RRepoGEO

REPOGEO REPORT · LITE

benchflow-ai/skillsbench

Default branch main · commit 5051af09 · scanned 6/26/2026, 12:27:03 AM

GitHub: 1,388 stars · 319 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
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 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 the repository

    Why:

    COPY-PASTE FIX
    ai-agents, agent-evaluation, llm-benchmarking, skill-evaluation, agent-skills, generative-ai
  • highreadme#2
    Reposition the README's opening sentence to clarify its specific niche

    Why:

    CURRENT
    The first benchmark for evaluating how well AI agents use skills.
    COPY-PASTE FIX
    The first benchmark specifically designed to evaluate how effectively AI agents leverage modular skills for complex, multi-step workflows.
  • mediumcomparison#3
    Add a 'Why SkillsBench?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Why SkillsBench?
Unlike general ML experiment tracking platforms (e.g., MLflow, Weights & Biases) or traditional reinforcement learning environments (e.g., OpenAI Gym, Meta-World), SkillsBench focuses exclusively on evaluating AI agents' ability to effectively use and compose modular skills for complex, real-world tasks. We provide a standardized benchmark for agent skill effectiveness, not just general model performance.

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. wandb/wandb · recommended 2×
  3. langchain-ai/langchain · recommended 1×
  4. LangSmith · recommended 1×
  5. openai/evals · recommended 1×
  • CATEGORY QUERY
    How to benchmark AI agent skill usage and evaluate their effectiveness in specialized workflows?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LangSmith
    3. MLflow (mlflow/mlflow)
    4. Weights & Biases (wandb/wandb)
    5. OpenAI Evals (openai/evals)
    6. Pandas (pandas-dev/pandas)
    7. NumPy (numpy/numpy)
    8. Matplotlib (matplotlib/matplotlib)
    9. Seaborn (mwaskom/seaborn)

    AI recommended 9 alternatives but never named benchflow-ai/skillsbench. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools exist for evaluating how well AI agents leverage modular skills for complex tasks?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym / Farama Foundation Gym (Farama-Foundation/Gymnasium)
    2. Meta-World (rlworkgroup/metaworld)
    3. Procgen Benchmark (openai/procgen)
    4. Ray RLlib (ray-project/ray)
    5. MLflow (mlflow/mlflow)
    6. Weights & Biases (wandb/wandb)
    7. Unity ML-Agents (Unity-Technologies/ml-agents)
    8. NVIDIA Isaac Sim (NVIDIA-Omniverse/IsaacSim)

    AI recommended 8 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 named benchflow-ai/skillsbench explicitly

    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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benchflow-ai/skillsbench — 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