route-planning

route-planning is a skill for Claude Code, Codex from cxcscmu/SkillLearnBench. It costs 15 tokens per session (913 once invoked), scanned A, original, MIT.

A route-planning guide for arranging a road trip through several cities using travel distances, available days, and other limits such as avoiding flights.

In plain words
What is it for?
Compare possible city orders, calculate total distance, and distribute days across stops.
Why use it?
It reduces backtracking and helps fit travel time and city visits into the available schedule.

Skill for Claude CodeCodex

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

Good fit Compare possible city orders, calculate total distance, and distribute days across stops.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/cxcscmu/skilllearnbench/route-planning
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 cxcscmu/SkillLearnBench --skill route-planning
Clone the repo
git clone --depth 1 https://github.com/cxcscmu/SkillLearnBench

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.

agentmods badge for route-planning

README.md
[![agentmods](https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/route-planning.svg)](https://agentmods.dev/skills/cxcscmu/skilllearnbench/route-planning)
Your own site
<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/route-planning"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/route-planning.svg" alt="Measured on agentmods" height="20"></a>
Per session 15 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 913 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.
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 $0.00015 $0.00913
Opus 5 $0.00008 $0.00456
Sonnet 5 $0.00003 $0.00183
Haiku 4.5 $0.00002 $0.00091

Measured 3d ago against content hash 645a39c40461, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

route-planning 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 3d 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.

skills/b1-one-shot-claude-haiku-4-5/travel-planning/route-planning/SKILL.md · 130 lines

How it starts

The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Route Planning Skill

Overview

Plan efficient multi-city road trips with constraints like starting point, number of days, and no-fly requirements.

Core Strategy

Distance-Based Route Optimization

  • Use distance matrix to calculate travel times/distances
  • Minimize backtracking between cities
  • Allocate days based on travel time and activities per city

Day Allocation Strategy

  • 1 day for initial travel to first city
  • Remaining days distributed across 3 Ohio cities
  • Typical allocation: 2-2-2 days or 2-2-3 depending on distances
  • Last day may include return travel (not full return in this case)

Python Code Example

from typing import List, Dict, Tuple
from itertools import permutations

def find_best_route(
    start_city: str,
    num_cities: int,
    available_cities: List[str],
    distance_matrix: Dict[str, Dict[str, float]]
) -> Tuple[List[str], float]:
    """
    Find optimal route visiting num_cities starting from start_city.
    Returns (route, total_distance)
    """
    best_route = None
    best_distance = float('inf')

    # Try all permutations of available cities
    for perm in permutations(available_cities[:num_cities]):
        route = [start_city] + list(perm)
        total_distance = calculate_route_distance(route, distance_matrix)

        if total_distance < best_distance:
            best_distance = total_distance
            best_route = route

    return best_route, best_distance

def calculate_route_distance(route: List[str], distance_matrix: Dict) -> float:
    """Calculate total distance for a route"""
    total = 0
    for i in range(len(route) - 1):
        from_city = route[i]
        to_city = route[i + 1]

        if from_city in distance_matrix and to_city in distance_matrix[from_city]:
            total += float(distance_matrix[from_city][to_city])
        else:
            return float('inf')  # Invalid route

    return total

def allocate_days_to_cities(
    num_days: int,
    num_cities: int
) -> List[int]:
    """
    Allocate days across cities.
    First city gets 1 day (travel day), remaining split among other cities.
    """
    # First city gets 1 day for arrival/travel
    remaining_days = num_days - 1
    remaining_cities = num_cities

    days_per_city = [1]  # First city

    # Distribute remaining days
    base_days = remaining_days // remaining_cities
    extra_days = remaining_days % remaining_cities

    for i in range(remaining_cities):
        days = base_days + (1 if i < extra_days else 0)
        days_per_city.append(days)

    return days_per_city

def estimate_travel_time(distance: float, mph: float = 60) -> float:
    """Estimate driving time in hours"""
    return distance / mph

Read the full file on GitHub · 130 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. 3d ago First seen · 130 lines · 15 tokens per session scan A 645a39c40461

Subscribe to this mod's changes

route-planning is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 1mo ago), licensed MIT. It adds 15 tokens to every session and 913 once invoked, about $0.0001 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.

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