dbscan-custom-metrics SKILL.md skillA
dbscan-custom-metrics 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/dbscan-custom-metrics@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-claude-haiku-4-5/dbscan-parameter-tuning/dbscan-custom-metrics/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260424.d50cf5a, committed Apr 24, 2026
- Size
- 2,378 bytes
- Repository stars
- 91
- License
- MIT
Implement DBSCAN clustering with custom distance metrics using scikit-learn's pairwise_distances.
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
# DBSCAN with Custom Distance Metrics
## Overview
DBSCAN can use custom distance metrics by computing a precomputed distance matrix or using `pairwise_distances` with a custom metric function.
## Key Concepts
### Custom Metric Function
A custom metric function takes two 1D arrays (two points) and returns a scalar distance:
```python
def custom_metric(u, v, shape_weight):
"""Compute weighted distance between two points."""
dx = u[0] - v[0]
dy = u[1] - v[1]
return np.sqrt((shape_weight * dx)**2 + ((2 - shape_weight) * dy)**2)
```
### Using with scikit-learn
For DBSCAN with a custom metric, use `metric='precomputed'` and pass a precomputed distance matrix:
```python
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.cluster import DBSCAN
# Compute precomputed distance matrix
distances = pairwise_distances(
points,
metric=custom_metric,
metric_params={'shape_weight': w}
)
# Run DBSCAN with precomputed distances
clusterer = DBSCAN(eps=epsilon, min_samples=min_samples, metric='precomputed')
labels = clusterer.fit_predict(distances)
```
## Implementation Pattern
```python
import numpy as np
…Read the whole file at its exact version.
How to install
mdr add cxcscmu/skilllearnbench/dbscan-custom-metrics@sha256:66acde56431f0222The 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_xv7g2i4rcaoiotpu)
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Versions
The registry has recorded 1 version of this file, committed on Apr 24, 2026.
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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_xv7g2i4rcaoiotpu GET https://markdownregistry.com/api/v1/resolve?ref=cxcscmu/skilllearnbench/dbscan-custom-metrics GET https://markdownregistry.com/api/v1/blob/66acde56431f022242d75d46f64d1944c2ff4f836a8c6bbb14e35efa15578dba
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