RRepoGEO

REPOGEO REPORT · LITE

microsoftarchive/promptbench

Default branch main · commit fcda538b · scanned 6/27/2026, 12:26:55 PM

GitHub: 2,811 stars · 220 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)

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

AI VISIBILITY SCORE
61 /100
Needs work
Category recall
1 / 2
Avg rank #3.0 when recommended
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 microsoftarchive/promptbench, 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
  • highabout#1
    Reposition the 'About' description to clarify its nature as an open-source library

    Why:

    CURRENT
    A unified evaluation framework for large language models
    COPY-PASTE FIX
    An open-source Python library and unified evaluation framework for large language models, designed for researchers and developers.
  • mediumreadme#2
    Add a statement clarifying active maintenance despite 'microsoftarchive' designation

    Why:

    COPY-PASTE FIX
    Add a prominent note near the top of the README, for example: 'Note on Repository Status: Despite the 'microsoftarchive' designation, this repository is actively maintained with regular updates and contributions. Please refer to the 'News and Updates' section for recent developments.'
  • lowtopics#3
    Add more specific topics to reinforce its identity as a code library/framework

    Why:

    CURRENT
    adversarial-attacks, benchmark, chatgpt, evaluation, large-language-models, prompt, prompt-engineering, robustness
    COPY-PASTE FIX
    adversarial-attacks, benchmark, chatgpt, evaluation, large-language-models, prompt, prompt-engineering, robustness, llm-evaluation-framework, python-library, open-source

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
1 / 2
50% of queries surface microsoftarchive/promptbench
Avg rank
#3.0
Lower is better. #1 = top recommendation.
Share of voice
7%
Of all named tools, what % are you?
Top rival
Weights & Biases
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Weights & Biases · recommended 1×
  2. Arize AI (Phoenix) · recommended 1×
  3. LangChain (LangSmith) · recommended 1×
  4. Galileo (LLM Studio) · recommended 1×
  5. Humanloop · recommended 1×
  • CATEGORY QUERY
    Seeking a unified platform to evaluate and understand large language model behavior.
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases
    2. Arize AI (Phoenix)
    3. LangChain (LangSmith)
    4. Galileo (LLM Studio)
    5. Humanloop
    6. MLflow
    7. DeepEval

    AI recommended 7 alternatives but never named microsoftarchive/promptbench. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to assess the robustness of large language models against adversarial prompt attacks?
    you: #3
    AI recommended (in order):
    1. Garak (llm-attacks/garak)
    2. Adversarial GLUE (AdvGLUE)
    3. PromptBench (microsoft/promptbench) ← you
    4. TextAttack (textattack/textattack)
    5. Robustness Gym (RobustnessGym/RobustnessGym)
    6. OpenAI Evals (openai/evals)
    7. DeepMind's "Safety Gym"
    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 microsoftarchive/promptbench?
    pass
    AI named microsoftarchive/promptbench explicitly

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

  • If a team adopts microsoftarchive/promptbench in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name microsoftarchive/promptbench — likely talking about a different project

    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 microsoftarchive/promptbench solve, and who is the primary audience?
    pass
    AI named microsoftarchive/promptbench explicitly

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

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microsoftarchive/promptbench — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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