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performance-analysis

Name: Performance Analysis
Author: voidful

// Use when investigating slow execution, high memory usage, or excessive token consumption. Systematic measurement before optimization.

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updated:2026年5月11日 08:45

SKILL.md

readonly

name	performance-analysis
description	Use when investigating slow execution, high memory usage, or excessive token consumption. Systematic measurement before optimization.
version	1.0.0
author	Aixlarity
license	Apache-2.0
metadata	{"aixlarity":{"tags":["performance","optimization","profiling","tokens"],"related_skills":["systematic-debugging","code-review"]}}

Performance Analysis

Core Principle

Measure first, optimize second. Never optimize based on intuition. Profiling data decides what to fix.

Two Performance Dimensions in Agent Systems

1. Runtime Performance (Rust code)

Standard profiling applies:

# Compile with debug symbols for profiling
cargo build --release
# Use system profiler (Linux)
perf record ./target/release/aixlarity exec "task"
perf report

2. Token Performance (LLM usage)

This is unique to agent systems. Every token costs money and latency.

Metrics to track:

Prompt tokens per turn — Is the context bloated?
Completion tokens per turn — Is the model over-generating?
Tool calls per task — Are there wasted tool calls (reading irrelevant files)?
Total cost per task — Aixlarity tracks this automatically per session.

Common Token Waste Patterns

Context stuffing: Injecting too many files via @{...} when only one is needed.
Skill catalog bloat: Too many skills listed in the prompt. Use Progressive Disclosure.
History accumulation: Long conversations without auto-compact triggering.
Redundant tool calls: Agent reads the same file multiple times across turns.

When to Use

When a task takes noticeably more tokens than expected.
When auto-compact triggers frequently (sign of context pressure).
Before adding new context sources to the prompt assembly.

related-skills.json

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package.json

"author": "voidful"

"repository": "voidful/Aixlarity"

GitHub リポジトリを開く Creator のリポジトリを見る

$ install --global

$ download --local

Manusで実行

$ useful --forSOC

ソフトウェア開発者コンピュータ・数学職15-1252L4

name	performance-analysis
description	Use when investigating slow execution, high memory usage, or excessive token consumption. Systematic measurement before optimization.
version	1.0.0
author	Aixlarity
license	Apache-2.0
metadata	{"aixlarity":{"tags":["performance","optimization","profiling","tokens"],"related_skills":["systematic-debugging","code-review"]}}

Performance Analysis

Core Principle

Measure first, optimize second. Never optimize based on intuition. Profiling data decides what to fix.

Two Performance Dimensions in Agent Systems

1. Runtime Performance (Rust code)

Standard profiling applies:

# Compile with debug symbols for profiling
cargo build --release
# Use system profiler (Linux)
perf record ./target/release/aixlarity exec "task"
perf report

2. Token Performance (LLM usage)

This is unique to agent systems. Every token costs money and latency.

Metrics to track:

Prompt tokens per turn — Is the context bloated?
Completion tokens per turn — Is the model over-generating?
Tool calls per task — Are there wasted tool calls (reading irrelevant files)?
Total cost per task — Aixlarity tracks this automatically per session.

Common Token Waste Patterns

Context stuffing: Injecting too many files via @{...} when only one is needed.
Skill catalog bloat: Too many skills listed in the prompt. Use Progressive Disclosure.
History accumulation: Long conversations without auto-compact triggering.
Redundant tool calls: Agent reads the same file multiple times across turns.

When to Use

When a task takes noticeably more tokens than expected.
When auto-compact triggers frequently (sign of context pressure).
Before adding new context sources to the prompt assembly.

performance-analysis

Performance Analysis

Core Principle

Two Performance Dimensions in Agent Systems

1. Runtime Performance (Rust code)

2. Token Performance (LLM usage)

Common Token Waste Patterns

When to Use

このリポジトリの他の Skills

このリポジトリの他の Skills

Performance Analysis

Core Principle

Two Performance Dimensions in Agent Systems

1. Runtime Performance (Rust code)

2. Token Performance (LLM usage)

Common Token Waste Patterns

When to Use