RRepoGEO

REPOGEO REPORT · LITE

albertan017/LLM4Decompile

Default branch main · commit 85b364bf · scanned 5/24/2026, 10:42:35 PM

GitHub: 6,665 stars · 529 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface albertan017/LLM4Decompile, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README opening to highlight unique LLM value

    Why:

    CURRENT
    Reverse Engineering: Decompiling Binary Code with Large Language Models
    COPY-PASTE FIX
    LLM4Decompile: Leveraging Large Language Models for more accurate and readable decompilation of *stripped binaries*, addressing challenges where traditional decompilers often struggle without symbol information.
  • mediumcomparison#2
    Add a 'How is LLM4Decompile different?' section to README

    Why:

    COPY-PASTE FIX
    ## How is LLM4Decompile different from traditional decompilers?
LLM4Decompile's core differentiator is its application of Large Language Models (LLMs) to the challenging problem of decompiling *stripped binaries*. Unlike traditional decompilers that struggle significantly without symbol information, this project aims to leverage LLMs to recover meaningful code from binaries where symbols have been removed.
  • lowtopics#3
    Expand GitHub topics for more specific LLM application

    Why:

    CURRENT
    binary, decompile, large-language-models, reverse-engineering
    COPY-PASTE FIX
    binary, decompile, large-language-models, reverse-engineering, llm-for-reverse-engineering, ai-decompiler, stripped-binary-decompilation

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface albertan017/LLM4Decompile
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
IDA Pro
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. IDA Pro · recommended 2×
  2. Hex-Rays Decompiler · recommended 1×
  3. BinDiff · recommended 1×
  4. NationalSecurityAgency/ghidra · recommended 1×
  5. avast-tl/retdec · recommended 1×
  • CATEGORY QUERY
    Looking for an AI-powered tool to decompile executable binaries into readable code.
    you: not recommended
    AI recommended (in order):
    1. IDA Pro
    2. Hex-Rays Decompiler
    3. BinDiff
    4. Ghidra (NationalSecurityAgency/ghidra)
    5. RetDec (avast-tl/retdec)
    6. Angr (angr/angr)
    7. DeepCode
    8. Snyk Code
    9. CodeQL (github/codeql)

    AI recommended 9 alternatives but never named albertan017/LLM4Decompile. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can large language models assist in reverse engineering compiled software?
    you: not recommended
    AI recommended (in order):
    1. Ghidra
    2. IDA Pro
    3. ChatGPT/GPT-4
    4. Binary Ninja
    5. Radare2/Cutter
    6. CodeQL
    7. Pangolin
    8. Voltron

    AI recommended 8 alternatives but never named albertan017/LLM4Decompile. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of albertan017/LLM4Decompile?
    pass
    AI named albertan017/LLM4Decompile explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts albertan017/LLM4Decompile in production, what risks or prerequisites should they evaluate first?
    pass
    AI named albertan017/LLM4Decompile explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo albertan017/LLM4Decompile solve, and who is the primary audience?
    pass
    AI named albertan017/LLM4Decompile explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of albertan017/LLM4Decompile. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
<a href="https://repogeo.com/en/r/albertan017/LLM4Decompile"><img src="https://repogeo.com/badge/albertan017/LLM4Decompile.svg" alt="RepoGEO" /></a>
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albertan017/LLM4Decompile — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite