greedy-bipartite-matching SKILL.md skillA
greedy-bipartite-matching 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-bipartite-matching@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/greedy-bipartite-matching/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260424.d50cf5a, committed Apr 24, 2026
- Size
- 3,588 bytes
- Repository stars
- 91
- License
- MIT
Implement greedy bipartite matching for pairing cluster centroids with expert annotations.
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 Bipartite Matching
## Overview
Greedy matching finds pairs between two sets of points by iteratively selecting the closest unmatched pair until no more matches are possible below a distance threshold.
## Algorithm
1. Compute pairwise distances between cluster centroids and expert points
2. Repeatedly find the closest pair (centroid, expert point) below max_distance
3. Mark both as matched and remove from consideration
4. Continue until no more valid pairs can be found
## Implementation
```python
import numpy as np
def greedy_match(centroids, expert_points, max_distance=100):
"""
Match centroids to expert points using greedy algorithm.
Args:
centroids: (n, 2) array of cluster centroids
expert_points: (m, 2) array of expert annotations
max_distance: Maximum distance for a valid match
Returns:
matched_pairs: List of (centroid_idx, expert_idx, distance) tuples
"""
centroids = np.asarray(centroids)
expert_points = np.asarray(expert_points)
if len(centroids) == 0 or len(expert_points) == 0:
return []
# Compute Euclidean distances (always standard Euclidean for matching)
…Read the whole file at its exact version.
How to install
mdr add cxcscmu/skilllearnbench/greedy-bipartite-matching@sha256:b698dbcb6d81ba21The 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_urmkpbk3luythtfx)
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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_urmkpbk3luythtfx GET https://markdownregistry.com/api/v1/resolve?ref=cxcscmu/skilllearnbench/greedy-bipartite-matching GET https://markdownregistry.com/api/v1/blob/b698dbcb6d81ba2184c4d13c826312d04555f1882c5a970dd769c8e6a65eaaef
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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.