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Ig5 Crypto

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Minglink
ig5-crypto

使用 IG5 定位加密数据、自动恢复 XOR 或检索 AES 候选密钥,再通过认证/独立明文验证与来源引用完成配置提取。

Overview

PublisherMinglink
Repositorydsh-infinite-gen-5
Skill nameig5-crypto
Stars
2.7K
Forks
246
Bundled files
Instructions only
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 Minglink on GitHub. Read the source before you install it.

Installation

Install the Ig5 Crypto 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/Minglink/dsh-infinite-gen-5/tree/master/skills/ig5-crypto
  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/Minglink/dsh-infinite-gen-5.git /tmp/dsh-infinite-gen-5
mkdir -p .claude/skills
cp -r /tmp/dsh-infinite-gen-5/skills/ig5-crypto .claude/skills/ig5-crypto
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ig5 Crypto 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 Ig5 Crypto 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 Ig5 Crypto 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.

先明确待分析的密文、配置或编码缓冲区。需要静态定位时,默认 engine=reverse、reverseProvider=bundled 使用随包服务,实际 provider=ghidra,无需本机商业安装;显式 Ghidra 使用独立车道,商业扩展仅在显式配置 reverseProvider=commercial 时使用。工具按当前 provider 与 capabilities 选择。需要高级工具时调用 ig5_profile toolset=full;按返回的 toolsetScope 判断作用域:支持作用域的宿主仅切换当前 agent,旧宿主明确返回 plugin-instance 时才影响插件实例。两种切换均不持久化,不授权样本执行。记录样本 artifactId、静态 engine/provider 与 dbRevision,以及字节的文件偏移或 VA;不能把运行时地址当作静态 VA。使用 ig5_scan 的标准结果、字符串、导入、xref、调用关系和反编译定位处理路径。常量、API、熵或块对齐只支持候选假设,不能据此确认算法或成功解密。

ig5_crypto 是离线数据工具,不需要打开静态数据库。input 必须选择一种来源:{encoding:"hex"|"base64",data:"..."}、{path:"完整本地文件路径"}、{ref:"sha256:...",result_id:"产生该 ref 的报告 UUID"},或 {source:{target,engine,ea,size,expected_revision}}。引用输入使用明确 producer ID 保留来源;省略 input.result_id 会标记 unbound,不能从相同字节 hash 推断样本或引擎归属。文件和静态读取受字节预算约束;静态来源优先携带本次修订。先用 action=inspect 获取范围、SHA-256、编码/压缩头、熵及有限预览,不自动执行疑似处理函数,也不把头部匹配当作完整压缩载荷有效。

从函数参数、调用上下文、只读字节或已授权的暂停事件恢复 key、IV、计数器初值、tag、AAD 与 padding。记下来源地址和实际观察,不猜默认参数。需要执行函数时,仅在用户已授权该目标执行后,通过审批调用 ig5_emulate 或 ig5_dbg。仿真架构与 ABI 按当前 provider、capabilities 和目标核验,不能仅凭 engine 名称推断 x86/x64/ARM64 支持;ARM64 使用 AAPCS64。受限 CPU 仿真没有 OS/import/TLS,异常、指令上限和超时不能当成明文。内置 Reverse 与显式 Ghidra 默认调试后端为随包 x64dbg;native win32 仅用于显式商业扩展且实际能力支持的情况,失败不静默换后端。调试捕获保留 runId、stopSeq、模块映射与缓冲区范围,不能重用旧暂停上下文。

仅当已有静态目标时,用 ig5_profile target=... workspace={action:create,goal:...} 建立调查任务,或用 workspace={action:get,id:...} 恢复。候选处理函数优先用 ig5_decompile style=dossier task_id=... 汇总反编译、CFG、调用者/被调函数和栈帧;检查章节 ok/truncated/unsupported/error、provenance、partial 与 stale,正文响应预算不等于全部后端计算预算。以本次 taskRevision 作为 expected_task_revision 更新假设/下一步/结论,冲突后重读。任务只存分析元数据,不写 IDB、不授权执行,status 是调用者进度声明;复开或修订变化后重新取证。任务文本只记录敏感材料引用及来源,不复制密钥原值;纯离线数据分析无需为建任务打开数据库,继续保留独立 result_id/ref。

密钥未知时先调用 ig5_crypto action=recover。recovery.method=auto|xor-single|xor-repeat|aes-candidates;用已知明文约束、有限候选或 key_source 的文件/ref/静态范围提高成功率。XOR 统计分数只是候选;partial key 的 unknown mask 不得当作完整字节执行。AES 需给准确 IV/tag/AAD/padding,用候选材料检索并验证,不尝试随机 AES 全空间。独立完整 expected 或足够强的 GCM tag 可支持 verified;训练 crib、padding、可读性不能替代独立验证。记录所有 budgets/truncated/歧义。

选完整恢复候选的 keyMaterial.dataRef.ref,配对 recovery result_id,作为 transform 的 recipe.key_ref:{ref,result_id};宿主核验完整性、来源与修订,密钥无须在模型上下文展开。也可显式提供 recipe.key。recipe.kind 可选 xor、aes-cbc、aes-ctr、aes-gcm、gzip 或 zlib;IV/tag/AAD 来自准确证据。XOR 循环起点用 recipe.key_offset,AES 明确 padding,CTR/GCM 使用 none。GCM 认证失败不返回明文。输入/输出最多 1 MiB,预览最多 4096 字节;敏感 key blob 是用户数据,不分发,不自动预览。

多阶段变换使用前一步响应的 output.ref 和同一响应的顶层 result_id;下一步把两者放进 input,例如:

json
{"action":"transform","input":{"ref":"<前一步 output.ref>","result_id":"<前一步顶层 result_id>"},"recipe":{"kind":"gzip"}}

将占位符替换为实际返回值。input.result_id 是下一次调用选择 producer 的参数;action=result 的顶层 result_id 则是要读取的历史报告。不要将“前一步捕获自哪里”与“本步骤生成的报告 UUID”混为一谈。若使用响应中对应 output.result_id,也须确认它与所选 ref 同属同一报告。

有确定的已知明文时,使用 expected={encoding,data},检查响应的 value.verification.status、长度、SHA-256 与首个差异偏移;也可单独调用 action=verify。无 expected 时保留 not-requested,不能写成已验证。至少用独立测试向量或多条配置样本复核同一参数/处理路径;GCM 的 value.authentication.status=passed 只验证这次给定参数的认证关系,不证明样本采用了该算法。

完成后记录 result_id、输入/输出 blob ref、字节范围、来源与验证状态。用 action=result result_id=... select=... 读取此前报告的相关部分,避免重新发送完整数据。保存的结果不包含 key、IV、tag 或 AAD 值;可复现分析还需要用户保管参数及其证据,不能声称仅凭报告就能恢复秘密。工作台只读显示结果,composer 只插入可编辑草稿,不自动发送;若要回写名称、注释或类型,仍通过对应审批工具。结论分别陈述已经认证/比对的结果、候选算法和未恢复参数。

Frequently asked questions

What does the Ig5 Crypto AI skill do?

使用 IG5 定位加密数据、自动恢复 XOR 或检索 AES 候选密钥,再通过认证/独立明文验证与来源引用完成配置提取。

Why use Ig5 Crypto on TypingMind?

Because you install it once and use it with any model. Ig5 Crypto 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 Ig5 Crypto in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Minglink/dsh-infinite-gen-5/tree/master/skills/ig5-crypto. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ig5 Crypto?

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 Ig5 Crypto?

As many as you like. As long as a model supports skills, you can use Ig5 Crypto with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ig5 Crypto AI skill free?

It is published on GitHub by Minglink. Check the repository for licensing terms. 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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