neo-optimize

neo-optimize is a command for Claude Code from Parslee-ai/neo. It costs 60 tokens per session (421 once invoked), scanned A, original, Apache-2.0.

A command that asks Neo to examine a measured performance problem and suggest improvements. Performance profiling or benchmarking means measuring where code spends time.

In plain words
What is it for?
Analyzing a slow function, algorithm, or file; identifying likely bottlenecks; and preparing changes to measure before and after.
Why use it?
It turns evidence about a slow part of the code into ranked suggestions, while making clear that the suggestions are not themselves benchmark results.

Command for Claude Code

Written for Claude Code: a Claude Code command (commands/*.md).

Part of the neo plugin — 9 skills, 18 commands, 1 agent, 1 hook shipped together

Good fit Analyzing a slow function, algorithm, or file; identifying likely bottlenecks; and preparing changes to measure before and after.

Compare 6 commands from other repositories ↓
Install with agentmods
npx agentmods add commands/parslee-ai/neo/neo-optimize
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.

Clone the repo
git clone --depth 1 https://github.com/Parslee-ai/neo

Made for: Claude Code.

Or install neo, the plugin that ships this one along with the rest of its 9 skills, 18 commands, 1 agent, 1 hook.

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 neo-optimize

README.md
[![agentmods](https://agentmods.dev/badge/commands/parslee-ai/neo/neo-optimize.svg)](https://agentmods.dev/commands/parslee-ai/neo/neo-optimize)
Your own site
<a href="https://agentmods.dev/commands/parslee-ai/neo/neo-optimize"><img src="https://agentmods.dev/badge/commands/parslee-ai/neo/neo-optimize.svg" alt="Measured on agentmods" height="20"></a>
Per session 60 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 421 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 $0.00060 $0.00421
Opus 5 $0.00030 $0.00211
Sonnet 5 $0.00012 $0.00084
Haiku 4.5 $0.00006 $0.00042

Measured 8d ago against content hash f7d1b7341c39, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

neo-optimize 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 8d 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.

commands/neo-optimize.md · 64 lines

What it actually says

Get optimization suggestions from Neo.

Usage

/neo-optimize <file path or function name>

Description

Use this command to get performance optimization recommendations from Neo using semantic analysis and past optimization patterns.

Examples

/neo-optimize process_large_dataset function

/neo-optimize src/data/processor.py

/neo-optimize the search algorithm

What Happens

Neo will:

  1. Analyze the code for algorithmic complexity
  2. Identify bottlenecks and inefficiencies
  3. Search memory for similar optimization patterns
  4. Suggest improvements with confidence scores

Presentation

Invoke with --json and follow the agent's communication protocol (agents/neo.md). For optimization specifically:

  • Lead with orchestrator.summary, then the identified bottleneck.
  • Show the evidence for the bottleneck — the complexity argument or the code path Neo pointed at. An optimization claim with no evidence is a guess wearing a confidence score.
  • State the expected impact and its basis. If Neo estimated rather than measured, say "estimated". Neo does not execute or benchmark anything.
  • Recommend the user measure before and after. Any suggested benchmark commands are advisory and are never run by Neo.
  • Surface orchestrator.cautions, especially correctness risks — a faster wrong answer is a regression.

Parameters

This command uses read-only advise mode. Suggested benchmarks and commands are advisory and are never executed by Neo.

  • <target> - File path or function name (required)

Optionally include performance requirements (e.g., "needs <2s for 10k records")

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. 8d ago First seen · 64 lines · 60 tokens per session scan A f7d1b7341c39

Subscribe to this mod's changes

neo-optimize is a command published in the GitHub repository Parslee-ai/neo (16 stars, last pushed today), licensed Apache-2.0. It adds 60 tokens to every session and 421 once invoked, about $0.0003 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 2026-08-30.