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 custom-distance-metricsgit 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/custom-distance-metrics)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/custom-distance-metrics"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/custom-distance-metrics.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00025 | $0.00310 |
| Opus 5 | $0.00013 | $0.00155 |
| Sonnet 5 | $0.00005 | $0.00062 |
| Haiku 4.5 | $0.00003 | $0.00031 |
Grade A, and why
custom-distance-metrics 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 4d 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.
What it actually says
Custom Distance Metrics for DBSCAN
Overview
DBSCAN in sklearn supports custom distance metrics via metric='precomputed' (pass a distance matrix) or metric=callable with the pairwise distance function.
Approach: Precomputed Distance Matrix
For small-to-medium datasets per image, computing a full pairwise distance matrix is efficient:
from sklearn.cluster import DBSCAN
from scipy.spatial.distance import pdist, squareform
import numpy as np
def weighted_euclidean(points, w):
"""Compute pairwise weighted Euclidean distance.
d(a,b) = sqrt((w*dx)^2 + ((2-w)*dy)^2)
"""
scaled = points * [w, 2 - w]
return squareform(pdist(scaled, metric='euclidean'))
# Usage
dist_matrix = weighted_euclidean(points_xy, shape_weight)
db = DBSCAN(eps=epsilon, min_samples=min_samples, metric='precomputed')
labels = db.fit_predict(dist_matrix)
Key Points
pdist+squareformis faster than looping over pairs- Scale the coordinates before computing standard Euclidean = same as custom weighted metric
- When w=1, this equals standard Euclidean distance
- Cluster centroids are computed from original (unscaled) coordinates
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.
- 4d ago First seen · 37 lines · 25 tokens per session scan A a7107392e03a
custom-distance-metrics is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 2mo ago), licensed MIT. It adds 25 tokens to every session and 310 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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