本文へ移動
cccskills
無料GitHub で公開

competition-reverse-pwn

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for reverse engineering, malware, DFIR, firmware, pwnable, and native exploit challenges. Use when the user asks to reverse a binary, unpack a sample, inspect a memory dump or PCAP, recover malware behavior, debug a crash, or build or verify an exploit chain under sandbox assumptions. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

インストール方法を見る

含まれるファイル(3)

  • SKILL.md3.8 KB
  • agents/openai.yaml370 B
  • references/reverse-pwn.md1010 B

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Competition Reverse Pwn

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 for binary-heavy challenges where the decisive path runs through artifacts, decoded layers, process behavior, crash state, or exploit primitives.

Reply in Simplified Chinese unless the user explicitly requests English.

Quick Start

  1. Preserve the original artifact before unpacking, patching, or instrumenting.
  2. Start with passive triage: type, headers, sections, imports, strings, entropy, resources.
  3. Decide whether the path is reverse-first, DFIR-first, or exploit-first.
  4. Tie every claim to an observable boundary: decode edge, persistence edge, crash edge, or leak edge.
  5. Reproduce the artifact or primitive from a clean baseline.

Workflow

1. Reverse Or Forensic Triage

  • Separate loader, payload, config, and post-decode behavior.
  • Correlate files, memory, logs, registry, services, tasks, IPC, and PCAPs as one graph.
  • Keep decoded or dumped artifacts separate from the pristine sample.

2. Native And Exploit Path

  • Map mitigations, loader behavior, libc or runtime, syscall and IPC surfaces, and protocol framing.
  • Record the primitive, controllable bytes, leak source, target object, and final artifact separately.
  • Compare host, libc, loader, and framing differences before doubting the primitive.

Read This Reference

  • Load references/reverse-pwn.md for triage order, exploit evidence expectations, and common failure modes.
  • If the task is specifically about staged payload boundaries, config blobs, beacon parameters, or decoded IOC fields, prefer $competition-malware-config.
  • If the task is specifically about firmware partitions, boot chains, extracted filesystems, or update-package trust boundaries, prefer $competition-firmware-layout.
  • 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 source maps, emitted bundles, chunk registries, or reconstructing hidden runtime structure from served frontend assets, prefer $competition-bundle-sourcemap-recovery.
  • If the task is specifically about container-to-host boundary crossing, kernel exploit preconditions, namespace or cgroup crossover, or escape primitive verification, prefer $competition-kernel-container-escape.
  • If the task is specifically about reconstructing protocols, streams, or transferred artifacts from packet captures, prefer $competition-pcap-protocol.
  • If the task is specifically about a custom binary or text protocol where replay state, message order, or checksum logic is the real blocker, prefer $competition-custom-protocol-replay.
  • If the task is specifically about reconstructing chronology across EVTX, PCAP, registry, mail, or disk artifacts, prefer $competition-forensic-timeline.

What To Preserve

  • Offsets, hashes, section names, imports, config blobs, mutexes, registry keys
  • Crash offsets, registers, heap or stack shape, leak addresses, and protocol steps
  • Original, decoded, dumped, and instrumented artifacts as separate files

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

A/B 测试设计与分析时使用。适用于产品实验、功能验证、转化率优化。融合假设驱动 + 样本量计算 + 置信度判断。

日本語の概要は準備中です。原文の説明を表示しています。

zhaoxuya520/AI-Fullstack-Delivery-Workflow532026年5月19日 更新

上线前业务方/产品方验收时使用。适用于功能交付确认、UAT、发布会签。融合 BDD Given-When-Then、Specification by Example、ATDD。

日本語の概要は準備中です。原文の説明を表示しています。

zhaoxuya520/AI-Fullstack-Delivery-Workflow532026年5月19日 更新

设计无障碍可访问的界面时使用。适用于满足 WCAG 标准、键盘导航、屏幕阅读器支持。优先使用 WCAG 2.2 AA 级标准 + 键盘 + 焦点 + 对比度 + ARIA。

日本語の概要は準備中です。原文の説明を表示しています。

zhaoxuya520/AI-Fullstack-Delivery-Workflow532026年5月19日 更新

实现可访问性(无障碍)时使用。覆盖 WCAG 2.2 AA / ARIA / 键盘导航 / 屏幕阅读器 / 颜色对比 / 焦点管理。融合 React Aria / Radix UI / Headless UI 实践 + axe-core 自动化测试。

日本語の概要は準備中です。原文の説明を表示しています。

zhaoxuya520/AI-Fullstack-Delivery-Workflow532026年5月19日 更新

AI 模型部署时使用。适用于推理服务 / GPU / TensorRT / 监控 / 扩缩容。

日本語の概要は準備中です。原文の説明を表示しています。

zhaoxuya520/AI-Fullstack-Delivery-Workflow532026年5月19日 更新

数据分析报告输出时使用。适用于业务分析报告、周报、专项分析、决策支持。融合洞察 + 建议 + 行动项。

日本語の概要は準備中です。原文の説明を表示しています。

zhaoxuya520/AI-Fullstack-Delivery-Workflow532026年5月19日 更新

zhaoxuya520 のスキルをすべて見る

このスキルの問題を報告する