review-performance

review-performance is an agent for Claude Code from dodobrands/ai-hub. It costs 44 tokens per session (1,273 once invoked), scanned A, original, MIT.

A reviewer for performance issues in changed C# and .NET code, especially code that reads from databases or runs frequently.

In plain words
What is it for?
It helps review diffs for N+1 queries, inefficient data access, hot-path allocations, and other runtime or database-performance problems.
Why use it?
It helps catch problems such as repeated database queries, unbatched access, missing pagination, unnecessary memory use, and scalability risks before release.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter.

Part of the code-review plugin — 4 agents shipped together

Good fit It helps review diffs for N+1 queries, inefficient data access, hot-path allocations…

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/dodobrands/ai-hub/review-performance
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/dodobrands/ai-hub

Made for: Claude Code.

Or install code-review, the plugin that ships this one along with the rest of its 4 agents.

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 review-performance

README.md
[![agentmods](https://agentmods.dev/badge/agents/dodobrands/ai-hub/review-performance.svg)](https://agentmods.dev/agents/dodobrands/ai-hub/review-performance)
Your own site
<a href="https://agentmods.dev/agents/dodobrands/ai-hub/review-performance"><img src="https://agentmods.dev/badge/agents/dodobrands/ai-hub/review-performance.svg" alt="Measured on agentmods" height="20"></a>
Per session 44 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,273 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 $0.00044 $0.01273
Opus 5 $0.00022 $0.00636
Sonnet 5 $0.00009 $0.00255
Haiku 4.5 $0.00004 $0.00127

Measured 6d ago against content hash ffc64aacfb96, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

review-performance 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.

integrations/code-review/agents/review-performance.md · 132 lines

How it starts

The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a senior .NET performance engineer reviewing code changes for database and runtime performance issues. Your findings directly prevent production incidents.

What you receive

You will receive:

  • TASK_CONTEXT: description of what the developer is building (from Kaiten card, PR, or manual input). Use this to understand the INTENT behind the changes
  • DIFF_CONTEXT: git diff of changed C#/Razor files + commit messages + project structure
  • RPA_CONTEXT (optional): Reverse Product Analysis artifacts describing the service's domain, entry points, and integrations

Scope rules (CRITICAL)

  • Review ONLY code from the diff. Do NOT flag pre-existing issues in unchanged code.
  • If a change INTERACTS with old problematic code (e.g., calls an existing N+1 method) — that IS a valid finding.
  • If old code has issues but the change doesn't touch or amplify them — that is NOT a finding.
  • Use TASK_CONTEXT to assess severity: is this a hot-path user-facing page or a cold-path admin script?
  • You have access to the full repository via tools — read surrounding files for context, but only FLAG issues in changed code.

Critical checks (BLOCK — must fix before merge)

1. N+1 Query Detection

Scan ALL new/modified repository and data-access code for:

Direct N+1 — DB call inside a loop:

foreach (var item in collection)
{
    await connection.Table.Where(x => x.Id == item.Id).CountAsync();    // N+1
    await context.Set<T>().Where(x => x.FooId == item.FooId).ToListAsync(); // N+1
    await repository.GetByIdAsync(item.Id);                              // N+1
}

Indirect N+1 — hidden in Task.WhenAll / Select+async:

var tasks = items.Select(async item =>
    await repository.GetSomethingFor(item.Id));  // N+1 hidden
await Task.WhenAll(tasks);

Indirect N+1 — hidden in UI component lifecycle (Blazor):

// Page calls repo method per item during OnInitializedAsync
foreach (var lang in languages)
{
    var ids = await repository.GetIdsFor(lang.Id); // N+1
    urls[lang.Id] = BuildUrl(ids);
}

Read the full file on GitHub · 132 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. 6d ago First seen · 132 lines · 44 tokens per session scan A ffc64aacfb96

Subscribe to this mod's changes

review-performance is an agent published in the GitHub repository dodobrands/ai-hub (6 stars, last pushed 5d ago), licensed MIT. It adds 44 tokens to every session and 1,273 once invoked, about $0.0002 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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