Network Request Auditing logo

Network Request Auditing

Community
spencerpauly
network-request-auditing

After navigating and interacting in Cursor's built-in browser, use browser_network_requests to audit every fetch/XHR for failures, slowness, duplicate calls, and suspicious payloads. Use for API-heavy pages and after backend or client networking changes.

Overview

Publisherspencerpauly
Repositoryawesome-cursor-skills
Skill namenetwork-request-auditing
Stars
848
Forks
169
Bundled files
Instructions only
LicenseCC0-1.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by spencerpauly on GitHub. Read the source before you install it.

Installation

Install the Network Request Auditing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/spencerpauly/awesome-cursor-skills/tree/main/resources/network-request-auditing
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/spencerpauly/awesome-cursor-skills.git /tmp/awesome-cursor-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-cursor-skills/resources/network-request-auditing .claude/skills/network-request-auditing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Network Request Auditing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Network Request Auditing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Network Request Auditing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Network Request Auditing

Deep-dive network health using the cursor-ide-browser MCP. This skill focuses on browser_network_requests — not just “any 500s” but patterns that indicate bugs, waste, or security issues.

How it works

  1. Drive the app in the browser (navigate, click, submit forms) so real requests fire.
  2. Call browser_network_requests after meaningful interactions (and after navigation settles).
  3. Classify and report findings using the criteria below.

Follow cursor-ide-browser workflow rules: use browser_snapshot before structural interactions; after actions that change the page, take a fresh snapshot before the next interaction.

Audit checklist

Failures

  • 4xx / 5xx — list method, URL (path + query), status, and whether the UI handled the error.
  • CORS or network errors — often misconfigured origins or mixed content.

Performance

  • Slow requests — flag requests with high latency (e.g. > 500 ms server time if timings are visible; otherwise note unusually large waterfalls).
  • Duplicate calls — same URL + method fired multiple times in one user action (often a React effect or missing deduplication).
  • Oversized payloads — responses that look huge for what the UI needs (suggest pagination, field selection, or compression).

Security and privacy

  • Sensitive data in URLs — tokens or PII in query strings.
  • Missing auth — API calls that should send credentials or bearer tokens but do not (compare with adjacent authenticated calls).

Correctness

  • Unexpected hosts — calls to third parties not documented for the feature (trackers, accidental leaks).
  • Preflight storms — excessive OPTIONS requests may indicate wrong CORS caching or too many distinct origins.

Steps

  1. Start from a clean navigation — browser_navigate to the target URL (or use an existing tab via browser_tabs).

  2. Exercise the feature — interactions that trigger API usage (filters, infinite scroll, form save, modal open).

  3. Fetch network log — browser_network_requests after each logical step if the page does multiple round-trips.

  4. Report — structured output:

    • Summary counts (failed, slow, duplicate groups).
    • Table or bullet list of issues with URL pattern (not necessarily full secrets), status, category (failure / perf / security / correctness).
    • Recommended next code changes or investigations.

Notes

  • Iframe traffic may not appear in the same log — note if the feature runs inside an iframe.
  • Compare against expected API design; a 404 might be correct for “optional resource not found” if handled in UI.
  • Pair with browser_console_messages for errors that do not surface as failed HTTP (e.g. parse errors after 200).

Frequently asked questions

What does the Network Request Auditing AI skill do?

After navigating and interacting in Cursor's built-in browser, use browser_network_requests to audit every fetch/XHR for failures, slowness, duplicate calls, and suspicious payloads. Use for API-heavy pages and after backend or client networking changes.

Why use Network Request Auditing on TypingMind?

Because you install it once and use it with any model. Network Request Auditing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Network Request Auditing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/spencerpauly/awesome-cursor-skills/tree/main/resources/network-request-auditing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Network Request Auditing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Network Request Auditing?

As many as you like. As long as a model supports skills, you can use Network Request Auditing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Network Request Auditing AI skill free?

Yes. It is published on GitHub by spencerpauly under the CC0-1.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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