RRepoGEO

REPOGEO REPORT · LITE

huggingface/upskill

Default branch main · commit 20c0a134 · scanned 6/15/2026, 4:37:39 AM

GitHub: 683 stars · 85 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 huggingface/upskill, 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
    Reposition the README H1 and opening paragraph to clearly state the tool's purpose for agent skills

    Why:

    CURRENT
    # UPskill

Generate and evaluate agent skills based on traces with agents. Create skills with teacher models (expensive/slow) that student models (cheap/fast) can use to perform harder tasks reliably.
    COPY-PASTE FIX
    # UPskill: Generate and Evaluate Agent Skills for Code Agents

UPskill is a framework to generate and evaluate agent skills for code agents like Claude Code, Open Code, and OpenAI Codex. It enables creating skills with teacher models (expensive/slow) that student models (cheap/fast) can then use to perform harder tasks reliably.
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    agent-skills, code-agents, llm-agents, skill-generation, skill-evaluation, ai-agents, huggingface, python
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://huggingface.co/docs/upskill/en/index

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 huggingface/upskill
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 3×
  2. huggingface/transformers · recommended 2×
  3. OpenAI API · recommended 1×
  4. pytest-dev/pytest · recommended 1×
  5. junit-team/junit5 · recommended 1×
  • CATEGORY QUERY
    How to generate and evaluate custom capabilities for AI coding assistants reliably?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Pytest (pytest-dev/pytest)
    3. JUnit (junit-team/junit5)
    4. Jest (facebook/jest)
    5. Requests-Mock (requests-mock/requests-mock)
    6. Nock (nock/nock)
    7. Scale AI
    8. Appen
    9. LangChain (langchain-ai/langchain)
    10. LangSmith
    11. Hugging Face Transformers (huggingface/transformers)
    12. Semantic Kernel (microsoft/semantic-kernel)
    13. ANTLR (antlr/antlr4)
    14. PLY (dabeaz/ply)
    15. MLflow (mlflow/mlflow)

    AI recommended 15 alternatives but never named huggingface/upskill. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help distill complex agent behaviors from powerful models to smaller ones?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Accelerate (huggingface/accelerate)
    3. PyTorch (pytorch/pytorch)
    4. TensorFlow (tensorflow/tensorflow)
    5. Keras (keras-team/keras)
    6. OpenVINO Toolkit (openvinotoolkit/openvino)
    7. ONNX Runtime (microsoft/onnxruntime)
    8. DistilBERT
    9. DeepSpeed (microsoft/deepspeed)
    10. FairScale (facebookresearch/fairscale)
    11. Ray (ray-project/ray)
    12. Ray Tune (ray-project/ray)
    13. Ray Train (ray-project/ray)

    AI recommended 13 alternatives but never named huggingface/upskill. 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 huggingface/upskill?
    pass
    AI named huggingface/upskill explicitly

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

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

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

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huggingface/upskill — 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