])
check_phase['learnings'] = learnings
return {
'cycle': cycle,
'analysis': {
'hypothesis_validated': hypothesis_validated,
'success_rate': round(criteria_met / total_criteria * 100, 1) if total_criteria > 0 else 0,
'criteria_results': criteria_results,
'learnings': learnings
}
}
def generate_learnings(criteria_results, observations, issues):
"""Generate learnings from check phase"""
learnings = []
# From criteria results
for cr in criteria_results:
if cr['met']:
learnings.append(f"SUCCESS: {cr['criterion']} achieved target")
else:
learnings.append(f"MISS: {cr['criterion']} did not meet target - investigate root cause")
# From issues
if issues:
learnings.append(f"ISSUES: {len(issues)} issues encountered during execution")
return learnings
def complete_act_phase(cycle: Dict, decision: str, next_steps: Dict):
"""
Complete Act phase with decision
decision: 'standardize', 'adjust', 'abandon'
next_steps: {
'standard_work_updates': [str],
'next_cycle_hypothesis': str, # if adjust
'reason_for_abandonment': str # if abandon
}
"""
act_phase = cycle['phases']['ACT']
act_phase['decision'] = decision
if decision == 'standardize':
act_phase['standard_work_updates'] = next_steps.get('standard_work_updates', [])
act_phase['next_cycle_needed'] = False
# Mark cycle complete
act_phase['status'] = 'complete'
act_phase['completed_date'] = datetime.now().strftime("%Y-%m-%d")
cycle['status'] = 'completed'
cycle['history'].append({
'timestamp': datetime.now().isoformat(),
'event': 'cycle_complete',
'decision': 'standardize',
'outcome': 'success'
})
elif decision == 'adjust':
act_phase['next_cycle_needed'] = True
act_phase['next_hypothesis'] = next_steps.get('next_cycle_hypothesis')
act_phase['adjustments'] = next_steps.get('adjustments', [])
# Prepare next iteration
cycle['iteration'] += 1
new_cycle = prepare_next_iteration(cycle)
cycle['history'].append({
'timestamp': datetime.now().isoformat(),
'event': 'iteration_start',
'iteration': cycle['iteration'],
'hypothesis': act_phase['next_hypothesis']
})
return {'cycle': cycle, 'next_iteration': new_cycle}
elif decision == 'abandon':
act_phase['reason_for_abandonment'] = next_steps.get('reason_for_abandonment')
act_phase['next_cycle_needed'] = False
act_phase['status'] = 'complete'
cycle['status'] = 'abandoned'
cycle['history'].append({
'timestamp': datetime.now().isoformat(),
'event': 'cycle_abandoned',
'reason': act_phase['reason_for_abandonment']
})
return {'cycle': cycle}
def prepare_next_iteration(current_cycle: Dict):
"""Prepare next PDCA iteration"""
return {
'iteration': current_cycle['iteration'],
'hypothesis': current_cycle['phases']['ACT'].get('next_hypothesis'),
'learnings_from_previous': current_cycle['phases']['CHECK']['learnings'],
'adjustments': current_cycle['phases']['ACT'].get('adjustments', [])
}
def get_portfolio_status(cycles: List[Dict]):
"""
Get portfolio view of all PDCA cycles
"""
summary = {
'total_cycles': len(cycles),
'by_phase': {phase.value: 0 for phase in PDCAPhase},
'by_status': {'active': 0, 'completed': 0, 'abandoned': 0},
'iterations': [],
'cycle_details': []
}
for cycle in cycles:
# Count by phase
current_phase = cycle.get('current_phase', 'PLAN')
summary['by_phase'][current_phase.lower()] += 1
# Count by status
status = cycle.get('status', 'active')
summary['by_status'][status] += 1
# Track iterations
summary['iterations'].append(cycle.get('iteration', 1))
# Cycle summary
summary['cycle_details'].append({
'id': cycle['id'],
'title': cycle['title'],
'owner': cycle['owner'],
'phase': current_phase,
'iteration': cycle.get('iteration', 1),
'status': status,
'hypothesis_validated': cycle['phases']['CHECK'].get('hypothesis_validated')
})
# Aggregate stats
summary['avg_iterations'] = round(np.mean(summary['iterations']), 1) if summary['iterations'] else 0
summary['success_rate'] = round(
sum(1 for c in cycles if c['phases']['CHECK'].get('hypothesis_validated') == True) /
len(cycles) * 100, 1
) if cycles else 0
return summary
def document_learnings(cycle: Dict):
"""
Create comprehensive learning document from PDCA cycle
"""
learning_doc = {
'cycle_id': cycle['id'],
'title': cycle['title'],
'date_completed': cycle['phases']['ACT'].get('completed_date'),
'iterations': cycle.get('iteration', 1),
'sections': {
'hypothesis': cycle['phases']['PLAN']['hypothesis'],
'what_we_tried': [],
'what_happened': [],
'what_we_learned': [],
'what_changed': [],
'recommendations': []
}
}
# What we tried
for action in cycle['phases']['PLAN']['planned_actions']:
learning_doc['sections']['what_we_tried'].append(action['description'])
# What happened
for obs in cycle['phases']['DO']['observations']:
learning_doc['sections']['what_happened'].append(obs)
for issue in cycle['phases']['DO']['issues_encountered']:
learning_doc['sections']['what_happened'].append(f"Issue: {issue}")
# What we learned
learning_doc['sections']['what_we_learned'] = cycle['phases']['CHECK']['learnings']
# What changed
if cycle['phases']['ACT']['decision'] == 'standardize':
learning_doc['sections']['what_changed'] = cycle['phases']['ACT']['standard_work_updates']
elif cycle['phases']['ACT']['decision'] == 'adjust':
learning_doc['sections']['what_changed'] = [
f"Adjusted hypothesis: {cycle['phases']['ACT'].get('next_hypothesis')}"
]
# Recommendations
learning_doc['sections']['recommendations'] = generate_recommendations(cycle)
return learning_doc
def generate_recommendations(cycle: Dict):
"""Generate recommendations based on cycle outcome"""
recommendations = []
if cycle['phases']['CHECK'].get('hypothesis_validated'):
recommendations.append("Document and share successful approach")
recommendations.append("Consider scaling to other areas")
else:
recommendations.append("Review root cause analysis depth")
if cycle.get('iteration', 1) >= 3:
recommendations.append("Consider different approach or escalation")
return recommendations
This skill integrates with the following processes:
continuous-improvement-program.jsa3-problem-solving-project.jskaizen-event-execution.js{
"pdca_cycle": {
"id": "abc12345",
"title": "Reduce Setup Time",
"current_phase": "CHECK",
"iteration": 2
},
"plan": {
"hypothesis": "Standardized tool staging will reduce setup 25%",
"success_criteria": {"setup_time_minutes": 15}
},
"check": {
"hypothesis_validated": false,
"actual": 18,
"gap": 3
},
"act": {
"decision": "adjust",
"next_hypothesis": "Add visual tool board"
}
}