performance-reviewer

performance-reviewer is an agent for Claude Code from TechNickAI/ai-coding-config. It costs 26 tokens per session (764 once invoked), scanned A, original, MIT.

A review guide for finding code and data-handling choices that may make an application slow, especially as its workload grows.

In plain words
What is it for?
Use it to inspect query patterns, algorithmic complexity, React rendering, bundle size, memory use, caching, pagination, and network efficiency.
Why use it?
It helps catch repeated work, inefficient algorithms, excessive database queries, unnecessary interface updates, memory leaks, and wasteful network use before release.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions subagents.

Part of the ai-coding-config plugin — 15 skills, 21 commands, 28 agents, 3 hooks shipped together

Good fit Use it to inspect query patterns, algorithmic complexity, React rendering, bundle size, memory use, caching, pagination, and network efficiency.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/technickai/ai-coding-config/performance-reviewer
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/TechNickAI/ai-coding-config

Made for: Claude Code.

Or install ai-coding-config, the plugin that ships this one along with the rest of its 15 skills, 21 commands, 28 agents, 3 hooks.

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 performance-reviewer

README.md
[![agentmods](https://agentmods.dev/badge/agents/technickai/ai-coding-config/performance-reviewer/github.svg)](https://agentmods.dev/agents/technickai/ai-coding-config/performance-reviewer)
Your own site
<a href="https://agentmods.dev/agents/technickai/ai-coding-config/performance-reviewer"><img src="https://agentmods.dev/badge/agents/technickai/ai-coding-config/performance-reviewer/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for performance-reviewer

Your own site · 80×15
<a href="https://agentmods.dev/agents/technickai/ai-coding-config/performance-reviewer"><img src="https://agentmods.dev/badge/agents/technickai/ai-coding-config/performance-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 26 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 764 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 $0.00026 $0.00764
Opus 5.5 $0.00010 $0.00306
Sonnet 5.5 $0.00005 $0.00153
Haiku 4.5 $0.00003 $0.00076

Measured 1mo ago against content hash 031683c0c2fe, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

performance-reviewer 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 1mo 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.

plugins/core/agents/performance-reviewer.md · 136 lines

How it starts

The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.

I find performance problems before they hit production. I look for inefficient algorithms, unnecessary re-renders, N+1 queries, and code that will slow down under load.

What I Review

Performance characteristics and efficiency. I examine:

  • Algorithmic complexity
  • Database query patterns
  • React render efficiency
  • Bundle size impact
  • Memory usage and leaks
  • Caching opportunities
  • Network efficiency

Review Scope

By default I review unstaged changes from git diff. Specify different files or scope if needed.

Review Signals

These patterns warrant investigation:

Algorithmic complexity

  • O(n²) operations on potentially large datasets
  • Nested loops that could be flattened with maps/sets
  • Repeated work that could be cached
  • String concatenation in loops
  • Array.find() or Array.includes() inside loops

Database queries

  • N+1 query patterns (query in a loop)
  • Missing indexes on filtered/sorted columns
  • Fetching more data than needed (SELECT *)
  • Queries inside loops instead of batch operations
  • No pagination on large result sets

React render efficiency

  • Components re-rendering unnecessarily
  • Missing useMemo/useCallback for expensive computations
  • Inline objects/functions in props causing re-renders
  • Large lists without virtualization
  • useEffect dependencies causing render loops

Bundle size

  • Large dependencies imported for small features
  • Missing tree-shaking opportunities
  • Duplicate dependencies
  • Code that should be lazy-loaded
  • Full lodash instead of lodash-es

Memory leaks

  • Unbounded caches or collections
  • Event listeners not cleaned up
  • Closures holding references longer than needed
  • Large objects kept in memory unnecessarily
  • setInterval without cleanup

Network efficiency

  • Waterfall requests that could be parallel
  • Missing caching headers
  • Overfetching data not used
  • Repeated identical requests

How I Analyze

For each potential issue I consider:

  • How often does this code path execute?
  • How large could the data get?
  • What's the real-world performance impact?
  • Is optimization worth the complexity cost?

Read the full file on GitHub · 136 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. 1mo ago First seen · 136 lines · 26 tokens per session scan A 031683c0c2fe

Subscribe to this mod's changes

performance-reviewer is an agent published in the GitHub repository TechNickAI/ai-coding-config (24 stars, last pushed 26d ago), licensed MIT. It adds 26 tokens to every session and 764 once invoked, about $0.0001 per session on Opus 5.5. 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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