assisting-reverse-engineering
Provides reverse engineering analysis support including function identification, data structure analysis, and behavior understanding. Use when analyzing unknown binaries, understanding program structure, or investigating binary behavior.
Best use case
assisting-reverse-engineering is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Provides reverse engineering analysis support including function identification, data structure analysis, and behavior understanding. Use when analyzing unknown binaries, understanding program structure, or investigating binary behavior.
Teams using assisting-reverse-engineering should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/assisting-reverse-engineering/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How assisting-reverse-engineering Compares
| Feature / Agent | assisting-reverse-engineering | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Provides reverse engineering analysis support including function identification, data structure analysis, and behavior understanding. Use when analyzing unknown binaries, understanding program structure, or investigating binary behavior.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Reverse Engineering Assistance ## Analysis Workflow 1. **Initial survey**: Get function list, extract strings, identify imports and exports, map binary structure 2. **Key function analysis**: Decompile main/entry functions, analyze control flow, identify critical operations, classify functions by purpose 3. **Data flow mapping**: Trace data through functions, identify data structures, map global state, analyze stack layouts 4. **Behavior understanding**: Identify protocol handlers, understand input/output patterns, map to known functionality, reconstruct high-level logic ## Key Capabilities - Function identification: entry points and main functions, common library functions, custom application logic, function classification - Data structure analysis: strings and constants, data structures (structs, arrays), global variables, stack layouts - Pattern recognition: common algorithms (sorting, hashing), protocol implementations, obfuscation techniques, anti-debugging code - Code reconstruction: high-level logic reconstruction, control flow patterns, error handling, mapping to source concepts ## Output Format Report with: binary_summary (type, architecture, language, compiler), key_functions (entry_points, protocol_handlers, utility_functions), data_structures, strings_of_interest, behavior_analysis (protocols, ports, functionality), recommendations. ## Quality Criteria - **Accuracy**: Correct identification of functionality - **Completeness**: Cover all key aspects - **Clarity**: Clear explanations of behavior - **Actionability**: Highlight areas needing review ## See Also - `patterns.md` - Detailed analysis patterns and techniques - `examples.md` - Example analysis cases and output formats - `references.md` - Tools and best practices
Related Skills
Binary Analysis and Reverse Engineering
Systematic approach to analyzing compiled binaries, understanding program behavior, and identifying vulnerabilities without source code access
android-engineering-core
This skill is used to implement Android features within the existing Kotlin, Compose, Room, Hilt and Navigation architecture, including data, navigation and background work.
Chaos Engineering
Design and execute controlled failure experiments to validate system resilience
chaos-engineering-fundamentals
Use when implementing chaos engineering, designing fault injection experiments, or building resilience testing practices. Covers chaos principles and experiment design.
Prompt Engineering Skill
Craft effective prompts that get the best results from language models.
prompt-engineering-openai-api-f7c24501
Log in [Sign up](https://platform.openai.com/signup)
data-engineering-data-pipeline
You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.
context-engineering
Use when designing agent system prompts, optimizing RAG retrieval, or when context is too expensive or slow. Reduces tokens while maintaining quality through strategic positioning and attention-aware design.
Build Your Data Engineering Skill
Create your LLMOps data engineering skill in one prompt, then learn to improve it throughout the chapter
ai-engineering-skill
Practical guide for building production ML systems based on Chip Huyen's AI Engineering book. Use when users ask about model evaluation, deployment strategies, monitoring, data pipelines, feature engineering, cost optimization, or MLOps. Covers metrics, A/B testing, serving patterns, drift detection, and production best practices.
ai-data-engineering
Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).
Data Engineering Data Driven Feature
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication.