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Network optimization skill for transportation, assignment, and flow problems on graph structures.
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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 | network-optimizer |
| description | Network optimization skill for transportation, assignment, and flow problems on graph structures. |
| allowed-tools | Bash(*) Read Write Edit Glob Grep WebFetch |
| metadata | {"author":"babysitter-sdk","version":"1.0.0","category":"operations-research","backlog-id":"SK-IE-003"} |
| 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 network-optimizer - a specialized skill for solving network optimization problems including shortest paths, minimum spanning trees, maximum flows, and assignment problems.
This skill enables AI-powered network optimization including:
import networkx as nx
def shortest_path_analysis(G, source, target):
"""
Select and apply appropriate shortest path algorithm
"""
# Check for negative weights
has_negative = any(d.get('weight', 1) < 0
for u, v, d in G.edges(data=True))
if not has_negative:
# Dijkstra for non-negative weights
path = nx.dijkstra_path(G, source, target)
length = nx.dijkstra_path_length(G, source, target)
else:
# Bellman-Ford for negative weights
path = nx.bellman_ford_path(G, source, target)
length = nx.bellman_ford_path_length(G, source, target)
return {
"path": path,
"length": length,
"algorithm": "dijkstra" if not has_negative else "bellman_ford"
}
# All-pairs shortest paths
def all_pairs_shortest_paths(G):
# Floyd-Warshall for dense graphs
if G.number_of_edges() > G.number_of_nodes()**2 / 4:
return dict(nx.floyd_warshall(G))
else:
# Johnson for sparse graphs
return dict(nx.johnson(G))
def minimum_spanning_tree(G, algorithm='kruskal'):
"""
Generate minimum spanning tree
"""
if algorithm == 'kruskal':
mst = nx.minimum_spanning_tree(G, algorithm='kruskal')
elif algorithm == 'prim':
mst = nx.minimum_spanning_tree(G, algorithm='prim')
total_weight = sum(d['weight'] for u, v, d in mst.edges(data=True))
return {
"tree": mst,
"total_weight": total_weight,
"edges": list(mst.edges(data=True))
}
def max_flow_min_cut(G, source, sink):
"""
Compute maximum flow and minimum cut
"""
# Maximum flow
flow_value, flow_dict = nx.maximum_flow(G, source, sink)
# Minimum cut
cut_value, partition = nx.minimum_cut(G, source, sink)
# Identify cut edges
reachable, non_reachable = partition
cut_edges = [(u, v) for u in reachable for v in G[u]
if v in non_reachable]
return {
"max_flow": flow_value,
"flow_dict": flow_dict,
"min_cut_value": cut_value,
"cut_edges": cut_edges,
"source_side": list(reachable),
"sink_side": list(non_reachable)
}
from ortools.graph.python import min_cost_flow
def min_cost_flow_problem(nodes, arcs):
"""
Solve minimum cost network flow
"""
smcf = min_cost_flow.SimpleMinCostFlow()
# Add arcs: (start, end, capacity, unit_cost)
for start, end, capacity, cost in arcs:
smcf.add_arc_with_capacity_and_unit_cost(
start, end, capacity, cost
)
# Set supplies/demands
for node, supply in nodes.items():
smcf.set_node_supply(node, supply)
status = smcf.solve()
if status == smcf.OPTIMAL:
result = {
"status": "optimal",
"total_cost": smcf.optimal_cost(),
"flows": []
}
for i in range(smcf.num_arcs()):
if smcf.flow(i) > 0:
result["flows"].append({
"from": smcf.tail(i),
"to": smcf.head(i),
"flow": smcf.flow(i),
"cost": smcf.flow(i) * smcf.unit_cost(i)
})
return result
return {"status": "infeasible"}
from scipy.optimize import linear_sum_assignment
def assignment_problem(cost_matrix):
"""
Solve assignment problem using Hungarian algorithm
"""
row_ind, col_ind = linear_sum_assignment(cost_matrix)
total_cost = cost_matrix[row_ind, col_ind].sum()
assignments = list(zip(row_ind.tolist(), col_ind.tolist()))
return {
"total_cost": total_cost,
"assignments": assignments,
"assignment_costs": cost_matrix[row_ind, col_ind].tolist()
}
def multi_commodity_flow(G, commodities):
"""
Model multi-commodity flow problem
commodities: list of (source, sink, demand)
"""
from ortools.linear_solver import pywraplp
solver = pywraplp.Solver.CreateSolver('GLOP')
# Flow variables for each commodity on each edge
flows = {}
for k, (s, t, d) in enumerate(commodities):
for u, v in G.edges():
flows[k, u, v] = solver.NumVar(0, G[u][v]['capacity'],
f'f_{k}_{u}_{v}')
# Flow conservation
for k, (s, t, d) in enumerate(commodities):
for node in G.nodes():
inflow = sum(flows[k, u, node] for u in G.predecessors(node))
outflow = sum(flows[k, node, v] for v in G.successors(node))
if node == s:
solver.Add(outflow - inflow == d)
elif node == t:
solver.Add(inflow - outflow == d)
else:
solver.Add(inflow == outflow)
# Capacity constraints (shared)
for u, v in G.edges():
solver.Add(sum(flows[k, u, v] for k in range(len(commodities)))
<= G[u][v]['capacity'])
# Minimize total cost
solver.Minimize(sum(
flows[k, u, v] * G[u][v].get('cost', 1)
for k in range(len(commodities))
for u, v in G.edges()
))
solver.Solve()
return solver
This skill integrates with the following processes:
transportation-route-optimization.jswarehouse-layout-slotting-optimization.jscapacity-planning-analysis.js{
"problem_type": "max_flow",
"status": "optimal",
"objective": 23.0,
"solution": {
"flow_paths": [
{"path": ["s", "a", "b", "t"], "flow": 10},
{"path": ["s", "c", "t"], "flow": 13}
]
},
"analysis": {
"bottleneck_edges": [["a", "b"], ["c", "t"]],
"recommendations": ["Increase capacity on edge (a,b)"]
}
}
| Library | Description | Use Case |
|---|---|---|
| NetworkX | Graph analysis | General networks |
| OR-Tools | Min cost flow | Large-scale |
| igraph | Fast algorithms | Performance |
| SciPy | Assignment | Hungarian method |