RRepoGEO

REPOGEO REPORT · LITE

ant4g0nist/lisa.py

Default branch dev · commit 9dd61e9d · scanned 6/10/2026, 7:03:17 PM

GitHub: 751 stars · 113 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 ant4g0nist/lisa.py, 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
    Explicitly name the repo in the README's opening sentence

    Why:

    CURRENT
    This project provides a Model-Context Protocol (MCP) integration for LLDB, allowing AI assistants like Claude to interact with your debugging sessions through a standardized interface.
    COPY-PASTE FIX
    The `ant4g0nist/lisa.py` project provides a Model-Context Protocol (MCP) integration for LLDB, allowing AI assistants like Claude to interact with your debugging sessions through a standardized interface.
  • mediumreadme#2
    Add a 'Why lisa.py?' section to highlight its unique value

    Why:

    COPY-PASTE FIX
    ## Why `lisa.py`?
Unlike general-purpose AI assistants (e.g., Claude, Copilot) or traditional debuggers (e.g., LLDB, PEDA), `lisa.py` acts as a dedicated bridge, enabling direct, natural language interaction between AI and your LLDB debugging sessions. This allows for AI-powered analysis and control within your existing low-level debugging workflow, specifically tailored for exploit development and reverse engineering on platforms like ARM64 macOS.
  • lowabout#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/ant4g0nist/lisa.py

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 ant4g0nist/lisa.py
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Gemini
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Gemini · recommended 2×
  2. GitHub Copilot · recommended 1×
  3. VS Code · recommended 1×
  4. Neovim/Vim · recommended 1×
  5. LLDB · recommended 1×
  • CATEGORY QUERY
    How to integrate AI assistants for debugging low-level code on ARM64 macOS?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot
    2. VS Code
    3. Neovim/Vim
    4. LLDB
    5. ChatGPT/GPT-4
    6. Code Llama / Llama 2
    7. ollama
    8. llama.cpp
    9. Google Gemini
    10. Replit AI (Ghostwriter)

    AI recommended 10 alternatives but never named ant4g0nist/lisa.py. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking tools for natural language interaction with a debugger during exploit development.
    you: not recommended
    AI recommended (in order):
    1. PEDA
    2. GEF
    3. pwndbg
    4. IDA Pro
    5. Ghidra
    6. ChatGPT
    7. Google Gemini

    AI recommended 7 alternatives but never named ant4g0nist/lisa.py. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 ant4g0nist/lisa.py?
    pass
    AI named ant4g0nist/lisa.py explicitly

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

  • If a team adopts ant4g0nist/lisa.py in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ant4g0nist/lisa.py 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 ant4g0nist/lisa.py solve, and who is the primary audience?
    pass
    AI named ant4g0nist/lisa.py explicitly

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

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ant4g0nist/lisa.py — 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