pareto-frontier SKILL.md skillA
pareto-frontier 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@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/pareto-frontier/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260424.d50cf5a, committed Apr 24, 2026
- Size
- 1,334 bytes
- Repository stars
- 91
- License
- MIT
Identify Pareto-optimal points from a set of multi-objective solutions.
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 Identification
A point is Pareto-optimal if no other point is better in all objectives. For this task, we want to maximize F1 and minimize Delta.
## Logic
A solution `A` dominates `B` if:
1. `A.F1 >= B.F1` AND `A.Delta <= B.Delta`
2. At least one inequality is strict.
## Python Implementation
```python
def is_pareto_efficient(costs):
"""
Find the pareto-efficient points
:param costs: An (n_points, n_costs) array where costs are to be MINIMIZED.
:return: A boolean array of length n_points indicating efficiency.
"""
is_efficient = np.ones(costs.shape[0], dtype=bool)
for i, c in enumerate(costs):
if is_efficient[i]:
# Keep any point that is better than 'c' in at least one attribute
# OR equal in all attributes (to handle duplicates)
is_efficient[is_efficient] = np.any(costs[is_efficient] < c, axis=1) | \
np.all(costs[is_efficient] == c, axis=1)
is_efficient[i] = True # And keep self
return is_efficient
# For Max F1 and Min Delta, transform F1:
# costs = np.array([[-f1, delta] for f1, delta in results])
…Read the whole file at its exact version.
How to install
mdr add cxcscmu/skilllearnbench/pareto-frontier@sha256:6cbadbd656a4f0eaThe 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_5rwfsec2u4dfexqa)
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Versions
The registry has recorded 1 version of this file, committed on Apr 24, 2026.
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (1334 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
cxcscmu/skilllearnbench · 91 stars · license MIT · pushed 2026-10-02 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_5rwfsec2u4dfexqa GET https://markdownregistry.com/api/v1/resolve?ref=cxcscmu/skilllearnbench/pareto-frontier GET https://markdownregistry.com/api/v1/blob/6cbadbd656a4f0eac25629dabc08f0a2d80d84be974c2a00ebe0fc16fb9e8398
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.