
marcosd4h/deepextractruntime
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AI-driven agent runtime for Windows PE binary analysis. Turns IDA Pro decompiled code and SQLite databases produced by DeepExtractIDA into structured intelligence through specialized agents, analysis...
Обзор
is an AI-driven binary analysis toolkit that operates on top of DeepExtractIDA extraction outputs. It transforms per-binary SQLite databases, decompiled C++ source files, and JSON metadata into a queryable runtime with slash commands, specialized subagents, analysis skills, lifecycle hooks, and a shared helper library. AI coding agents (Cursor, Claude Code, Codex) parse source code repositories effectively: they resolve imports, follow type definitions, and navigate call hierarchies through language servers and syntax trees. Compiled binaries present a structural gap. The cross-references, PE metadata, assembly instructions, and stack frame layouts required for binary analysis are locked inside reverse engineering frameworks and are inaccessible to these agents through their native code navigation tools. Decompiled C++ output compounds the problem: it consists of isolated function definitions grouped into flat .
README
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DeepExtract Agent Analysis Runtime
Overview
DeepExtract Agent Analysis Runtime is an AI-driven binary analysis toolkit that operates on top of DeepExtractIDA extraction outputs. It transforms per-binary SQLite databases, decompiled C++ source files, and JSON metadata into a queryable runtime with slash commands, specialized subagents, analysis skills, lifecycle hooks, and a shared helper library.
AI coding agents (Cursor, Claude Code, Codex) parse source code repositories effectively: they resolve imports, follow type definitions, and navigate call hierarchies through language servers and syntax trees. Compiled binaries present a structural gap. The cross-references, PE metadata, assembly instructions, and stack frame layouts required for binary analysis are locked inside reverse engineering frameworks and are inaccessible to these agents through their native code navigation tools. Decompiled C++ output compounds the problem: it consists of isolated function definitions grouped into flat .cpp files with no project structure, no #include headers, and no shared type definitions. Standard code indexing mechanisms (LSPs, Tree-sitter parsers, embedding-based search) fail to resolve cross-references across these files, forcing the agent to fall back to unreliable text search for callgraph traversal.
DeepExtractIDA addresses the extraction side by running a deterministic pipeline through IDA Pro 9.x and the Hex-Rays decompiler, producing structured SQLite databases with full function records, cross-reference tables, PE metadata, and JSON indexes. The Agent Analysis Runtime addresses the consumption side: it provides deterministic Python scripts that query those databases directly, replacing semantic search with structured tool invocation. The agent invokes a skill script through its shell tool, the script queries the database, and the agent reasons on the structured result. Large payloads (function bodies, callgraph data, scan results) remain on disk in workspace directories; the agent operates on compact summaries and loads full data on demand.
The runtime deploys as a .claude/ directory alongside the extraction data and operates across Claude Code, Cursor, Codex, and any AI coding environment that supports CLAUDE.md or equivalent agent configuration.
DeepExtractIDA does not write the runtime bootstrap by default. Headless extraction must pass -EnableAI so the output directory contains CLAUDE.md. Without that flag the extraction data is still produced, but the runtime cannot be installed through the agent bootstrap flow.
New here? Start with the Onboarding Guide.
How It Works
The runtime is organized into five layers. Each layer depends only on the layers below it.
User
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Slash Commands (/triage, /audit, /scan, ...)
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Specialized Agents (re-analyst, security-auditor, ...)
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Установка
npx skillfish add marcosd4h/deepextractruntime