Ce Optimize (Grade A)Security-tested development skill for Claude AI. Grade A. Run metric-driven iterative optimization loops. Define a measurable goal, build measurement scaffolding, then run parallel experiments that tr

— (0)

Reviewed by Daniel Nikulshyn·Updated July 2026

Overview

Ce Optimize is a security-tested development skill for Claude AI that enables the run of metric-driven iterative optimization loops. It allows users to define a measurable goal, build measurement scaffolding, and then run parallel experiments that try various approaches. Each experiment is measured against hard gates and/or LLM-as-judge quality scores, and improvements are kept while converging toward the best solution. This skill is useful for optimizing clustering quality, search relevance, build performance, prompt quality, or any measurable outcome that benefits from systematic experimentation. It was inspired by Karpathy's autoresearch and generalized for multi-file code changes and non-ML domains.

Key features

Metric-driven iterative optimization
Parallel experiments with multiple approaches
Convergence toward best solution based on hard gates and quality scores
Support for multi-file code changes and non-ML domains

Pricing

Model: Free
Category: Skills
Rating: No reviews yet

Use cases

Optimizing Clustering Quality

Use Ce Optimize to improve the quality of clustering results by defining a measurable goal, such as minimizing cluster distance or maximizing cluster cohesion.

Improving Search Relevance

Apply Ce Optimize to enhance search relevance by running parallel experiments with different ranking algorithms and evaluating their performance against hard gates and quality scores.

Pros & Cons

Pros

Enables systematic experimentation for measurable outcomes
Allows for parallel experiments with multiple approaches
Converges toward the best solution based on hard gates and quality scores
Inspired by established research methods

Cons

Requires careful definition of measurable goals and scaffolding
May require significant computational resources for multiple experiments
Results are only durable if written to disk immediately
Complex to set up and manage for inexperienced users

Reviews

No reviews yet. Be the first!

Q&A

Is Ce Optimize suitable for non‑ML code changes?

Yes, the skill is generalized for multi‑file code changes and non‑ML domains, allowing systematic optimization beyond purely machine‑learning tasks.

Asked by Hiroshi Tanaka · Aug 6, 2025

Do I need special hardware or resources to run Ce Optimize?

Running multiple parallel experiments can be computationally intensive, so sufficient CPU/GPU resources are recommended, especially for large codebases or heavy metric calculations.

Asked by Oscar Lindqvist · Jul 30, 2025

How does Ce Optimize evaluate and select the best experiment?

Each parallel experiment is scored against hard gates and/or LLM-as-judge quality metrics; the system keeps improvements that meet the gates and converges toward the highest‑scoring solution.

Asked by Yuki Kobayashi · Jul 22, 2025

What kind of measurable goals can I set for Ce Optimize?

You can define any quantifiable objective such as clustering quality, search relevance, build performance, or prompt quality, as long as you can build measurement scaffolding to evaluate it.

Asked by Amara Chukwu · Jun 14, 2025

Ask a question

Free

Overview

Key features

Pricing

Use cases

Optimizing Clustering Quality

Improving Search Relevance

Pros & Cons

Pros

Cons

Reviews

Q&A

Ask a question

Skills alternatives

Manage Headers (Grade A)

Using Git Worktrees (Grade A)

Ga4 Bigquery Schema (Grade A)

Meta Capi (Grade A)

Callees (Grade A)

Test Module Name (Grade A)

Board Of Directors (Grade A)

Advpl Mvc Avancado (Grade A)

Trending now

Reducto AI

AdCrier

Biology AI

Pin AI