القائمة
Discrete event simulation skill for modeling and analyzing complex systems with stochastic processes.
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npx skills add https://github.com/a5c-ai/babysitter --skill discrete-event-simulatorيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
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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/.
عرض SKILL.md
استنادا إلى تصنيف SOC المهني
| name | discrete-event-simulator |
| description | Discrete event simulation skill for modeling and analyzing complex systems with stochastic processes. |
| allowed-tools | Bash(*) Read Write Edit Glob Grep WebFetch |
| metadata | {"author":"babysitter-sdk","version":"1.0.0","category":"simulation","backlog-id":"SK-IE-005"} |
| 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 discrete-event-simulator - a specialized skill for building and analyzing discrete event simulation models for complex systems with stochastic processes.
This skill enables AI-powered discrete event simulation including:
import simpy
import numpy as np
def manufacturing_system(env, arrival_rate, service_rate, num_machines):
"""
Simple manufacturing system simulation
"""
machines = simpy.Resource(env, capacity=num_machines)
# Statistics collection
wait_times = []
system_times = []
def customer(env, name, machines):
arrival_time = env.now
with machines.request() as request:
yield request
wait_time = env.now - arrival_time
wait_times.append(wait_time)
# Service time
service_time = np.random.exponential(1/service_rate)
yield env.timeout(service_time)
system_times.append(env.now - arrival_time)
def customer_generator(env, arrival_rate, machines):
customer_id = 0
while True:
yield env.timeout(np.random.exponential(1/arrival_rate))
customer_id += 1
env.process(customer(env, f"Customer_{customer_id}", machines))
env.process(customer_generator(env, arrival_rate, machines))
env.run(until=1000)
return {
"avg_wait_time": np.mean(wait_times),
"avg_system_time": np.mean(system_times),
"max_wait_time": np.max(wait_times),
"customers_served": len(wait_times)
}
class ManufacturingLine:
"""
Multi-stage manufacturing line with buffers
"""
def __init__(self, env, config):
self.env = env
self.config = config
# Create resources
self.stations = {
name: simpy.Resource(env, capacity=cap)
for name, cap in config['stations'].items()
}
# Buffers between stations
self.buffers = {
name: simpy.Container(env, capacity=cap, init=0)
for name, cap in config['buffers'].items()
}
# Statistics
self.stats = {
'throughput': 0,
'wip': [],
'utilization': {s: [] for s in self.stations}
}
def part_flow(self, part_id):
"""Process a part through all stations"""
for station_name in self.config['routing']:
station = self.stations[station_name]
with station.request() as req:
yield req
# Record utilization
self.stats['utilization'][station_name].append(
station.count / station.capacity
)
# Process time
process_time = self.config['process_times'][station_name]()
yield self.env.timeout(process_time)
self.stats['throughput'] += 1
def part_arrivals(self):
"""Generate arriving parts"""
part_id = 0
while True:
yield self.env.timeout(self.config['interarrival_time']())
part_id += 1
self.env.process(self.part_flow(part_id))
self.stats['wip'].append(sum(s.count for s in self.stations.values()))
class PriorityQueue:
"""
Priority queue resource for SimPy
"""
def __init__(self, env, capacity):
self.env = env
self.capacity = capacity
self.queue = []
self.available = capacity
def request(self, priority=0):
return PriorityRequest(self, priority)
class PriorityRequest:
def __init__(self, resource, priority):
self.resource = resource
self.priority = priority
self.event = resource.env.event()
def __enter__(self):
if self.resource.available > 0:
self.resource.available -= 1
self.event.succeed()
else:
# Insert by priority (lower = higher priority)
heapq.heappush(self.resource.queue,
(self.priority, self.event))
return self.event
def __exit__(self, *args):
self.resource.available += 1
if self.resource.queue:
_, waiting_event = heapq.heappop(self.resource.queue)
waiting_event.succeed()
def welch_warmup_detection(data, window_size=50):
"""
Welch's method for detecting warm-up period
"""
n = len(data)
moving_avg = []
for i in range(n - window_size + 1):
moving_avg.append(np.mean(data[i:i+window_size]))
# Find convergence point
threshold = 0.01 * np.std(moving_avg)
converged = False
warmup_end = 0
for i in range(len(moving_avg) - 1):
if abs(moving_avg[i+1] - moving_avg[i]) < threshold:
if not converged:
warmup_end = i
converged = True
else:
converged = False
return warmup_end * window_size
def run_with_warmup(env, model, warmup_time, run_time):
"""
Run simulation with warm-up period removal
"""
env.run(until=warmup_time)
model.reset_statistics()
env.run(until=warmup_time + run_time)
return model.get_statistics()
def replicated_runs(model_func, num_replications, confidence=0.95):
"""
Run multiple replications and compute confidence intervals
"""
results = []
for rep in range(num_replications):
env = simpy.Environment()
result = model_func(env, seed=rep)
results.append(result)
# Compute statistics
means = {key: np.mean([r[key] for r in results])
for key in results[0].keys()}
stds = {key: np.std([r[key] for r in results], ddof=1)
for key in results[0].keys()}
# Confidence intervals
from scipy import stats
t_value = stats.t.ppf((1 + confidence) / 2, num_replications - 1)
ci = {key: (means[key] - t_value * stds[key] / np.sqrt(num_replications),
means[key] + t_value * stds[key] / np.sqrt(num_replications))
for key in means.keys()}
return {
"means": means,
"std_devs": stds,
"confidence_intervals": ci,
"num_replications": num_replications
}
This skill integrates with the following processes:
discrete-event-simulation-modeling.jsqueuing-system-analysis.jscapacity-planning-analysis.js{
"model_name": "Manufacturing_Line",
"simulation_time": 10000,
"replications": 30,
"warmup_time": 1000,
"performance_measures": {
"throughput": {
"mean": 245.3,
"std": 12.4,
"ci_95": [240.1, 250.5]
},
"avg_wait_time": {
"mean": 5.2,
"std": 0.8,
"ci_95": [4.9, 5.5]
},
"utilization": {
"station_1": 0.82,
"station_2": 0.91
}
},
"bottleneck": "station_2",
"recommendations": [
"Consider adding capacity at station_2"
]
}
| Library | Description | Use Case |
|---|---|---|
| SimPy | DES framework | General simulation |
| Ciw | Queue networks | Service systems |
| salabim | Animation | Visual models |
| scipy.stats | Statistics | Output analysis |