optimize

Run measured optimization loops with Datadog, OTel-native, or local metrics.

1|Updated Apr 18, 2026
One-click install
npx skills add https://github.com/mopeyjellyfish/flywheel --skill optimize-mopeyjellyfish
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: optimize
Source: https://github.com/mopeyjellyfish/flywheel/tree/main/skills/optimize
Command: npx skills add https://github.com/mopeyjellyfish/flywheel --skill optimize-mopeyjellyfish

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Coordinates a measurement-first loop to identify and validate performance, reliability, and cost improvements, replacing guesswork with data-driven decisions.

Core Features & Use Cases

  • Establishes a structured optimization contract, including primary metric, guardrails, and measurement source.
  • Supports Datadog-backed, OTel-native, or local measurement surfaces, guiding serial experiments from baseline to winning change.
  • Integrates with Flywheel workflow handoffs ($fw:review/$fw:commit) and outputs an evidence bundle when applicable.

Quick Start

Start a measured optimization loop by selecting a target metric, choosing a measurement path, and applying the smallest verifiable change.

Frequently Asked Questions about optimize

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

FAQPage Schema
How do I use Datadog and OTel to establish a measured software performance optimization loop?

Measured software performance optimization uses a six-phase workflow to establish a primary metric and guardrails, defining a measurement contract using Datadog, OTel-native, or local measurement surfaces to validate latency and throughput improvements with data.

How do I prove software latency and throughput improvements with data instead of guesswork?

Proving software latency and throughput improvements requires a formal optimization contract that defines a primary metric, guardrails, and stop criteria, running serial experiments from baseline to winning change to replace guesswork with data-driven decisions.

Does this performance optimization workflow support local measurement surfaces or only Datadog?

The performance optimization workflow supports Datadog-backed, OTel-native, and local measurement surfaces, allowing you to define the measurement source, workload, rollout, and stop criteria regardless of your telemetry platform.

What is the best way to define a measurement contract for resource and cost constraints?

The best way to define a measurement contract for resource and cost constraints is to enforce a primary metric with guardrails, specifying the measurement source, workload, rollout strategy, and stop criteria before applying the smallest verifiable change.

Can I integrate measured optimization loops with Flywheel workflow handoffs?

Measured optimization loops integrate with Flywheel workflow handoffs through $fw:review and $fw:commit, outputting an evidence bundle and concise optimization brief when applicable to formalize data-driven performance decisions.

When should I not use a formal measurement contract for software optimization?

You should not use a formal measurement contract when you cannot define a primary metric or establish a reliable measurement source, as the six-phase workflow relies on measurable guardrails and stop criteria to validate improvements.