ortools-routing-modeling

ortools-routing-modeling is a skill for Claude Code, Codex from benchflow-ai/skillsbench. It costs 42 tokens per session (2,350 once invoked), scanned A, original, Apache-2.0.

A guide to building vehicle-routing models with Google OR-Tools, including limits on capacity, timing, visits, and travel between locations.

In plain words
What is it for?
Use it to model vehicles, travel and demand data, time windows, optional stops, pairing rules, and route extraction.
Why use it?
It helps turn real delivery or service rules into a solvable routing problem and checks that the resulting routes are meaningful.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to model vehicles, travel and demand data, time windows, optional stops, pairing rules, and route extraction.

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Install with agentmods
npx agentmods add skills/benchflow-ai/skillsbench/ortools-routing-modeling
About the project

SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.

benchflow-ai/skillsbench · 1,764 stars · on GitHub · skillsbench.ai

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add benchflow-ai/skillsbench --skill ortools-routing-modeling
Clone the repo
git clone --depth 1 https://github.com/benchflow-ai/skillsbench

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

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README.md
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Your own site · 80×15
<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/ortools-routing-modeling"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/ortools-routing-modeling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,350 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00042 $0.02350
Opus 5 $0.00021 $0.01175
Sonnet 5 $0.00008 $0.00470
Haiku 4.5 $0.00004 $0.00235

Measured 9d ago against content hash c563c6cdd94a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

ortools-routing-modeling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

tasks/paratransit-routing/environment/skills/ortools-routing-modeling/SKILL.md · 147 lines

How it starts

The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.

OR-Tools Routing Modeling

Use this skill for vehicle-routing models built with ortools.constraint_solver.pywrapcp.RoutingModel.

Modeling Flow

  1. Normalize integer data and map external nodes to routing-manager nodes.
  2. Build transit, cost, and demand matrices or vectors before registering callbacks.
  3. Add time and capacity dimensions, including vehicle start/end ranges and span limits.
  4. Add problem-specific visits, time windows, optional-node penalties, and pairing constraints.
  5. Solve with an internal time limit, extract ordered routes, then audit them independently before reporting.

Data And Indexing

  • Convert travel times, service times, demands, and time bounds to integers before registering callbacks or transit matrices. Avoid floats and NaN; OR-Tools can silently treat bad values as usable arcs.
  • Assert that matrix dimensions, node counts, vehicle counts, and declared record counts agree before building the model. Shape mistakes often produce plausible but meaningless routes.
  • Keep a clear distinction between external node IDs, routing manager node IDs, and internal routing indices. Convert with manager.NodeToIndex(node) and manager.IndexToNode(index).
  • When vehicles need distinct starts or ends but the input has shared depots, copy the depot into one internal start node and one internal end node per vehicle. Prefer these internal depot copies over reusing the same shared depot node for every vehicle, then map the copies back to the shared external depot IDs when reporting.
  • A common cloned-depot layout is: solver job nodes first, then one start depot per vehicle, then one end depot per vehicle. External shared depot IDs can be stripped from the job matrix and restored only when reporting.
  • Treat negative, missing, or sentinel travel times as invalid arcs. Either forbid them with explicit constraints or give them a prohibitive transit cost and reject any final route that uses them. Choose invalid-arc sentinels large enough to dominate feasible legs, but small enough to avoid integer overflow when service times and route spans are added.
  • For static integer data, prefer precomputed RegisterTransitMatrix and RegisterUnaryTransitVector calls over Python callbacks. Routing callbacks are called very often during local search and can consume solve time on large instances.

Read the full file on GitHub · 147 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 147 lines · 42 tokens per session scan A c563c6cdd94a

Subscribe to this mod's changes

ortools-routing-modeling is a skill published in the GitHub repository benchflow-ai/skillsbench (1,764 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 42 tokens to every session and 2,350 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.