deep-research-prompt

Generate structured research prompts from DeepExtractIDA analysis outputs.

17|3|Updated Feb 22, 2026

Search Tags:#automation #orchestration #reverse-engineering #deep-research #prompt-generation #cross-module #module-analysis

Ready to run

npx skills add https://github.com/marcosd4h/DeepExtractRuntime --skill deep-research-prompt

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: deep-research-prompt
Source: https://github.com/marcosd4h/DeepExtractRuntime/tree/main/skills/deep-research-prompt
Command: npx skills add https://github.com/marcosd4h/DeepExtractRuntime --skill deep-research-prompt

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Generate comprehensive, evidence-based deep research prompts by coordinating all available analysis skills to gather maximum context from DeepExtractIDA outputs and synthesize prompts and reports.

Core Features & Use Cases

Orchestrates multiple analysis skills (classification, call graph tracing, data flow, string intelligence, and module context) to produce a unified research prompt.
Synthesizes the gathered context into structured research prompts and detailed cross-module reports.
Supports area-focused prompts and cross-module tracing to understand function behavior across DLL boundaries.

Quick Start

Run the Deep Research Prompt Generator to produce a structured research prompt for a target function.

Frequently Asked Questions about deep-research-prompt

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate deep research prompts for reverse engineering a function across DLL boundaries?▼

To generate deep research prompts across DLL boundaries, you orchestrate analysis skills to aggregate call graphs, data flow, and strings from DeepExtractIDA outputs into a structured, evidence-based prompt.

What is the best way to gather cross-module context for reverse engineering research plans?▼

Gathering cross-module context for research plans involves orchestrating classification, call graph tracing, and string intelligence skills to synthesize a comprehensive prompt from existing analysis databases.

Can I produce detailed research reports alongside prompts for module analysis?▼

Yes, you can produce a detailed research report alongside the ready-to-use prompt, aggregating cross-module data and exposing dependencies on supported scripts for reproducible workflows.

Does deep research prompt generation work without prior reverse engineering analysis outputs?▼

No, deep research prompt generation requires prior DeepExtractIDA analysis outputs as dependencies to gather the maximum context needed for synthesizing evidence-based prompts and reports.

Why do I need to orchestrate multiple analysis skills for cross-module reverse engineering?▼

Orchestrating multiple analysis skills is needed for cross-module reverse engineering to aggregate classification, call graphs, and data flow into a unified, comprehensive research prompt rather than isolated data points.

Are there limitations when tracing function behavior across DLL boundaries using automated prompt generation?▼

Limitations include relying entirely on the completeness of upstream analysis databases; if cross-module data flow or string intelligence outputs are missing, the generated prompt will lack comprehensive context.