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Reverse Engineering

该 Skill 提供一套逆向工程方法论:先界定目标、期望结果与操作边界,再按 TRACE 循环记录可观察行为、抽象假设并用最小可复现探针验证;用证据阶梯区分观察、推断与猜测,先绘制由输入、输出、状态、副作用和信任边界组成的接口地图,最后产出目标简报、接口地图、假设台账、复现说明和未解问题,并把偏好与工件保存在本地目录。

ivangdavila
ivangdavila
热度 89.02
最近更新 2026-05-11 17:32:38
分类:安全依赖:读写文件应用场景:研究 / 调试排错 / API 集成 / 报告撰写

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add reverse-engineering
Metadata
name
Reverse Engineering
slug
reverse-engineering
version
1.0.0
homepage
https://clawic.com/skills/reverse-engineering
description
Reverse engineer binaries, APIs, protocols, and workflows with evidence ladders, interface maps, and falsifiable hypotheses.
changelog
Adds a structured reverse engineering workflow with evidence tracking, interface mapping, and safer uncertainty handling.
metadata
{"clawdbot":{"emoji":"🧩","requires":{"bins":[]},"os":["linux","darwin","win32"],"configPaths":["~/reverse-engineering/"]}}

When to Use

User needs to understand something opaque, undocumented, legacy, or partially broken. Agent handles behavioral tracing, artifact mapping, hypothesis testing, and concise documentation for binaries, APIs, file formats, protocols, devices, and human workflows.

Architecture

Memory lives in ~/reverse-engineering/. If ~/reverse-engineering/ does not exist, run setup.md. See memory-template.md for structure.

~/reverse-engineering/
├── memory.md          # durable preferences, approvals, and common target types
├── current-target.md  # active engagement snapshot
├── targets/           # one file per target or system
└── artifacts/         # traces, decoded notes, and reproduction snippets

Quick Reference

Use these files on demand instead of loading the whole method every time.

Topic File
Setup guide setup.md
Memory template memory-template.md
TRACE protocol protocol.md
Confidence model evidence-ladder.md
Surface mapping interface-map.md
Deliverable templates deliverables.md
Safety boundaries boundaries.md

Requirements

  • Authorized access to the target, samples, and environment being analyzed
  • A clear statement of whether the target is production, staging, or an offline copy
  • Explicit user approval before any invasive, destructive, or credential-bearing step

Core Rules

1. Bound the job before probing

  • Name the target, desired outcome, available artifacts, and operational boundary first.
  • Ask what is allowed: read-only inspection, replay, instrumentation, decompilation, fuzzing, or patching.
  • If the boundary is unclear, default to the safest read-only path.
  • Before the first persistent write, state what will be stored locally and ask for permission.

2. Run the TRACE loop from protocol.md

  • Triage the target.
  • Record observable behavior.
  • Abstract hypotheses.
  • Challenge each hypothesis with the smallest useful test.
  • Explain the result in user-facing language.

3. Separate evidence, inference, and guess

  • Tag every claim using the ladder in evidence-ladder.md.
  • Never blur "observed" with "likely" or "possible."
  • When certainty is low, say what would raise confidence instead of pretending to know.

4. Map surfaces before internals

  • Build the interface inventory from interface-map.md before writing an implementation story.
  • Start from inputs, outputs, states, side effects, and trust boundaries.
  • Reverse engineering is faster when the outer contract is stable before diving deeper.

5. Prefer minimal, reproducible probes

  • Use the smallest sample, trace, packet, call, or binary slice that can prove or disprove a hypothesis.
  • Keep every probe replayable and attributable.
  • If a result cannot be reproduced, it is a clue, not a conclusion.

6. Deliver models, not raw notes

  • Every session should end with concrete outputs from deliverables.md: target brief, interface map, hypothesis ledger, reproduction note, and remaining unknowns.
  • Optimize for what the user can act on next: debug, reimplement, migrate, document, or secure.
  • Good reverse engineering compresses complexity without hiding uncertainty.

Common Traps

These failures usually waste the most time or create false confidence.

Trap Why It Fails Better Move
Jumping straight to decompilation You lose the external contract and context Start with behavior, interfaces, and captured artifacts
Treating logs as truth Logs reflect one code path and one viewpoint Cross-check with traces, outputs, and controlled inputs
Reverse engineering the whole system Scope explodes and confidence drops Pick one question, one layer, and one target surface
Confusing correlation with mechanism Similar timings or names can mislead Design a falsifiable probe before concluding
Keeping findings in loose notes Knowledge becomes untestable and unreusable Convert findings into deliverables with evidence tags
Poking live systems casually You create risk and destroy signal Prefer offline copies, captures, and explicit approvals

Security & Privacy

Data that leaves your machine:

  • Nothing by default.
  • Only user-approved samples or public documentation if the task explicitly requires external lookup.

Data that stays local:

  • Preferences and engagement notes in ~/reverse-engineering/
  • Captured traces, decoded notes, and reproduction snippets kept in the workspace or the local reverse-engineering folder

This skill does NOT:

  • Steal credentials, bypass authorization, or hide activity
  • Run exploit chains on production targets by default
  • Claim certainty without evidence
  • Persist sensitive data outside the documented local folder
  • Create durable local memory without first telling the user what will be stored

Related Skills

Install with clawhub install <slug> if user confirms:

  • analysis — structure ambiguous problems and turn raw evidence into decisions
  • api — reason about endpoints, payloads, contracts, and integration behavior
  • architecture — model system boundaries, components, and data flow once the target is understood
  • bash — build small inspection and replay loops for traces, logs, and artifacts
  • cybersecurity — evaluate trust boundaries, attack surface, and safe handling of sensitive targets

Feedback

  • If useful: clawhub star reverse-engineering
  • Stay updated: clawhub sync