pareto-frontier-analysis SKILL.md skillA
pareto-frontier-analysis 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/pareto-frontier-analysis@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/pareto-frontier-analysis/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260424.d50cf5a, committed Apr 24, 2026
- Size
- 2,980 bytes
- Repository stars
- 91
- License
- MIT
Identify Pareto-optimal solutions from multi-objective optimization results.
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
# Pareto Frontier Analysis
## Overview
The Pareto frontier identifies non-dominated solutions where you cannot improve one objective without worsening another. For this task: maximize F1 score and minimize delta distance.
## Key Concepts
- **Dominated**: A solution is dominated if another solution has both better F1 AND better (lower) delta
- **Pareto-optimal**: A solution is not dominated by any other solution in the set
- **Pareto frontier**: The set of all Pareto-optimal solutions
## Implementation
```python
import numpy as np
import pandas as pd
def compute_pareto_frontier(results_df):
"""
Find Pareto-optimal solutions from results.
Args:
results_df: DataFrame with columns 'f1' and 'delta'
Returns:
pareto_indices: Boolean array marking Pareto-optimal solutions
"""
f1_scores = results_df['f1'].values
deltas = results_df['delta'].values
n = len(results_df)
is_pareto = np.ones(n, dtype=bool)
for i in range(n):
# Check if solution i is dominated
for j in range(n):
if i == j:
continue
# Solution j dominates solution i if:
…Read the whole file at its exact version.
How to install
mdr add cxcscmu/skilllearnbench/pareto-frontier-analysis@sha256:422b50d2b93cdae8The 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_zobyli5hxks3nzlq)
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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_zobyli5hxks3nzlq GET https://markdownregistry.com/api/v1/resolve?ref=cxcscmu/skilllearnbench/pareto-frontier-analysis GET https://markdownregistry.com/api/v1/blob/422b50d2b93cdae8becbde6eada81ad1406f950b3eb09dc8ee0c2f3b8a89bbed
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.