slate-ar-fast

Selects the hottest Slate v2 performance target and runs no-regression optimization loops.

16.5k|997|Updated Dec 2, 2019
One-click install
npx skills add https://github.com/udecode/plate --skill slate-ar-fast
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: slate-ar-fast
Source: https://github.com/udecode/plate/tree/main/.agents/skills/slate-ar-fast
Command: npx skills add https://github.com/udecode/plate --skill slate-ar-fast

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Optimizing a rich-text editor's performance without breaking typing, selection, or IME behavior is risky and time-consuming. This Skill automates the fastest-safe performance loop for Slate v2 by picking the highest-value target, running benchmarks, and enforcing correctness checks before keeping any optimization.

Core Features & Use Cases

  • Automatic Target Selection: When no target is named, it picks the hottest performance surface, prioritizing user-visible latency, then legacy regressions, then architectural fanout.
  • No-Regression Enforcement: Every kept optimization packet requires correctness evidence covering selection, typing, copy, paste, undo, IME, focus, and cursor placement.
  • Stop-Rule Discipline: Halts when the target threshold is met, legacy parity is reached, improvements plateau below 5%, or correctness blocks further work.
  • Use Case: A developer notices the Slate v2 editor feels sluggish during typing. They invoke this Skill, which loads current status, picks the typing latency target, runs benchmarked optimization packets, and stops once the threshold is met with all correctness checks green.

Quick Start

Ask the agent to make the Slate editor fast and let it pick the hottest performance target automatically.

Frequently Asked Questions about slate-ar-fast

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

FAQPage Schema
How do I optimize Slate editor performance without breaking typing?

Run a no-regression optimization loop that benchmarks one primary metric while checking correctness of selection, typing, copy, paste, undo, IME, focus, and cursor placement. Any packet that regresses behavior is logged as checks_failed or routed to patching.

How does the skill choose which performance target to optimize?

When no target is named, it picks the hottest target from current evidence: user-visible behavior latency first, then regressions versus legacy, then broad architectural fanout, and noisy raw microbenchmarks last.

When should I use slate-ar-fast versus slate-ar-perfect?

Use slate-ar-fast when you want the agent to pick the highest-value target and run a fastest-safe loop. The normal broad workflow is slate-ar-perfect on a specific surface, which may call this skill internally.

When does the performance optimization loop stop?

It stops when the target threshold is met with checks green, legacy parity is reached, three correctness-green packets improve the primary metric by less than 5%, the next win needs planning, or correctness blocks further work.

What happens if an optimization regresses editor behavior?

The packet is logged as checks_failed or routed to slate-patch for fixing. Optimizations are never kept by weakening behavior, delaying input, or hiding work behind debounce.