Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add dslsdzc/rev-skills --skill re-malwaregit clone --depth 1 https://github.com/dslsdzc/rev-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dslsdzc/rev-skills/re-malware)<a href="https://agentmods.dev/skills/dslsdzc/rev-skills/re-malware"><img src="https://agentmods.dev/badge/skills/dslsdzc/rev-skills/re-malware/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dslsdzc/rev-skills/re-malware"><img src="https://agentmods.dev/badge/skills/dslsdzc/rev-skills/re-malware.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00105 | $0.01363 |
| Opus 5 | $0.00053 | $0.00681 |
| Sonnet 5 | $0.00021 | $0.00273 |
| Haiku 4.5 | $0.00011 | $0.00136 |
Grade A, and why
re-malware scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
恶意软件分析
完整工作流
- 默认沙箱前置：[[re-sandbox]] —— 判定恶意行为 / 一切动态执行先建沙箱（VM 快照 > 容器 > firejail），网络隔离（INetSim / fake DNS / 断网），快照就绪再传样本。仅静态初勘可免沙箱（见 [[platform-tips]] 最高原则）
- 静态初勘：[[re-triage]] —— file / hash / 熵 / strings，确认真实类型与架构；熵 >7.0 先怀疑加壳/加密
- 行为分析：[[re-behavior]] —— 沙箱内运行样本：进程树与注入、持久化清单、文件/注册表、网络连接、ATT&CK 映射
- C2/协议：[[re-protocol]] —— [[re-netcap]] 捕获回连流量、[[re-proto-rev]] 重建协议状态机、[[re-crypto-id]] / [[re-crypto-keys]] / [[re-crypto-decrypt]] 识别并解密通信加密
- 加壳样本转 [[re-anti-analysis]]（packer-id → unpack-simple/advanced）：脱壳产物回到步骤 1 沙箱复跑验证，再继续行为分析
- IOC/报告：[[re-ioc]] —— 提取 IOC（hash/域名/IP/路径/互斥体）、写 YARA 规则、按报告结构（摘要/行为/证据/IOC/结论）产出
每步结果存档（证据路径 + sha256，见 [[re-triage]]），供第 6 步报告引用。
何时用哪个原子技能（选择树）
按目标分支：
- 行为判定（"会不会回连""它在干什么"）→ [[re-sandbox]] → [[re-behavior]] →（发现 C2）[[re-protocol]] → [[re-ioc]]
- 配置提取（C2 域名/密钥/解密配置）→ 静态优先 [[re-triage]]（可免沙箱）；加壳先转 [[re-anti-analysis]]；配置在内存则 [[re-memdump]]；需要运行时进 [[re-sandbox]] + [[re-behavior]]
- 家族识别（"哪个家族""是否已知恶意"）→ [[re-triage]] 哈希/字符串比对已知库 → [[re-ioc]] 写 YARA 验证；行为佐证走 [[re-behavior]]
- 静态逻辑深挖（不涉及运行）→ 转 [[re-binary-core]] 域（[[re-ghidra]] / [[re-ida]] / [[re-radare2]]）
- 勒索样本（加密文件/勒索信/解密恢复）→ [[re-ransomware]]（加密识别走 crypto 域，C2 交互走 [[re-protocol]]）
- 加载器/投放器（多层下载/内存加载/模块拼接）→ [[re-loader]]（沙箱+[[re-tracing]]+[[re-memdump]]）
- 无文件样本（无落盘/内存执行/PowerShell 链）→ [[re-fileless]]（[[re-sandbox]]+[[re-memdump]]+[[re-script-deob]] 联动）
- 钓鱼附件（PDF/Office/RTF 文档） → [[re-doc-malware]]（文档武器化/宏链/载荷提取）
跨域联合
- 恶意样本加壳：[[re-malware]] → [[re-anti-analysis]]（packer-id / unpack-*），脱壳产物回沙箱复跑再行为分析
- C2 通信分析：[[re-malware]] → [[re-protocol]]（netcap / proto-rev / crypto-*）
- 深度静态逻辑 → [[re-binary-core]]（[[re-ghidra]] 反编译等）
- 无文件/内存执行样本：[[re-malware]] → [[re-fileless]]（沙箱 + 内存转储 + 脚本解码链还原；与 [[re-evasion]] 的 AMSI/ETW 对抗衔接）
- 本网关被 [[re-analyze]] 的 triage.md「判定恶意行为 / 会不会回连」路径调用（re-malware → re-sandbox → re-behavior → re-protocol → re-ioc）
常见坑与陷阱
- 拿到样本跳过沙箱直接运行 → 环境污染且破坏证据 —— 动态分析默认沙箱是最高原则（见 [[platform-tips]]）
- 熵高（>7.0）不确认加壳就硬跑行为分析 → 观察到的全是壳的行为 —— 先 [[re-triage]] 判断，加壳转 [[re-anti-analysis]]
- 行为结果不留证据（无日志/无 hash）→ 报告不可复现 —— 每步产物按 [[re-ioc]] 报告结构存档
- 沙箱网络未隔离就跑样本 → 真实外联 —— 先 INetSim / fake DNS / 断网（[[re-sandbox]] 网络隔离步骤）
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 51 lines · 105 tokens per session scan A bf6cf514ef31
re-malware is a skill published in the GitHub repository dslsdzc/rev-skills (54 stars, last pushed 14d ago), licensed Apache-2.0. It adds 105 tokens to every session and 1,363 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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