RRepoGEO

REPOGEO REPORT · LITE

benchflow-ai/skillsbench

Default branch main · commit 3c874640 · scanned 5/15/2026, 8:27:55 AM

GitHub: 1,172 stars · 295 forks

AI VISIBILITY SCORE
28 /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
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 benchflow-ai/skillsbench, 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
  • hightopics#1
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    ai-agents, llm-evaluation, agent-benchmarking, skill-evaluation, generative-ai, gym-environment
  • highreadme#2
    Strengthen the opening statement to clarify core purpose

    Why:

    CURRENT
    The first benchmark for evaluating how well AI agents use skills.
    COPY-PASTE FIX
    SkillsBench is the pioneering open-source framework for systematically evaluating how effectively AI agents leverage specialized skills and compose them to perform complex workflows, offering gym-style benchmarking specifically for generative AI agents.
  • mediumreadme#3
    Add a sentence clarifying SkillsBench's unique focus within agent evaluation

    Why:

    COPY-PASTE FIX
    Unlike general LLM evaluation or MLOps platforms, SkillsBench focuses specifically on the nuanced assessment of agent skill utilization and composition in dynamic, interactive environments.

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 benchflow-ai/skillsbench
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
mlflow/mlflow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. mlflow/mlflow · recommended 2×
  2. matplotlib/matplotlib · recommended 2×
  3. mwaskom/seaborn · recommended 2×
  4. ray-project/ray · recommended 2×
  5. wandb/wandb · recommended 1×
  • CATEGORY QUERY
    How can I benchmark the performance and effectiveness of my AI agents' specialized skills?
    you: not recommended
    AI recommended (in order):
    1. MLflow (mlflow/mlflow)
    2. Weights & Biases (W&B) (wandb/wandb)
    3. LangChain Evaluation (langchain-ai/langchain)
    4. Scale AI
    5. Appen
    6. Surge AI
    7. scikit-learn (scikit-learn/scikit-learn)
    8. pandas (pandas-dev/pandas)
    9. matplotlib (matplotlib/matplotlib)
    10. seaborn (mwaskom/seaborn)
    11. OpenAI Evals (openai/openai-evals)
    12. Apache JMeter (apache/jmeter)
    13. Locust (locustio/locust)

    AI recommended 13 alternatives but never named benchflow-ai/skillsbench. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools are available for evaluating AI agent behavior and skill composition in a gym-style environment?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym (openai/gym)
    2. Gymnasium (Farama-Foundation/Gymnasium)
    3. RLlib (ray-project/ray)
    4. Ray Tune (ray-project/ray)
    5. Weights & Biases
    6. TensorBoard (tensorflow/tensorboard)
    7. MLflow (mlflow/mlflow)
    8. Matplotlib (matplotlib/matplotlib)
    9. Seaborn (mwaskom/seaborn)
    10. DeepMind's Acme (deepmind/acme)

    AI recommended 10 alternatives but never named benchflow-ai/skillsbench. 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 benchflow-ai/skillsbench?
    pass
    AI did not name benchflow-ai/skillsbench — 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?

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