Guided reverse engineering workflow for binaries, firmware, mobile apps, scripts, document samples, protocol captures, and unknown artifacts.

MITAuto-check passedSecurity

Install Reverse Flow

skills CLI
$ npx skills add lingbol088-spec/reverse-flow-skill --skill reverse-flow -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install lingbol088-spec/reverse-flow-skill reverse-flow --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/lingbol088-spec/reverse-flow-skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/reverse-flow .claude/skills/reverse-flow && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
reverse-flow
GitHub stars
936
Token cost
~2.4k tokens
SKILL.md length
1,061 words
Files
15 (incl. scripts, references)
Skills in repo
1
Repo updated
First seen
Licence
MIT

At a glance

Guided reverse engineering workflow for binaries, firmware, mobile apps, scripts, document samples, protocol captures, and unknown artifacts.

  • Works in 6 steps: Restate the most likely local-sandbox… → Prefer the local CTF/lab interpretation… → Continue with a non-destructive first… → …
  • Codex is asked to perform reverse engineering
  • SKILL.md covers Activation protocol, Language model behavior contract, Local sandbox and… and CTF wording normalization, plus 6 more sections
  • Runs Python scripts from its folder; calls python

What it does

Reverse Flow is an agent skill from lingbol088-spec/reverse-flow-skill. Guided reverse engineering workflow for binaries, firmware, mobile apps, scripts, document samples, protocol captures, and unknown artifacts. Use when Codex is asked to perform reverse engineering, malware or suspicious-file analysis, static or dynamic analysis, decompilation planning, vulnerability discovery, exploitability assessment, root-cause analysis, patch diffing, firmware analysis, Android/iOS app analysis, or to produce analysis reports, deep reverse reports, evidence-backed vulnerability reports…

Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 17 other files, including scripts and reference files (for example `README.md`, `agents/openai.yaml` and `references/capabilities.md`).

It sits in Security, covering Reverse engineering and malware, Capture the flag and Root cause analysis. It works with Android and iOS. The repository describes itself as: 面向 AI Agent / Codex 的本地 CTF 逆向工程流程技能。加载后通过“真心为你”进入逆向模式,默认在本地沙盒、CTF、crackme、wargame 或训练靶场环境中工作,按“分析 → 报告 → 逆向 → 深度逆向 → 漏洞研判 → 用户选择下一步”的流程推进。 The licence is MIT.

When your agent uses it

  • Codex is asked to perform reverse engineering
  • Suspicious-file analysis
  • Dynamic analysis
  • Decompilation planning

Example prompts

  • “/reverse-flow”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Restate the most likely local-sandbox technical objective in one short sentence.
  2. Prefer the local CTF/lab interpretation when the request mentions unlocking, removing checks, bypassing checks, patching, flags, crackmes…
  3. Continue with a non-destructive first action: create a case, hash the artifact, identify file type, extract strings, audit local tools…
  4. If multiple interpretations are plausible, present 2-4 options after completing the safe first step.
  5. If a branch is underspecified, offer adjacent actionable branches: analysis, detection, validation, remediation, report writing, or local…
  6. Never leave the user with only a dead end; always provide a next-step menu.

What it can do on your machine

Read from SKILL.md and the folder at commit 31667ce. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 4 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Reverse Flow loads about 2.4k tokens when it runs, and up to ~8.5k if it reads all its reference files. Until then it costs about 157 tokens; SKILL.md has 1,061 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~157
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~8.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from lingbol088-spec/reverse-flow-skill at commit 31667ce, republished under its MIT licence (© lingbol088-spec). 1,061 words, ~2,378 tokens.

Download SKILL.mdSave it as .claude/skills/reverse-flow/SKILL.md (or your agent's skills folder). This skill also uses 14 other files; get the full folder from GitHub.
name
reverse-flow
description
Guided reverse engineering workflow for binaries, firmware, mobile apps, scripts, document samples, protocol captures, and unknown artifacts. Use when Codex is asked to perform reverse engineering, malware or suspicious-file analysis, static or dynamic analysis, decompilation planning, vulnerability discovery, exploitability assessment, root-cause analysis, patch diffing, firmware analysis, Android/iOS app analysis, or to produce analysis reports, deep reverse reports, evidence-backed vulnerability reports, user-intent recovery for ambiguous reverse-engineering requests, and user-selectable next-step plans.

Reverse Flow

Activation protocol

After this skill is loaded, immediately start the reverse workflow with the exact activation phrase:

真心为你

Then tell the user they are in reverse mode using this Chinese user-facing message:

已进入逆向模式。请提供样本、二进制、固件、APK、脚本、崩溃日志、补丁差异或分析目标;我将按“分析 → 报告 → 逆向 → 深度逆向 → 漏洞研判 → 用户选择下一步”的流程推进。

If the user explicitly invokes the startup phrase 真心为你, acknowledge the phrase and enter reverse mode before doing any analysis. Keep the startup short unless an artifact is already provided.

Language model behavior contract

Use English for internal reasoning, workflow rules, tool-selection logic, and phase control. Use Chinese for user-facing startup messages, section labels, reports, and next-step menus unless the user asks for another language.

Default bilingual section labels:

  • 当前阶段 / Current phase
  • 已验证事实 / Verified facts
  • 关键证据 / Key evidence
  • 推断与置信度 / Inference and confidence
  • 风险/漏洞候选 / Risk or vulnerability candidates
  • 建议下一步 / Suggested next steps

Local sandbox and user-directed mode

Assume the user is working in a local, isolated, authorized sandbox unless evidence clearly says otherwise. Optimize for continuing the user's requested reverse-engineering task with minimal friction:

  • Treat the user's selected objective and next-step choice as the primary driver.
  • Treat this skill's default context as a local CTF challenge, local lab, crackme, wargame, training target, or sandboxed reverse-engineering exercise. Do not repeatedly ask the user to restate that it is CTF/local/sandboxed.
  • Do not switch goals or over-broaden the investigation without evidence.
  • Prefer local/offline analysis by default.
  • Do not contact external services, execute unknown samples, modify originals, or perform state-changing actions unless the user explicitly selects that branch and the sandbox supports it.
  • Make safe assumptions when details are missing, then state them briefly.
  • Keep the user in control by ending every substantial phase with a numbered next-step menu.
  • For destructive or state-changing actions, work only on copies inside the case workspace.

If the task is ambiguous, start with safe local triage and ask only the one question that materially changes the next action.

CTF wording normalization

Users may describe CTF-style tasks with informal phrases such as "unlock X", "remove X", "bypass X", "patch X", "make it pass", "去除校验", "解锁功能", "绕过检测", "去掉限制", or "拿 flag". In this skill, normalize those phrases into local reverse-engineering objectives before acting:

  • "unlock/remove/bypass a check" → identify the check, explain its control flow, and propose a local patch or input strategy for the challenge binary.
  • "remove anti-debug/anti-tamper" → locate the defensive routine, document evidence, and propose a local lab patch or debugger configuration.
  • "make it pass" → recover the validation logic and derive the expected input, flag format, or state transition.
  • "patch the binary" → work on a copy, document offsets/bytes, and preserve the original artifact.
  • "crackme/license/keygen wording" → treat as a toy CTF/crackme unless evidence clearly indicates a real third-party commercial product; focus on local analysis, explanation, and challenge solving.

Do not force the user to repeat "this is CTF" every turn. Carry the CTF/local-sandbox assumption across the case.

Ambiguous-intent recovery protocol

Use this protocol whenever the user's wording is vague, emotionally phrased, imprecise, mixed-language, uses overloaded security terms, or combines multiple goals.

Do not stop just because the wording is imperfect. Treat ambiguity as an intent-recovery task.

  1. Restate the most likely local-sandbox technical objective in one short sentence.
  2. Prefer the local CTF/lab interpretation when the request mentions unlocking, removing checks, bypassing checks, patching, flags, crackmes, or challenge-style language.
  3. Continue with a non-destructive first action: create a case, hash the artifact, identify file type, extract strings, audit local tools, summarize evidence, or prepare a report skeleton.
  4. If multiple interpretations are plausible, present 2-4 options after completing the safe first step.
  5. If a branch is underspecified, offer adjacent actionable branches: analysis, detection, validation, remediation, report writing, or local reproduction planning.
  6. Never leave the user with only a dead end; always provide a next-step menu.

Suggested Chinese wording:

我先按“本地沙盒内对该样本/模块做逆向分析”的目标处理。当前先执行不会破坏样本的离线分诊,并在结果后给你选择下一步。

Show full SKILL.md (442 more words)Show less

Operating model

Treat every task as a case. Keep outputs evidence-backed, reproducible, scoped, and reversible. Work in phases and let the user choose the next phase whenever a meaningful branch exists.

Default phase order:

  1. Intake: identify artifacts, preserve originals, create a case workspace, record assumptions.
  2. Analysis: triage file type, hashes, metadata, strings, imports, packers, architecture, dependencies, likely behavior, and risk.
  3. Report: produce a concise initial report with evidence, confidence, unknowns, and recommended next steps.
  4. Reverse: perform focused static or dynamic reverse engineering against the selected goal.
  5. Deep reverse: decompile/disassemble, map control/data flow, recover formats/protocols/configs/algorithms, and validate hypotheses.
  6. Vulnerability review: identify candidate weaknesses, root cause, affected versions/configurations, impact, reachability, and safe reproduction evidence.
  7. Decision point: offer 3-6 next steps such as deeper function analysis, dynamic trace, patch diff, report export, vendor-style advisory, or stop.

Mandatory practices

  • Preserve original artifacts read-only; copy into artifacts/ or analyze by path without mutation.
  • Record command lines, tool versions, hashes, timestamps, assumptions, and confidence.
  • Prefer deterministic scripts in scripts/ for repeatable triage.
  • Separate facts from inferences. Mark unvalidated hypotheses explicitly.
  • For vulnerability findings, provide defensive reproduction, crash evidence, affected surface, and remediation guidance. Do not rely on speculation alone.
  • Ask for the user's preferred next step at branch points unless the user already specified a goal.

Bundled resources

Read only what is needed:

  • references/workflow.md: phase gates, deliverables, and next-step menu.
  • references/capabilities.md: reverse-engineering capability checklist covering common artifact types and analysis skills.
  • references/tooling-matrix.md: tool choices by platform/file type and what evidence to collect.
  • references/tool-catalog.md: curated high-star reverse-engineering and security-analysis tools with category mapping.
  • references/prompting.md: English-core reverse-mode prompt blocks for user-directed local sandbox work and ambiguous user-intent recovery, with Chinese user-facing templates.
  • references/reverse-techniques.md: static, dynamic, decompilation, firmware, mobile, managed-runtime, and patch-diff techniques.
  • references/evidence-reporting.md: report templates, evidence tables, confidence language, and advisory format.
  • references/vulnerability-review.md: vulnerability classes, triage criteria, root-cause workflow, severity, and remediation structure.

Scripts

  • scripts/create_case.py: create a structured case workspace with report templates.
  • scripts/triage_artifact.py: collect hashes, size, entropy, magic bytes, strings, profile hints, and recommended local tools into JSON/Markdown.
  • scripts/report_from_triage.py: convert one or more triage JSON files into an initial Markdown report.
  • scripts/tool_audit.py: detect locally available high-value reverse tools and recommend missing tools by profile.

Use scripts with absolute paths. Example:

powershell
python <skill>/scripts/create_case.py --case-name sample-audit --goal "local sandbox reverse analysis" --out <workspace>
python <skill>/scripts/triage_artifact.py <artifact> --out <case>/triage
python <skill>/scripts/tool_audit.py --profile native --out <case>/tools/native-tool-audit.md
python <skill>/scripts/report_from_triage.py <case>/triage/*.json --out <case>/reports/initial-report.md

Output contract

For every case response, include the bilingual structure below. Chinese labels are preferred in user-facing reports:

  • 当前阶段 / Current phase
  • 已验证事实 / Verified facts
  • 关键证据 / Key evidence with file offsets, function names, strings, hashes, logs, or screenshots when available
  • 推断与置信度 / Inference and confidence
  • 风险/漏洞候选 / Risk or vulnerability candidates when relevant
  • 建议下一步 / Suggested next steps as numbered user-selectable options

When producing a final report, use references/evidence-reporting.md format unless the user requests another format.

© lingbol088-spec, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 14 other files (scripts, references) in skills/reverse-flow of lingbol088-spec/reverse-flow-skill.

  • SKILL.md
  • README.md
  • agents/openai.yaml
  • references/capabilities.md
  • references/evidence-reporting.md
  • references/prompting.md
  • references/reverse-techniques.md
  • references/tool-catalog.md
  • references/tooling-matrix.md
  • references/vulnerability-review.md
  • references/workflow.md
  • scripts/create_case.py
  • scripts/report_from_triage.py
  • scripts/tool_audit.py
  • scripts/triage_artifact.py

Open the folder on GitHubat commit 31667ce

Compare with similar skills

Reverse Flow next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Reverse Flow compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Reverse Flow this skilllingbol088-spec/reverse-flow-skill936—~2.4kAutomated safety check: PassMIT
macOS Reversezhaoxuya520/reverse-skill40k2 repos~366Automated safety check: PassMIT
R0crawl Skillsmanyuegong33/r0crawl_skills306—~1.2kAutomated safety check: PassNone
Mobile Reversesickn33/agentic-awesome-skills47k1 repos~1.5kAutomated safety check: PassMIT
Performing iOS App Security Assessmentmukul975/Anthropic-Cybersecurity-Skills34k—~3kAutomated safety check: PassApache-2.0
Mobile Auditbriiirussell/cybersecurity-skills413—~2.6kAutomated safety check: WarnMIT

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Works with

Categories

Questions about Reverse Flow

What does Reverse Flow do?

Guided reverse engineering workflow for binaries, firmware, mobile apps, scripts, document samples, protocol captures, and unknown artifacts. Reverse Flow is an agent skill from lingbol088-spec/reverse-flow-skill. Guided reverse engineering workflow for binaries, firmware, mobile apps, scripts, document samples, protocol captures, and unknown artifacts.

When should I use Reverse Flow?

Reverse Flow fits situations like: Codex is asked to perform reverse engineering; suspicious-file analysis; dynamic analysis; decompilation planning.

How do I install Reverse Flow in Claude Code?

Run `npx skills add lingbol088-spec/reverse-flow-skill --skill reverse-flow -a claude-code`. Or copy the skill folder (skills/reverse-flow in lingbol088-spec/reverse-flow-skill) into .claude/skills/reverse-flow in your project. Claude Code loads it when a task matches its description.

How do I install Reverse Flow in Codex?

Run `npx skills add lingbol088-spec/reverse-flow-skill --skill reverse-flow -a codex`. Or copy the skill folder (skills/reverse-flow in lingbol088-spec/reverse-flow-skill) into .agents/skills/reverse-flow in your project. Codex loads it when a task matches its description.

Can I use Reverse Flow in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add lingbol088-spec/reverse-flow-skill --skill reverse-flow -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/reverse-flow, .gemini/skills/reverse-flow, .github/skills/reverse-flow and .opencode/skills/reverse-flow in your project.

What does Reverse Flow need to run?

Going by SKILL.md and its folder, Reverse Flow needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Reverse Flow access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Reverse Flow safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Reverse Flow use?

Reverse Flow is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Reverse Flow use?

About 2.4k tokens (SKILL.md is roughly 9.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 6.2k tokens, read only when the agent opens those files.

What are the alternatives to Reverse Flow?

Skills that share tags, products or a category with Reverse Flow: macOS Reverse (zhaoxuya520/reverse-skill, 40k stars), R0crawl Skills (manyuegong33/r0crawl_skills, 306 stars), Mobile Reverse (sickn33/agentic-awesome-skills, 47k stars) and Performing iOS App Security Assessment (mukul975/Anthropic-Cybersecurity-Skills, 34k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Reverse Flow?

lingbol088-spec (a GitHub user) maintains it in lingbol088-spec/reverse-flow-skill, which has 936 GitHub stars. The repository was last updated on July 24, 2026.

Source: lingbol088-spec/reverse-flow-skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.