RRepoGEO

REPOGEO REPORT · LITE

tophant-ai/promptbeat

Default branch main · commit a65f874e · scanned 6/15/2026, 2:31:54 AM

GitHub: 777 stars · 0 forks

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 tophant-ai/promptbeat, 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
    Rephrase README to emphasize the repo's role in enabling Promptbeat's safety evaluation

    Why:

    CURRENT
    # Promptbeat

Promptbeat is a scenario-driven safety evaluation toolkit for LLMs and agent applications. It starts from targets, scenarios, seeds, and dataset subscriptions, then generates and evaluates adversarial cases against real model or agent targets.

## What This Repository Contains / 仓库内容

This public repository is for documentation, website content, and runnable configuration examples. It is not the full product source release.
    COPY-PASTE FIX
    # Promptbeat

Promptbeat is a scenario-driven safety evaluation toolkit for LLMs and agent applications. It starts from targets, scenarios, seeds, and dataset subscriptions, then generates and evaluates adversarial cases against real model or agent targets.

This repository provides essential resources for leveraging Promptbeat's capabilities: comprehensive documentation, website content, and runnable configuration examples. While it does not contain the full product source code, it enables users to understand, configure, and apply Promptbeat for robust AI safety evaluation.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT, Apache-2.0, or a custom license if applicable) in the repository root to clearly state the terms of use.
  • mediumabout#3
    Refine the repository description for clarity and keyword inclusion

    Why:

    CURRENT
    Break your AI before they do.
    COPY-PASTE FIX
    Scenario-driven safety evaluation toolkit for LLMs and AI agents, providing documentation and examples for adversarial testing and robustness assessment.

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 tophant-ai/promptbeat
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Adversarial Robustness Toolbox (ART)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Adversarial Robustness Toolbox (ART) · recommended 1×
  2. TextAttack · recommended 1×
  3. OpenAI Evals · recommended 1×
  4. Garak · recommended 1×
  5. Robustness Gym · recommended 1×
  • CATEGORY QUERY
    How to evaluate the safety and robustness of large language models against adversarial attacks?
    you: not recommended
    AI recommended (in order):
    1. Adversarial Robustness Toolbox (ART)
    2. TextAttack
    3. OpenAI Evals
    4. Garak
    5. Robustness Gym
    6. CheckPoint

    AI recommended 6 alternatives but never named tophant-ai/promptbeat. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking tools for scenario-driven security testing of AI agent applications and their boundaries.
    you: not recommended
    AI recommended (in order):
    1. OWASP Top 10 for LLM Applications
    2. Giskard (GiskardAI/giskard)
    3. Adversa.AI Platform
    4. Robust Intelligence (RI) Platform
    5. Microsoft Counterfit (Azure/counterfit)
    6. American Fuzzy Lop (AFL++) (AFLplusplus/AFLplusplus)
    7. libFuzzer (llvm/llvm-project)
    8. LangChain (langchain-ai/langchain)
    9. OpenAI API
    10. Hugging Face Transformers (huggingface/transformers)

    AI recommended 10 alternatives but never named tophant-ai/promptbeat. 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 tophant-ai/promptbeat?
    pass
    AI named tophant-ai/promptbeat explicitly

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

  • If a team adopts tophant-ai/promptbeat in production, what risks or prerequisites should they evaluate first?
    pass
    AI named tophant-ai/promptbeat 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 tophant-ai/promptbeat solve, and who is the primary audience?
    pass
    AI named tophant-ai/promptbeat explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite