advanced-performance-tuning

advanced-performance-tuning is a skill for Claude Code, Codex from LgrappaG/Workflows-Agents. It costs 12 tokens per session (252 once invoked), scanned A, original, MIT.

A guide for investigating and improving software performance through detailed profiling and testing. Profiling measures where an application spends time or resources so bottlenecks can be identified.

In plain words
What is it for?
Use it to profile slow systems, remove bottlenecks, test edge cases, and validate performance changes before deployment.
Why use it?
It helps locate genuine performance problems and verify that optimizations improve results without damaging maintainability.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to profile slow systems, remove bottlenecks, test edge cases, and…

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/lgrappag/workflows-agents/advanced-performance-tuning
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add LgrappaG/Workflows-Agents --skill advanced-performance-tuning
Clone the repo
git clone --depth 1 https://github.com/LgrappaG/Workflows-Agents

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for advanced-performance-tuning

README.md
[![agentmods](https://agentmods.dev/badge/skills/lgrappag/workflows-agents/advanced-performance-tuning.svg)](https://agentmods.dev/skills/lgrappag/workflows-agents/advanced-performance-tuning)
Your own site
<a href="https://agentmods.dev/skills/lgrappag/workflows-agents/advanced-performance-tuning"><img src="https://agentmods.dev/badge/skills/lgrappag/workflows-agents/advanced-performance-tuning.svg" alt="Measured on agentmods" height="20"></a>
Per session 12 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 252 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00012 $0.00252
Opus 5 $0.00006 $0.00126
Sonnet 5 $0.00002 $0.00050
Haiku 4.5 $0.00001 $0.00025

Measured 6d ago against content hash ed1dfb07fb50, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

advanced-performance-tuning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/advanced-performance-tuning/SKILL.md · 49 lines

What it actually says

Advanced Performance Tuning

Perform deep performance tuning for extreme optimization

Risk Level

HIGH

Core Rules

  • Profile thoroughly
  • validate improvements
  • Test thoroughly before deploying

Response Pattern

When Using This Skill

  1. Profile deeply
  2. Validate the implementation
  3. Test edge cases and error conditions
  4. Ensure performance meets requirements

Usage Contexts

  • Extreme optimization
  • bottleneck elimination

What NOT to Do

  • Premature optimization
  • maintainability loss
  • Deploy without testing

Key Requirements

  • Understand the use cases before application
  • Follow the documented response pattern
  • Validate results in the target environment
  • Monitor for performance impact

Further Learning

Review related skills and documentation for deeper understanding of related systems and best practices.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 6d ago First seen · 49 lines · 12 tokens per session scan A ed1dfb07fb50

Subscribe to this mod's changes

advanced-performance-tuning is a skill published in the GitHub repository LgrappaG/Workflows-Agents (2 stars, last pushed 4mo ago), licensed MIT. It adds 12 tokens to every session and 252 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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