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.
git clone --depth 1 https://github.com/Parslee-ai/neoWrote 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.
[](https://agentmods.dev/commands/parslee-ai/neo/neo-optimize)<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>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.
| Model | Per session | Once 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 |
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.
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:
- Analyze the code for algorithmic complexity
- Identify bottlenecks and inefficiencies
- Search memory for similar optimization patterns
- 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")
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.
- 8d ago First seen · 64 lines · 60 tokens per session scan A f7d1b7341c39
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.
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