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Agent skill

Optimization Mastery

by xenitV1 in xenitV1/claude-code-maestro

2026-grade Cross-Domain Optimization. An agent skill from xenitV1/claude-code-maestro.

MITAuto-check: notes

Install Optimization Mastery

skills CLI
$ npx skills add xenitV1/claude-code-maestro --skill optimization-mastery -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install xenitV1/claude-code-maestro optimization-mastery --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/xenitV1/claude-code-maestro.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/optimization-mastery .claude/skills/optimization-mastery && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
optimization-mastery
GitHub stars
229
Token cost
~763 tokens
SKILL.md length
329 words
Files
1
Skills in repo
13
Repo updated
First seen
Licence
MIT

At a glance

2026-grade Cross-Domain Optimization. An agent skill from xenitV1/claude-code-maestro.

  • Works in 3 steps: The INP Threshold → Hydration Strategies → Asset Governance
  • SKILL.md covers 🎨 PROTOCOL 1: FRONTEND…, 🏗️ PROTOCOL 2: BACKEND…, 🤖 PROTOCOL 3: AI TOKEN… and 📂 COGNITIVE AUDIT CYCLE
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Optimization Mastery is an agent skill from xenitV1/claude-code-maestro. 2026-grade Cross-Domain Optimization. Expertise in Interaction to Next Paint (INP), Partial Hydration, UUIDv7 indexing, and AI Token Stewardship. Performance is a feature, not an afterthought.

Its SKILL.md is about 760 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The licence is MIT.

Example prompts

  • “/optimization-mastery”

Requirements

  • Pre-approved tools (allowed-tools): Read, Write, Edit, Glob, Grep, Bash

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. The INP Threshold
  2. Hydration Strategies
  3. Asset Governance

What it can do on your machine

Read from SKILL.md and the folder at commit 924315b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Glob
    • Grep
    • Bash

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Optimization Mastery loads about 763 tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 329 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~53
When it runs · the whole SKILL.md, loaded when a task matches
~763

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Write, Edit, Glob, Grep, Bash

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from xenitV1/claude-code-maestro at commit 924315b, republished under its MIT licence (© xenitV1). 329 words, ~763 tokens.

Download SKILL.mdSave it as .claude/skills/optimization-mastery/SKILL.md (or your agent's skills folder).
name
optimization-mastery
description
2026-grade Cross-Domain Optimization. Expertise in Interaction to Next Paint (INP), Partial Hydration, UUIDv7 indexing, and AI Token Stewardship. Performance is a feature, not an afterthought.
allowed-tools
Read, Write, Edit, Glob, Grep, Bash

<domain_overview>

⚡ OPTIMIZATION MASTERY: THE VELOCITY CORE

Philosophy: Efficiency is the highest form of quality. Minimal overhead, maximum impact. Performance-First is the only law. INTERACTION HYGIENE MANDATE (CRITICAL): Never prioritize synthetic benchmarks over real-world interaction smoothness. AI-generated code often misses Interaction to Next Paint (INP) bottlenecks caused by synchronous main-thread blocking. You MUST use scheduler.yield() or requestAnimationFrame for any complex DOM or state updates triggered by user events. Any implementation that risks "Layout Thrashing" or exceeds the 200ms INP threshold must be rejected. </domain_overview> <frontend_velocity>

🎨 PROTOCOL 1: FRONTEND PRECISION (INP & BUNDLE)

Aesthetics must be fast. Refer to frontend-design for visuals, but enforce these for speed.

  1. The INP Threshold:
    • Core Metric: Interaction to Next Paint (INP) MUST be < 200ms.
    • Action: Yield to main thread for heavy logic. Use scheduler.yield() or requestIdleCallback.
  2. Hydration Strategies:
    • Mandatory: Use Partial Hydration or Resumability (e.g. Qwik/Astro patterns).
    • Forbidden: Massive "Full Hydration" of static content.
  3. Asset Governance:
    • Images: Modern formats (AVIF/WebP) with srcset are mandatory.
    • Fonts: Only wght variable fonts; subsetted. </frontend_velocity> <backend_velocity>

🏗️ PROTOCOL 2: BACKEND VELOCITY (QUERY & DATA)

The backend must be a fortress of speed. Refer to backend-design for architecture.

  1. Identifier Strategy:
    • Mandatory: Use UUIDv7 for all primary keys in high-insert tables.
    • Rationale: Time-sortable IDs prevent B-tree fragmentation and boost insert speed by ~30%.
  2. Query Budget:
    • Max Latency: Sub-100ms for OLTP queries.
    • Action: Every index MUST be a "Covering Index" for critical read paths.
  3. Edge compute:
    • Offload logic to Edge Functions (Vercel/Cloudflare) to reduce Time-to-First-Byte (TTFB). </backend_velocity> <ai_token_stewardship>

🤖 PROTOCOL 3: AI TOKEN STEWARDSHIP (RESOURCE OPS)

AIs are expensive/slow. Optimize the "thought" itself.

  1. Context Window Management:
    • Action: Use "Context Folding" (summarizing history) to keep prompts under 4k tokens if possible.
  2. Credit-Based Execution:
    • Assign a "Token Budget" to complex tool calling phases.
  3. Caching:
    • Implement Semantic Caching for repetitive LLM queries. </ai_token_stewardship> <audit_and_reference>

📂 COGNITIVE AUDIT CYCLE

  1. Is INP < 200ms?
  2. Are primary keys UUIDv7?
  3. Is hydration partial/resumable?
  4. Is the token budget justified for this request? </audit_and_reference>

© xenitV1, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/optimization-mastery of xenitV1/claude-code-maestro.

Open the folder on GitHubat commit 924315b

Compare with similar skills

Optimization Mastery next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Optimization Mastery compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Optimization Mastery this skillxenitV1/claude-code-maestro229—~763Automated safety check: NotesMIT
Author Expertisethedaviddias/Front-End-Checklist74k—~438Automated safety check: PassMIT
Remotion Interactivityremotion-dev/remotion62k5 repos~4.7kAutomated safety check: PassCustom licence
Firecrawl Interact Integrationfirecrawl/firecrawl189k1 repos~731Automated safety check: PassISC
CLI Masterygithub/awesome-copilot40k2 repos~517Automated safety check: PassMIT
Grade Iteratealirezarezvani/claude-skills28k—~1kAutomated safety check: PassMIT

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Questions about Optimization Mastery

What does Optimization Mastery do?

2026-grade Cross-Domain Optimization. An agent skill from xenitV1/claude-code-maestro. Optimization Mastery is an agent skill from xenitV1/claude-code-maestro. 2026-grade Cross-Domain Optimization.

How do I install Optimization Mastery in Claude Code?

Run `npx skills add xenitV1/claude-code-maestro --skill optimization-mastery -a claude-code`. Or copy the skill folder (skills/optimization-mastery in xenitV1/claude-code-maestro) into .claude/skills/optimization-mastery in your project. Claude Code loads it when a task matches its description.

How do I install Optimization Mastery in Codex?

Run `npx skills add xenitV1/claude-code-maestro --skill optimization-mastery -a codex`. Or copy the skill folder (skills/optimization-mastery in xenitV1/claude-code-maestro) into .agents/skills/optimization-mastery in your project. Codex loads it when a task matches its description.

Can I use Optimization Mastery in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add xenitV1/claude-code-maestro --skill optimization-mastery -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/optimization-mastery, .gemini/skills/optimization-mastery, .github/skills/optimization-mastery and .opencode/skills/optimization-mastery in your project.

What does Optimization Mastery need to run?

SKILL.md names no scripts, command-line tools or credentials: Optimization Mastery is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Write, Edit, Glob, Grep, Bash.

Does Optimization Mastery access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Optimization Mastery safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Optimization Mastery use?

Optimization Mastery is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Optimization Mastery use?

About 763 tokens (SKILL.md is roughly 3.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Optimization Mastery?

Skills that share tags, products or a category with Optimization Mastery: Author Expertise (thedaviddias/Front-End-Checklist, 74k stars), Remotion Interactivity (remotion-dev/remotion, 62k stars), Firecrawl Interact Integration (firecrawl/firecrawl, 189k stars) and CLI Mastery (github/awesome-copilot, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Optimization Mastery?

xenitV1 (a GitHub user) maintains it in xenitV1/claude-code-maestro, which has 229 GitHub stars. The repository holds 13 skills in this directory. The repository was last updated on January 24, 2026.

Source: xenitV1/claude-code-maestro on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.