Competition Request Normalization Smuggling logo

Competition Request Normalization Smuggling

CommunityPopular
zhaoxuya520
competition-request-normalization-smuggling

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for parser differentials, HTTP normalization gaps, ambiguous headers, path decoding drift, transfer-framing mismatches, and request smuggling routes. Use when the user asks to trace proxy and backend parse differences, conflicting path normalization, Host or forwarded-header ambiguity, CL/TE issues, or routing outcomes that differ across hops. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

Overview

Publisherzhaoxuya520
Repositoryreverse-skill
Skill namecompetition-request-normalization-smuggling
Stars
36.3K
Forks
5K
Bundled files
2
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by zhaoxuya520 on GitHub. Read the source before you install it.

Installation

Install the Competition Request Normalization Smuggling 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.
  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/zhaoxuya520/reverse-skill.git /tmp/reverse-skill
mkdir -p .claude/skills
cp -r /tmp/reverse-skill/CTF-Sandbox-Orchestrator/competition-request-normalization-smuggling .claude/skills/competition-request-normalization-smuggling
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competition Request Normalization Smuggling 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 Competition Request Normalization Smuggling 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 Competition Request Normalization Smuggling 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.

Competition Request Normalization Smuggling

Use this skill only as a downstream specialization after $ctf-sandbox-orchestrator is already active and has established sandbox assumptions, node ownership, and evidence priorities. If that has not happened yet, return to $ctf-sandbox-orchestrator first.

Use this skill when request interpretation changes between proxy, middleware, and backend parser layers.

Reply in Simplified Chinese unless the user explicitly requests English.

Quick Start

  1. Map every parsing hop: client-facing proxy, gateway, app server, and downstream service.
  2. Record path normalization, header canonicalization, transfer framing, and host derivation at each hop.
  3. Capture one accepted baseline request and one differential request with minimal delta.
  4. Prove which hop interprets the request differently.
  5. Reproduce one minimal differential path that yields decisive behavior.

Workflow

1. Map Parse And Routing Boundaries

  • Record Host, forwarded headers, path decoding, slash collapsing, dot-segment handling, and case behavior.
  • Note Content-Length, Transfer-Encoding, chunk framing, and connection reuse behavior when relevant.
  • Keep edge parser and backend parser decisions side by side.

2. Prove Differential Interpretation

  • Build paired requests that differ in one canonicalization dimension only.
  • Capture proxy logs, backend logs, route match, and downstream request shape.
  • Show where route, auth scope, or body boundary diverges.

3. Reduce To Decisive Smuggling Chain

  • Compress to: crafted request -> parser differential across hops -> unintended routed request or hidden endpoint reach -> resulting effect.
  • State whether root cause is path normalization drift, header ambiguity, transfer framing differential, or host-derivation confusion.
  • If the chain becomes primarily runtime routing without framing tricks, hand off to runtime routing skill.

Read This Reference

  • Load references/request-normalization-smuggling.md for parse-differential checklist and evidence packaging.

What To Preserve

  • Raw request pairs, hop-by-hop interpretation, and final routed target
  • Exact normalization or framing delta that flips behavior
  • One minimal replayable differential request path

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Competition Request Normalization Smuggling AI skill do?

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for parser differentials, HTTP normalization gaps, ambiguous headers, path decoding drift, transfer-framing mismatches, and request smuggling routes. Use when the user asks to trace proxy and backend parse differences, conflicting path normalization, Host or forwarded-header ambiguity, CL/TE issues, or routing outcomes that differ across hops. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

Why use Competition Request Normalization Smuggling on TypingMind?

Because you install it once and use it with any model. Competition Request Normalization Smuggling 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 Competition Request Normalization Smuggling in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zhaoxuya520/reverse-skill/tree/main/CTF-Sandbox-Orchestrator/competition-request-normalization-smuggling. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Competition Request Normalization Smuggling?

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 Competition Request Normalization Smuggling?

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

Is the Competition Request Normalization Smuggling AI skill free?

Yes. It is published on GitHub by zhaoxuya520 under the MIT 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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