custom-dbscan-metric SKILL.md skillA
custom-dbscan-metric from cxcscmu/skilllearnbench is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add cxcscmu/skilllearnbench/custom-dbscan-metric@git:20260424.d50cf5aPins git:20260424.d50cf5a in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- cxcscmu/skilllearnbench
- Path
skills/b1-one-shot-gemini-3-flash-preview/dbscan-parameter-tuning/custom-dbscan-metric/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260424.d50cf5a, committed Apr 24, 2026
- Size
- 1,788 bytes
- Repository stars
- 91
- License
- MIT
Implementation of a custom distance metric for DBSCAN clustering using scipy and sklearn.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Custom Distance Metric for DBSCAN
When using DBSCAN with a non-standard distance metric, you can either provide a callable to the `metric` parameter or precompute the distance matrix.
## Mathematical Formulation
For the Mars cloud task, the distance is defined as:
`d(a, b) = sqrt((w * Δx)² + ((2 - w) * Δy)²)`
## Implementation using scipy.spatial.distance.cdist
Precomputing the distance matrix is often more efficient for grid searches if the metric is reused or if you want to use `sklearn.cluster.DBSCAN`.
```python
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.cluster import DBSCAN
def custom_metric(p1, p2, w):
dx = p1[0] - p2[0]
dy = p1[1] - p2[1]
return np.sqrt((w * dx)**2 + ((2 - w) * dy)**2)
# Vectorized version for efficiency
def precompute_custom_distance(X, w):
# X is (N, 2)
# Using cdist with a custom lambda can be slow,
# better to use vectorized numpy if possible.
X_weighted = X * np.array([w, 2 - w])
# Note: the formula is sqrt((w*dx)^2 + ((2-w)*dy)^2)
# which is equivalent to standard Euclidean distance on weighted coordinates
return cdist(X_weighted, X_weighted, metric='euclidean')
…Read the whole file at its exact version.
How to install
mdr add cxcscmu/skilllearnbench/custom-dbscan-metric@sha256:87ef714b61b25985The line at the top of this page pins the label git:20260424.d50cf5a, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_aeljszbg3tokfrl7)
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Versions
The registry has recorded 1 version of this file, committed on Apr 24, 2026.
Audit of the latest version
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- pass: No link to an exfiltration or paste host
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- pass: No instruction to send local credentials anywhere
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Source
cxcscmu/skilllearnbench · 91 stars · license MIT · pushed 2026-10-02 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_aeljszbg3tokfrl7 GET https://markdownregistry.com/api/v1/resolve?ref=cxcscmu/skilllearnbench/custom-dbscan-metric GET https://markdownregistry.com/api/v1/blob/87ef714b61b25985ce09b25f191243c68486ea62651b7acc302014fc62b0f894
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.