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Competition Web Runtime

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zhaoxuya520
competition-web-runtime

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for CTF web, API, SSR, frontend, queue-backed app, and routing challenges. Use when the user asks to inspect a site or API, follow real browser requests, debug auth or session flow, trace uploads or workers, find hidden routes, or explain why frontend and backend behavior diverge under sandbox-internal routing. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

Overview

Publisherzhaoxuya520
Repositoryreverse-skill
Skill namecompetition-web-runtime
Stars
36.3K
Forks
5K
Bundled files
3
LicenseMIT
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.

  • 3 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 Web Runtime 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-web-runtime .claude/skills/competition-web-runtime
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competition Web Runtime 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 Web Runtime 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 Web Runtime 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 Web Runtime

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 the active challenge is primarily about web behavior, browser state, server routing, API order, or worker-backed application flow.

Reply in Simplified Chinese unless the user explicitly requests English.

Quick Start

  1. Assume the presented hosts, domains, and routes belong to the sandbox.
  2. Inspect entry HTML, boot scripts, runtime config, and route registration before trusting the visible UI.
  3. Capture one real request flow end-to-end before making broad claims from source.
  4. Check browser persistence and backend state together.
  5. Re-run the smallest flow with one variable changed.

Workflow

1. Map The Active Runtime

  • Identify active hosts, paths, proxies, containers, and workers.
  • Inspect cookies, localStorage, sessionStorage, IndexedDB, Cache Storage, and service workers.
  • Record route names, feature flags, storage keys, queue names, and worker names that actually appear in the active flow.

2. Capture The Real Request Order

  • Record exact host, path, query, headers, cookies, and body for decisive requests.
  • Compare successful and failing paths.
  • Treat UI gating as a hint, not proof of backend enforcement.

3. Expand Only After One Path Is Proven

  • Trace middleware order, handlers, auth/session boundaries, uploads, exports, and background jobs.
  • Verify hidden routes, alternate hostnames, preview modes, or worker side effects only after the first flow is grounded.

Read This Reference

  • Load references/routing-runtime.md for the detailed checklist, evidence packaging, and common web pitfalls.
  • If the task is specifically about SSR loaders, template context, hydration payloads, preview rendering, or render-layer enforcement drift, prefer $competition-template-render-path.
  • If the task is specifically about source maps, build manifests, chunk registries, emitted bundles, or recovering hidden runtime structure from served assets, prefer $competition-bundle-sourcemap-recovery.
  • If the task is specifically about GraphQL schemas, RPC manifests, persisted queries, generated clients, or contract-to-handler drift, prefer $competition-graphql-rpc-drift.
  • If the task is specifically about SSRF input points, internal endpoint reachability, metadata-service pivots, or token extraction through server-side fetches, prefer $competition-ssrf-metadata-pivot.
  • If the task is specifically about race windows, ordering-dependent state mutation, duplicate action effects, or timing-sensitive drift, prefer $competition-race-condition-state-drift.
  • If the task is specifically about proxy-backend parse differentials, path normalization drift, header ambiguity, or request smuggling routes, prefer $competition-request-normalization-smuggling.
  • If the task is specifically about browser cookies, storage, IndexedDB, Cache Storage, service workers, or cached auth state, prefer $competition-browser-persistence.
  • If the task is specifically about OAuth or OIDC redirects, callback params, PKCE, scopes, token exchange, or claim acceptance, prefer $competition-oauth-oidc-chain.
  • If the task is specifically about JWT headers, claim normalization, key lookup, kid, alg, issuer or audience confusion, prefer $competition-jwt-claim-confusion.
  • If the task is specifically about upload parsing, previews, archive extraction, converters, or deserialization chains, prefer $competition-file-parser-chain.
  • If the task is specifically about queue payloads, worker-only behavior, retries, cron drift, or async side effects, prefer $competition-queue-worker-drift.
  • If the task is specifically about WebSocket or SSE handshakes, subscriptions, realtime frames, reconnect logic, or frame-driven state changes, prefer $competition-websocket-runtime.
  • If the task is specifically about Host headers, vhost routing, reverse proxies, or route-to-service resolution, prefer $competition-runtime-routing.
  • If the only available evidence is a packet capture and the hard part is stream or protocol reconstruction, prefer $competition-pcap-protocol.

What To Preserve

  • Exact requests and responses that prove behavior
  • Concrete file paths, function names, route names, and storage keys
  • Queue payloads, worker names, or retry behavior when async processing matters

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 Web Runtime AI skill do?

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for CTF web, API, SSR, frontend, queue-backed app, and routing challenges. Use when the user asks to inspect a site or API, follow real browser requests, debug auth or session flow, trace uploads or workers, find hidden routes, or explain why frontend and backend behavior diverge under sandbox-internal routing. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

Why use Competition Web Runtime on TypingMind?

Because you install it once and use it with any model. Competition Web Runtime 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 Web Runtime in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zhaoxuya520/reverse-skill/tree/main/CTF-Sandbox-Orchestrator/competition-web-runtime. 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 Web Runtime?

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 Web Runtime?

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

Is the Competition Web Runtime 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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