ce-optimize

A flexible, multi-interface approach for building LLM applications.

10|Updated Dec 13, 2016
One-click install
npx skills add https://github.com/vitallium/dotfiles --skill ce-optimize-vitallium
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ce-optimize
Source: https://github.com/vitallium/dotfiles/tree/main/dot_agents/skills/ce-optimize
Command: npx skills add https://github.com/vitallium/dotfiles --skill ce-optimize-vitallium

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

metric-driven optimization over software artifacts, enabling teams to define goals, run parallel experiments, measure outcomes, and converge on the best solution.

Core Features & Use Cases

  • Define measurable goals and run iterative experiments with deterministic gates (hard metrics) or LLM-based judgments to select the best improvements.
  • Maintain a durable experiment log and strategy digest to ensure reproducibility, crash recovery, and transparent branch merges.
  • Supports both serial and parallel execution, per-experiment worktrees, dependency management, and structured prompts for hypothesis work.

Quick Start

Define a spec with a measurable target and run a first serial baseline to validate the harness.

Frequently Asked Questions about ce-optimize

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate metric-driven optimization experiments for software engineering?▼

Automate metric-driven optimization by defining a spec with a measurable target, running parallel experiments, and evaluating outcomes using deterministic hard gates or LLM-as-judge scores to converge on the best solution.

What is the best way to improve search relevance and clustering quality systematically?▼

Systematically improve search relevance and clustering quality by running iterative optimization loops that define baselines, apply sampling strategies, and evaluate results against structured scoring rubrics.

Can I run parallel optimization experiments and recover from crashes during execution?▼

Yes, you can run parallel optimization experiments safely because the system maintains a durable experiment-log and strategy digest that ensure reproducibility and provide crash resilience during execution.

How do I evaluate prompt quality improvements without manual review?▼

Evaluate prompt quality automatically by defining scoring rubrics and using LLM-as-judge scores to assess measurable outcomes, eliminating manual review while ensuring systematic experimentation.

Does this optimization approach support per-experiment dependency management?▼

Yes, the optimization approach supports per-experiment worktrees and dependency management, allowing both serial and parallel execution to run isolated tests without conflicts.

What is a strategy digest and how does it help with branch merges in optimization?▼

A strategy digest is a structured record that ensures reproducibility and transparent branch merges by maintaining a durable experiment-log of baselines, hypotheses, and evaluation results across iterative optimization loops.