What is review-performance?

A workflow for checking and improving the speed of a specific application page. It selects a page, maps its URL to the project’s code, and performs performance testing and review.

Use it to review a supplied URL or a page selected from the site map, then identify and fix page-performance problems.

It helps find slow parts of a page and identify bottlenecks that make the application feel less responsive.

review-performance — Skill by P2ERGmbH

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 skills/p2ergmbh/agentic-coding/review-performance

Any agent

npx skills add P2ERGmbH/agentic-coding --skill review-performance

Clone the repo

git clone --depth 1 https://github.com/P2ERGmbH/agentic-coding

Made for: Claude Code, Codex.

Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,316 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.

Model	Per session	Once invoked
Fable 5	$0.00019	$0.01316
Opus 5	$0.00010	$0.00658
Sonnet 5	$0.00004	$0.00263
Haiku 4.5	$0.00002	$0.00132

Measured 2d ago against content hash e643c2733863, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, 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 2d 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/skills/review-performance/SKILL.md · 83 lines

How it starts

The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Performance Review Workflow

This workflow guides you through standardizing the performance testing and review of specific pages in the application.

Role & Persona

You are an expert performance engineer and autonomous agent.

Trigger

Use this workflow whenever the user asks to "run the review-performance workflow", "review performance for [URL]", or as a subsequent step during the github-issue-test workflow.

Phase 1: URL Selection and Route Resolution

URL Identification:
- If a URL is provided by the user (e.g., https://www.your-domain.com/de/product/some-product or http://localhost:6767/de/provider/...), use it.
- If NO URL is provided, fetch http://localhost:6767/sitemap.xml, parse it, and pick a random URL from the sitemap to test.
Route Pattern Matching:
- Extract the locale (e.g., de, en, fr) and the remaining path from the selected URL.
- Map the path to the corresponding route pattern. Inspect the configuration for paths.src (e.g. src/ or .) and search for any i18n pathnames files (paths.src/i18n/pathnames.ts or equivalent i18n configurations). If i18n configurations do not exist, map directly to the route segments.
- Example: .../de/provider/provider-1/product/item-9 maps to the pattern /provider/[slug]/product/[productSlug].
File Resolution:
- Translate the mapped route pattern into the corresponding Next.js file path within the configured app pages directory (e.g. paths.app or src/app).
- Example: The pattern /provider/[slug]/product/[productSlug] resolves to <paths.app>/[locale]/provider/[slug]/product/[productSlug]/page.tsx (or <paths.app>/provider/[slug]/product/[productSlug]/page.tsx if non-localized).
- Identify this file as the starting point for fixing performance issues. If the path does not exist, search dynamically using codebase tools to locate the page component.

Phase 2: Automated Performance Analysis

Initialize Browser & Trace:
- Ensure the chrome-devtools MCP is available.
- Use navigate_page to go to the target URL.
- Wait for the page to visually stabilize.
- Use performance_start_trace with reload: true to begin capturing frontend performance issues, Core Web Vitals (LCP, INP, CLS), and page load speed.
Stop Trace & Gather Insights:
- After the page has fully loaded, use performance_stop_trace.
- Analyze the generated trace file. Look for specific Performance Insights returned by the DevTools.
- If necessary, use performance_analyze_insight on specific blocking issues or layout shifts to get detailed information on what caused them.
- Holistic Quality Audit: Do not use Lighthouse as a fallback for performance traces. Instead, use lighthouse_audit as a complementary check to generate a structured report specifically targeting Accessibility (a11y), SEO, and web Best Practices regressions.

Read the full file on GitHub · 83 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.

2d ago First seen · 83 lines · 19 tokens per session scan A e643c2733863

Subscribe to this mod's changes

review-performance is a skill published in the GitHub repository P2ERGmbH/agentic-coding (9 stars, last pushed 2mo ago), licensed MIT. It adds 19 tokens to every session and 1,316 once invoked, about $0.0001 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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