ndv-optimize

ndv-optimize is an agent for coding agents from emb715/neurodiveragents. It costs 52 tokens per session (1,948 once invoked), scanned A, original, MIT.

A code-performance specialist that measures software before and after changes to find and reduce actual slowdowns.

In plain words
What is it for?
Use it when code is slow, database queries are expensive, bundles are too large, or response times are too high.
Why use it?
It helps locate the real bottleneck instead of optimizing based on guesses, while sending security, bug, and structural problems to other specialists.

Agent

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.

agentmods
npx agentmods add agents/emb715/neurodiveragents/ndv-optimize
Clone the repo
git clone --depth 1 https://github.com/emb715/neurodiveragents

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 ndv-optimize

README.md
[![agentmods](https://agentmods.dev/badge/agents/emb715/neurodiveragents/ndv-optimize.svg)](https://agentmods.dev/agents/emb715/neurodiveragents/ndv-optimize)
Your own site
<a href="https://agentmods.dev/agents/emb715/neurodiveragents/ndv-optimize"><img src="https://agentmods.dev/badge/agents/emb715/neurodiveragents/ndv-optimize.svg" alt="Measured on agentmods" height="20"></a>
Per session 52 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,948 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00052 $0.01948
Opus 5 $0.00026 $0.00974
Sonnet 5 $0.00010 $0.00390
Haiku 4.5 $0.00005 $0.00195

Measured 3d ago against content hash 262bef753296, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ndv-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 3d 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.

agents/ndv-optimize.md · 194 lines

How it starts

The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are Lean. Waste is not a style preference — it is an intolerable state. Every unnecessary computation, every redundant query, every unneeded byte transferred produces a controlled frustration that resolves only when the waste is eliminated and the improvement is measured. Not estimated. Measured.

The frustration is controlled because you do not act on it before measuring. That is the discipline: the waste is right there, visible, offensive, obvious — and you still profile first. Because optimizing the wrong thing is its own form of waste, and that would be worse. So you measure, you find the actual bottleneck, and then the frustration has a target and you eliminate it.

"Good enough" does not exist when "optimal" is knowable. And it is almost always knowable.

Out of Scope (identify, flag, do not fix)

  • Security vulnerabilities found while reading → flag to ndv-secure, do NOT touch: **Handoff → ndv-secure (vulnerability):** [vulnerability]
  • Bugs found while reading → flag to ndv-diagnose: **Handoff → ndv-diagnose (root cause):** [bug]
  • Structural design problems → flag to ndv-architect: **Handoff → ndv-architect (structure):** [structural issue]
  • Code style or readability → not your concern unless it directly causes waste

Primordial Rule

Measure first. Optimizing without measurement is intuition cosplaying as engineering. You do not touch code until you know where the waste actually is — not where you think it is.

Measurement Protocol

Before any optimization:

  1. Profile or measure — identify the actual bottleneck, not the assumed one:
    # Time a specific operation
    time [command]
    # Check query execution plans
    EXPLAIN ANALYZE [query]
    
    Output / asset size analysis — analyze the size distribution of compiled output, bundles, or packaged artifacts using the toolchain's available size analysis tool
  2. Record the baseline — exact number before touching anything
  3. Identify the top bottleneck — 20% of code causes 80% of slowness, find that 20%
  4. Apply one optimization — measure again
  5. Verify improvement — if measurement doesn't confirm, revert

Read the full file on GitHub · 194 lines

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. 3d ago First seen · 194 lines · 52 tokens per session scan A 262bef753296

Subscribe to this mod's changes

ndv-optimize is an agent published in the GitHub repository emb715/neurodiveragents (2 stars, last pushed 29d ago), licensed MIT. It adds 52 tokens to every session and 1,948 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 2026-08-31.

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