bundled-skills/ghidra-reverse/SKILL.md
Free/open reverse engineering with Ghidra (headless or GUI): decompilation, cross-references, scripting, and optional Ghidra MCP workflows when IDA is unavailable.
npx skillsauth add FrancoStino/opencode-skills-antigravity ghidra-reverseInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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binary-diff / patch-diff-exploit 的 ghidriff 联动| 需求 | 优先 |
|------|------|
| 已有 IDA MCP 深挖 | ida-reverse/ |
| 开源 / 批量 / 教学 | 本 skill |
| 仅 CLI 快速侦察 | radare2/ |
□ 新建 Project → Import 文件 → Analyze(默认分析器)
□ 记录语言/编译器识别结果与基址
□ 标记入口、导出表、字符串 xref
□ 从字符串 / 导入 API 反查
□ Decompile 窗口还原算法
□ 重命名函数/变量;写 Plate comment
□ 需要动态时交接 Frida/GDB(reverse-engineering 动态章)
# 示例:analyzeHeadless 路径因安装而异,MUST 从 tool-index 取
analyzeHeadless /path/to/project Proj -import sample.bin -postScript ExportDecomp.py
□ 确认 ghidra MCP 端口(常见 8765,以 tool-index 为准)
□ 用 MCP 工具拉反编译 / xrefs,禁止猜端口
| 工具 | 用途 | 自举 |
|------|------|------|
| Ghidra | 反编译主工具 | 手动 release / 包管理器 |
| ghidra-mcp | AI 桥 | bootstrap 能力名 ghidra-mcp |
| ghidriff | 补丁差分 | 见 patch-diff-exploit |
references/ghidra-cheatsheet.md../ida-reverse/ ../radare2/ ../binary-diff/上游: MASTER R22
下游: 动态验证 → Frida/GDB;利用 → pwn-chain
同级: ida-reverse(商业深挖)
Adapted from zhaoxuya520/reverse-skill (MIT).
tools
Authorized security assessment of LLM applications and AI agents: prompt injection, tool abuse, RAG exposure, memory poisoning, system-prompt extraction, and agent-compliance engineering per OWASP LLM/ASI Top 10.
development
Builds two parameterized UI modes—流光溢彩白 (iridescent white) and 五彩斑斓黑 (colorful black)—with OKLCH, WebGL/CSS fallback, vision gating, screenshot QA, and total/per-color intensity reports. Use when a UI request names either mode or needs measured color parameters.
tools
Delegate coding tasks to the Kimi Code CLI (`kimi`) only when the user explicitly requests it, while the orchestrator retains review and landing responsibility.
development
Front-end JavaScript reverse engineering: locate signature chains, analyze encrypted request parameters, sample runtime behavior, and reproduce logic locally in Node for evidence-based output.