Competition File Parser Chain logo

Competition File Parser Chain

CommunityPopular
zhaoxuya520
competition-file-parser-chain

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for file uploads, imports, previews, archive extraction, format conversion, parser invocation, and deserialization chains. Use when the user asks to inspect an upload or import path, trace archive extraction, preview or converter behavior, explain how a file reaches a parser or deserializer, or connect one uploaded artifact to the decisive backend effect. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

Overview

Publisherzhaoxuya520
Repositoryreverse-skill
Skill namecompetition-file-parser-chain
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 File Parser Chain 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-file-parser-chain .claude/skills/competition-file-parser-chain
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competition File Parser Chain 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 File Parser Chain 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 File Parser Chain 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 File Parser Chain

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 hard part is following a file from ingress through every parser, extractor, converter, or deserializer boundary that matters.

Reply in Simplified Chinese unless the user explicitly requests English.

Quick Start

  1. Preserve the original upload and every derived artifact separately.
  2. Map the chain in order: ingress, temp storage, archive extraction, format conversion, parser call, deserialization, and final consumer.
  3. Record filenames, MIME guesses, extensions, temp paths, and parser choices before mutating anything.
  4. Separate client-visible validation from backend parser behavior.
  5. Reproduce the smallest file-processing chain that yields the decisive branch or artifact.

Workflow

1. Map File Ingress And Derivation

  • Record request shape, multipart names, content type, filename, temp paths, upload staging, and storage keys.
  • Note every derived artifact: extracted archive member, converted preview, generated thumbnail, temp document, or deserialized object.
  • Keep original file and each derivative labeled separately.

2. Trace Parser And Conversion Boundaries

  • Show which parser, converter, extractor, or deserializer runs at each step.
  • Record parser-specific decisions driven by extension, MIME, magic bytes, schema, archive member names, or embedded metadata.
  • Distinguish parsing success, preview success, conversion success, and business-logic acceptance.

3. Reduce To The Decisive File Chain

  • Compress the result to the smallest sequence: upload -> derived artifact -> parser boundary -> resulting effect.
  • State clearly whether the decisive weakness lives in archive handling, MIME inference, file conversion, path resolution, or deserialization.
  • If the chain becomes mostly a generic async worker problem after enqueue, hand off to the tighter queue or worker skill.

Read This Reference

  • Load references/file-parser-chain.md for the ingress checklist, parser checklist, and evidence packaging.

What To Preserve

  • Original uploads, derived files, temp paths, storage keys, parser names, and conversion steps
  • The exact boundary where backend behavior diverges from user-visible validation
  • One minimal replayable file-processing sequence that reaches the decisive effect

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 File Parser Chain AI skill do?

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for file uploads, imports, previews, archive extraction, format conversion, parser invocation, and deserialization chains. Use when the user asks to inspect an upload or import path, trace archive extraction, preview or converter behavior, explain how a file reaches a parser or deserializer, or connect one uploaded artifact to the decisive backend effect. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

Why use Competition File Parser Chain on TypingMind?

Because you install it once and use it with any model. Competition File Parser Chain 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 File Parser Chain in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zhaoxuya520/reverse-skill/tree/main/CTF-Sandbox-Orchestrator/competition-file-parser-chain. 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 File Parser Chain?

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 File Parser Chain?

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

Is the Competition File Parser Chain 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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