pareto-optimization SKILL.md skillA
pareto-optimization 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-optimization@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-sonnet-4-6/dbscan-parameter-tuning/pareto-optimization/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260424.d50cf5a, committed Apr 24, 2026
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- 5,315 bytes
- Repository stars
- 91
- License
- MIT
Compute Pareto-optimal frontiers from multi-objective optimization results, including dominance checking and frontier extraction.
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 Optimization
## Overview
A solution is **Pareto-optimal** if no other solution dominates it — i.e., no other solution is at least as good on all objectives and strictly better on at least one.
## When to Use
- Multi-objective optimization with conflicting goals
- Finding trade-off solutions (e.g., maximize F1 vs minimize error)
- Grid search results where you want the best trade-off set
## Core Implementation
```python
import numpy as np
import pandas as pd
def is_pareto_dominated(costs, i):
"""
Check if solution i is dominated by any other solution.
costs: 2D array where each row is [obj1_to_minimize, obj2_to_minimize, ...]
Returns True if solution i is dominated.
"""
for j in range(len(costs)):
if j == i:
continue
# j dominates i if j is <= i on all objectives and < i on at least one
if all(costs[j] <= costs[i]) and any(costs[j] < costs[i]):
return True
return False
def get_pareto_frontier(df, objectives):
"""
Extract Pareto-optimal rows from a DataFrame.
Args:
df: pandas DataFrame with results
objectives: dict mapping column names to 'min' or 'max'
…Read the whole file at its exact version.
How to install
mdr add cxcscmu/skilllearnbench/pareto-optimization@sha256:6f5d07789ce02f02The 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_n7ozkgqz53qxlizk)
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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_n7ozkgqz53qxlizk GET https://markdownregistry.com/api/v1/resolve?ref=cxcscmu/skilllearnbench/pareto-optimization GET https://markdownregistry.com/api/v1/blob/6f5d07789ce02f020e609f622a2e68740c476ee8aedd5e62e668fcbbd5dc8023
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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.
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