RRepoGEO

REPOGEO REPORT · LITE

vgel/repeng

Default branch main · commit 0ba7196d · scanned 6/11/2026, 7:43:03 PM

GitHub: 731 stars · 64 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 vgel/repeng, 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's opening statement to clarify its core purpose

    Why:

    CURRENT
    A Python library for generating control vectors with representation engineering.
    COPY-PASTE FIX
    A Python library for generating and applying **control vectors to steer the behavior of large language models (LLMs)** using Representation Engineering (RepE).
  • mediumreadme#2
    Add a brief "What is Representation Engineering?" section

    Why:

    COPY-PASTE FIX
    ## What is Representation Engineering?
This library implements the Representation Engineering (RepE) technique, allowing you to programmatically influence the specific behavior of large language models (LLMs) by training and applying 'control vectors' to their internal representations. For a deeper dive, see the [blog post](https://vgel.me/posts/representation-engineering/).
  • lowreadme#3
    Add a "Comparison to Alternatives" section

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives
While `repeng` leverages the Hugging Face `transformers` library, it differs from general fine-tuning libraries like `PEFT` (which modifies model weights) or interpretability tools like `TransformerLens`. `repeng` focuses specifically on **runtime steering of LLM outputs** by applying learned control vectors to internal activations, offering a distinct approach to influencing model behavior without retraining.

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 vgel/repeng
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/peft
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/peft · recommended 2×
  2. huggingface/transformers · recommended 2×
  3. neelnanda-io/TransformerLens · recommended 1×
  4. pytorch/pytorch · recommended 1×
  5. tensorflow/tensorflow · recommended 1×
  • CATEGORY QUERY
    Python library for generating control vectors to steer transformer model outputs?
    you: not recommended
    AI recommended (in order):
    1. PEFT (huggingface/peft)
    2. TransformerLens (neelnanda-io/TransformerLens)
    3. Hugging Face Transformers library (huggingface/transformers)
    4. PyTorch (pytorch/pytorch)
    5. TensorFlow (tensorflow/tensorflow)
    6. Accelerate (huggingface/accelerate)

    AI recommended 6 alternatives but never named vgel/repeng. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to programmatically influence the specific behavior of a large language model?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4
    2. Anthropic Claude 3
    3. Google Gemini
    4. OpenAI's Chat Completion API
    5. OpenAI Fine-tuning API
    6. Google Cloud Vertex AI Custom Models
    7. Hugging Face Transformers (huggingface/transformers)
    8. PaLM 2
    9. Llama 2
    10. Mistral
    11. LangChain (langchain-ai/langchain)
    12. LlamaIndex (run-llama/llama_index)
    13. Pinecone
    14. Weaviate (weaviate/weaviate)
    15. ChromaDB (chroma-core/chroma)
    16. Pydantic (pydantic/pydantic)
    17. Instructor (jxnl/instructor)
    18. ChatGPT
    19. Hugging Face's TRL (Transformer Reinforcement Learning) (huggingface/trl)
    20. Hugging Face PEFT library (huggingface/peft)
    21. LoRA
    22. Falcon

    AI recommended 22 alternatives but never named vgel/repeng. This is the gap to close.

    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 vgel/repeng?
    pass
    AI named vgel/repeng explicitly

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

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