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.
npx skills add cxcscmu/SkillLearnBench --skill route-planninggit clone --depth 1 https://github.com/cxcscmu/SkillLearnBenchWrote 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.
[](https://agentmods.dev/skills/cxcscmu/skilllearnbench/route-planning)<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>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.
| Model | Per session | Once 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 |
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.
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
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.
- 3d ago First seen · 130 lines · 15 tokens per session scan A 645a39c40461
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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