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/dodobrands/ai-hubWrote 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/agents/dodobrands/ai-hub/review-performance)<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>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 | $0.00044 | $0.01273 |
| Opus 5 | $0.00022 | $0.00636 |
| Sonnet 5 | $0.00009 | $0.00255 |
| Haiku 4.5 | $0.00004 | $0.00127 |
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.
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);
}
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.
- 6d ago First seen · 132 lines · 44 tokens per session scan A ffc64aacfb96
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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atomic-auditor
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bt6-pr-auditor
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Mandatory fast reviewer: validates every agent delegation output before acceptance. Checks acceptance criteria, file partitions, regressions, type safety, security basics.
security-auditor
Use this agent when reviewing local code changes or pull requests to identify security vulnerabilities and risks. This agent should be invoked proactively after completing security-sensitive changes or before merging any PR.