greedy-matching-evaluation SKILL.md skillA
greedy-matching-evaluation 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/greedy-matching-evaluation@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/greedy-matching-evaluation/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260424.d50cf5a, committed Apr 24, 2026
- Size
- 2,281 bytes
- Repository stars
- 91
- License
- MIT
Greedy matching algorithm to pair predicted cluster centroids with ground truth points for F1 and distance metrics.
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
# Greedy Matching for Clustering Evaluation
To evaluate clustering performance against ground truth (expert) points, we need to match predicted centroids to expert points.
## Algorithm
1. Compute all pairwise standard Euclidean distances between predicted centroids and expert points.
2. Filter pairs with distance > `max_dist` (e.g., 100 pixels).
3. Sort remaining pairs by distance (closest first).
4. Iteratively pick the closest pair, ensuring each centroid and each expert point is matched at most once.
## F1 Score Calculation
- **True Positives (TP)**: Number of matched pairs.
- **False Positives (FP)**: Number of unmatched predicted centroids.
- **False Negatives (FN)**: Number of unmatched expert points.
- `Precision = TP / (TP + FP)`
- `Recall = TP / (TP + FN)`
- `F1 = 2 * (Precision * Recall) / (Precision + Recall)` (Handle division by zero)
## Python Implementation
```python
import numpy as np
from scipy.spatial.distance import cdist
def evaluate_clustering(centroids, experts, max_dist=100):
if len(centroids) == 0:
return 0.0, np.nan
if len(experts) == 0:
return 0.0, np.nan
distances = cdist(centroids, experts, metric='euclidean')
…Read the whole file at its exact version.
How to install
mdr add cxcscmu/skilllearnbench/greedy-matching-evaluation@sha256:b5925a171c01541dThe 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_lrobt7bm6z7dz5s6)
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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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Source
cxcscmu/skilllearnbench · 91 stars · license MIT · pushed 2026-10-02 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_lrobt7bm6z7dz5s6 GET https://markdownregistry.com/api/v1/resolve?ref=cxcscmu/skilllearnbench/greedy-matching-evaluation GET https://markdownregistry.com/api/v1/blob/b5925a171c01541d46db1e3256bec42b235165d9bc444f2c6c34d3d21295b350
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