review-perf

review-perf is a skill for Claude Code from mgiovani/cc-arsenal. It costs 143 tokens per session (1,977 once invoked), scanned A, original, MIT.

A code-reading performance audit for database queries, algorithm efficiency, frontend slowdowns, and resource leaks. It reviews a pull request, commit, or codebase and produces a report ranked by severity.

In plain words
What is it for?
Use it to look for issues such as repeated database queries, inefficient algorithms, frontend bottlenecks, and resources that are not released.
Why use it?
It helps find likely causes of slow or wasteful code without changing the project. It does not run benchmarks, profiling, or load tests, so important findings should be checked with runtime measurements.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter. Also seen: mentions subagents; names the TodoWrite tool.

Part of the cc-arsenal-review plugin — 7 skills shipped together , and of cc-arsenal

Good fit Use it to look for issues such as repeated database queries, inefficient algorithms, frontend bottlenecks, and resources that are not released.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mgiovani/cc-arsenal/review-perf
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.

Any agent
npx skills add mgiovani/cc-arsenal --skill review-perf
Clone the repo
git clone --depth 1 https://github.com/mgiovani/cc-arsenal

Made for: Claude Code.

Or install cc-arsenal-review, the plugin that ships this one along with the rest of its 7 skills.

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-perf

README.md
[![agentmods](https://agentmods.dev/badge/skills/mgiovani/cc-arsenal/review-perf/github.svg)](https://agentmods.dev/skills/mgiovani/cc-arsenal/review-perf)
Your own site
<a href="https://agentmods.dev/skills/mgiovani/cc-arsenal/review-perf"><img src="https://agentmods.dev/badge/skills/mgiovani/cc-arsenal/review-perf/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 review-perf

Your own site · 80×15
<a href="https://agentmods.dev/skills/mgiovani/cc-arsenal/review-perf"><img src="https://agentmods.dev/badge/skills/mgiovani/cc-arsenal/review-perf.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 143 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,977 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 $0.00143 $0.01977
Opus 5 $0.00072 $0.00988
Sonnet 5 $0.00029 $0.00395
Haiku 4.5 $0.00014 $0.00198

Measured 10d ago against content hash cb7732df9e40, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

review-perf 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 10d 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.

skills/review-perf/SKILL.md · 131 lines

How it starts

The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Performance Review

Comprehensive performance analysis targeting database query inefficiencies, algorithmic complexity issues, frontend bottlenecks, and resource leaks. Analysis only - identifies problems and suggests optimizations without making code changes.

Constraints

  • Analysis only - never modifies, fixes, or commits code, even if asked to "also fix these" mid-run; report the findings and stop
  • Static analysis - no runtime profiling, no benchmarking, no load testing
  • Pattern-based - Big O and impact estimates are approximate; may miss context-specific issues a profiler would catch
  • Not exhaustive - does not guarantee 100% detection; profiling is recommended before acting on critical findings
  • Read before claiming - never report a finding in a file that has not actually been read; every finding cites the specific file path and line number it came from
  • No invented numbers - counts, query-multiplication estimates, and Big O claims must trace back to code actually read, not generic examples copied from the report template
  • Diff-scope confinement - for a PR or commit review, never grep or read a file the diff didn't touch, and never let a pre-existing issue in a touched file masquerade as a PR finding; see Phase 0/2

Scan Workflow

Phase 0: Determine Scan Scope

Parse arguments:

  • <pr_number>: scan only files changed in that PR (e.g. 123, #123)
  • <commit_sha>: scan only files changed in that commit
  • --all or no args: scan entire codebase
  • --scope [database|algorithm|frontend|resources|backend]: focus on specific categories (backend = database + algorithm + resources, excludes frontend)

If PR or commit specified, pull the full diff - not just the file list - so hunk ranges are available for Phase 2's confinement check:

# For PR
gh pr diff <pr_number>

# For commit
git diff-tree -p <commit_sha>

From that diff, extract two things and carry both into Phase 2:

  1. Changed-file list: the paths after each +++ b/ line.
  2. Hunk ranges per file: each @@ -a,b +c,d @@ header gives the new-file line range c to c+d-1 for that hunk. A file can have multiple hunks.

Read the full file on GitHub · 131 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 10d ago First seen · 131 lines · 143 tokens per session scan A cb7732df9e40

Subscribe to this mod's changes

review-perf is a skill published in the GitHub repository mgiovani/cc-arsenal (8 stars, last pushed yesterday), licensed MIT. It adds 143 tokens to every session and 1,977 once invoked, about $0.0007 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.

Related

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