القائمة
Value stream mapping skill for current state analysis, waste identification, and future state design.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/a5c-ai/babysitter --skill value-stream-mapperيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
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/.
عرض SKILL.md
استنادا إلى تصنيف SOC المهني
| name | value-stream-mapper |
| description | Value stream mapping skill for current state analysis, waste identification, and future state design. |
| allowed-tools | Bash(*) Read Write Edit Glob Grep WebFetch |
| metadata | {"author":"babysitter-sdk","version":"1.0.0","category":"lean-manufacturing","backlog-id":"SK-IE-009"} |
| 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 value-stream-mapper - a specialized skill for creating and analyzing value stream maps to identify waste and design improved future states.
This skill enables AI-powered value stream mapping including:
class ProcessBox:
"""
Data structure for VSM process box
"""
def __init__(self, name):
self.name = name
self.cycle_time = None # C/T in seconds
self.changeover_time = None # C/O in minutes
self.uptime = None # Percentage
self.operators = None
self.batch_size = None
self.available_time = None # seconds per shift
self.shifts = None
self.scrap_rate = None # Percentage
self.wip_before = None # Inventory before process
self.wip_after = None # Inventory after process
def calculate_metrics(self):
"""Calculate derived metrics"""
# Effective cycle time
if self.uptime:
self.effective_ct = self.cycle_time / (self.uptime / 100)
# Available production time
if self.available_time and self.shifts:
self.daily_available = self.available_time * self.shifts
# Daily capacity
if self.daily_available and self.cycle_time:
self.daily_capacity = self.daily_available / self.cycle_time
return self
class ValueStreamMap:
"""
Complete value stream map data structure
"""
def __init__(self, product_family, customer_demand):
self.product_family = product_family
self.customer_demand = customer_demand # units per day
self.processes = []
self.suppliers = []
self.customer = None
self.information_flows = []
def add_process(self, process_box):
self.processes.append(process_box)
def calculate_takt_time(self, available_time_per_day):
"""Takt time = available time / customer demand"""
self.takt_time = available_time_per_day / self.customer_demand
return self.takt_time
def calculate_timeline(vsm):
"""
Calculate lead time and process time from VSM
"""
process_time = 0
lead_time = 0
timeline = []
for process in vsm.processes:
# Process time (value-added time)
pt = process.cycle_time
process_time += pt
# Lead time includes waiting (inventory)
# Assume inventory in days
wait_time = 0
if process.wip_before:
wait_time = process.wip_before / vsm.customer_demand
lead_time += wait_time + (pt / 3600 / 8) # Convert to days
timeline.append({
"process": process.name,
"process_time_sec": pt,
"wait_time_days": wait_time,
"cumulative_lead_time": lead_time
})
return {
"total_process_time_sec": process_time,
"total_lead_time_days": lead_time,
"pce": (process_time / 3600 / 8) / lead_time * 100, # Process Cycle Efficiency
"timeline": timeline
}
def identify_wastes(vsm, timeline):
"""
Identify seven wastes + skills underutilization
"""
wastes = {
"transportation": [],
"inventory": [],
"motion": [],
"waiting": [],
"overproduction": [],
"overprocessing": [],
"defects": [],
"skills_underutilization": []
}
# Inventory waste
for process in vsm.processes:
if process.wip_before and process.wip_before > vsm.customer_demand:
wastes["inventory"].append({
"location": f"Before {process.name}",
"amount": process.wip_before,
"days_supply": process.wip_before / vsm.customer_demand,
"impact": "Excess inventory ties up capital"
})
# Waiting waste
total_wait = sum(t['wait_time_days'] for t in timeline['timeline'])
if total_wait > timeline['total_lead_time_days'] * 0.5:
wastes["waiting"].append({
"description": "Significant waiting time",
"wait_percentage": total_wait / timeline['total_lead_time_days'] * 100,
"impact": "Low process cycle efficiency"
})
# Defects
for process in vsm.processes:
if process.scrap_rate and process.scrap_rate > 1:
wastes["defects"].append({
"process": process.name,
"scrap_rate": process.scrap_rate,
"impact": f"Losing {process.scrap_rate}% of production"
})
# Overproduction (producing more than takt)
for process in vsm.processes:
if process.daily_capacity and process.daily_capacity > vsm.customer_demand * 1.2:
wastes["overproduction"].append({
"process": process.name,
"capacity_vs_demand": process.daily_capacity / vsm.customer_demand,
"risk": "May produce excess inventory"
})
return wastes
class FutureStateDesign:
"""
Design future state value stream
"""
def __init__(self, current_vsm, target_improvements):
self.current = current_vsm
self.targets = target_improvements
self.kaizen_bursts = []
self.supermarket_locations = []
self.pacemaker_process = None
def design_pull_system(self):
"""Design supermarkets and pull signals"""
# Identify pacemaker (process closest to customer that sets pace)
self.pacemaker_process = self.current.processes[-1]
# Supermarket locations - break continuous flow where needed
for i, process in enumerate(self.current.processes[:-1]):
next_process = self.current.processes[i+1]
# Supermarket if: different cycle times, changeovers, reliability issues
needs_supermarket = (
abs(process.cycle_time - next_process.cycle_time) / process.cycle_time > 0.2 or
(process.changeover_time and process.changeover_time > 10) or
(process.uptime and process.uptime < 90)
)
if needs_supermarket:
self.supermarket_locations.append({
"after_process": process.name,
"reason": "Decouple due to cycle time or reliability mismatch",
"kanban_quantity": self._calculate_kanban(process)
})
def _calculate_kanban(self, process):
"""Calculate kanban quantity for supermarket"""
daily_demand = self.current.customer_demand
lead_time_days = 1 # Replenishment lead time
safety_factor = 1.5
container_size = 50 # Typical container
kanban_qty = (daily_demand * lead_time_days * safety_factor) / container_size
return int(kanban_qty) + 1
def add_kaizen_burst(self, location, description, target_improvement):
"""Add improvement opportunity"""
self.kaizen_bursts.append({
"location": location,
"description": description,
"target": target_improvement,
"priority": None # Set during implementation planning
})
def create_implementation_roadmap(future_state):
"""
Create phased implementation plan
"""
roadmap = {
"phase_1_foundation": {
"duration": "1-3 months",
"activities": [
"5S implementation at pacemaker",
"Standard work documentation",
"Basic visual management"
]
},
"phase_2_flow": {
"duration": "3-6 months",
"activities": [
"Implement supermarkets",
"Create kanban loops",
"Level production schedule"
]
},
"phase_3_pull": {
"duration": "6-12 months",
"activities": [
"Connect all pull signals",
"SMED on changeovers",
"TPM implementation"
]
}
}
# Prioritize kaizen bursts
for burst in future_state.kaizen_bursts:
if "changeover" in burst['description'].lower():
burst['priority'] = "phase_2_flow"
elif "quality" in burst['description'].lower():
burst['priority'] = "phase_1_foundation"
else:
burst['priority'] = "phase_3_pull"
roadmap['kaizen_events'] = future_state.kaizen_bursts
return roadmap
Process Box: [========] Contains C/T, C/O, Uptime
| Name |
[========]
Inventory Triangle: /\ Shows quantity and days
/ \
/____\
Supermarket: [===] Pull inventory buffer
[===]
[===]
Kanban: [K] Pull signal
FIFO Lane: >>> First-in-first-out
Information Flow: ------> Electronic
~~~~~~> Manual
Push Arrow: ===> Push production
Kaizen Burst: *** Improvement opportunity
* K *
***
This skill integrates with the following processes:
value-stream-mapping-analysis.jskaizen-event-facilitation.jsstandard-work-development.js{
"vsm_summary": {
"product_family": "Widget A",
"customer_demand": 460,
"takt_time_sec": 62,
"total_processes": 5
},
"current_state": {
"lead_time_days": 23.5,
"process_time_min": 185,
"pce_percent": 0.55
},
"wastes_identified": {
"inventory": 3,
"waiting": 2,
"defects": 1
},
"future_state": {
"target_lead_time": 5,
"supermarkets": 2,
"kaizen_bursts": 6
},
"implementation_timeline": "12 months"
}