RRepoGEO

REPOGEO REPORT · LITE

e-p-armstrong/augmentoolkit

Default branch master · commit ec18b905 · scanned 6/27/2026, 9:37:19 AM

GitHub: 1,858 stars · 246 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 e-p-armstrong/augmentoolkit, 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 README H1 and opening paragraph for fine-tuning LLMs

    Why:

    CURRENT
    # Augmentoolkit - Data for Domain-expert AI
Augmentoolkit creates domain-expert datasets that update an AI's brain (basically, its knowledge cutoff), so that the AI becomes an expert in an area of your choosing. You upload documents, and press a button. And get a fully trained custom LLM. Now every aspect of your AI's behavior and understanding is under your control. Better still, Augmentoolkit **optionally works offline on your computerno external API key required* for datagen† on most hardware.
    COPY-PASTE FIX
    # Augmentoolkit: Fine-tune LLMs & Create Custom Domain-Expert AIs Offline
Augmentoolkit is an end-to-end solution for fine-tuning large language models (LLMs) on your own documents, enabling you to create custom, domain-expert AIs. You upload documents, and press a button to get a fully trained custom LLM. Now every aspect of your AI's behavior and understanding is under your control. Better still, Augmentoolkit **optionally works offline on your computer—no external API key required* for datagen† on most hardware.
  • mediumabout#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/e-p-armstrong/augmentoolkit#readme
  • mediumtopics#3
    Add more specific topics to reinforce LLM fine-tuning and custom AI creation

    Why:

    CURRENT
    ai, dataset-generation, finetuning-llms
    COPY-PASTE FIX
    ai, dataset-generation, finetuning-llms, custom-llm, llm-training, offline-llm

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 e-p-armstrong/augmentoolkit
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI API
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 1×
  2. Anthropic Claude API · recommended 1×
  3. Pinecone · recommended 1×
  4. Weaviate · recommended 1×
  5. Chroma · recommended 1×
  • CATEGORY QUERY
    How can I train an LLM on my own documents to become a domain expert?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Anthropic Claude API
    3. Pinecone
    4. Weaviate
    5. Chroma
    6. Qdrant
    7. GPT-4
    8. Claude 3 Opus
    9. GPT-3.5 Turbo
    10. Claude 3 Sonnet
    11. Claude 3 Haiku
    12. LangChain
    13. LlamaIndex
    14. OpenAI Fine-tuning API
    15. Google Cloud Vertex AI
    16. PaLM 2
    17. Gemini
    18. Azure OpenAI Service
    19. Llama 2
    20. Mistral
    21. Gemma
    22. Hugging Face Transformers library
    23. PyTorch
    24. TensorFlow
    25. bitsandbytes
    26. PEFT library
    27. DeepSpeed
    28. Megatron-LM

    AI recommended 28 alternatives but never named e-p-armstrong/augmentoolkit. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help generate custom datasets for fine-tuning large language models offline?
    you: not recommended
    AI recommended (in order):
    1. Snorkel
    2. Argilla
    3. Prodigy
    4. Label Studio
    5. nlpaug
    6. TextAttack
    7. Python
    8. pandas
    9. spaCy
    10. NLTK

    AI recommended 10 alternatives but never named e-p-armstrong/augmentoolkit. 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 e-p-armstrong/augmentoolkit?
    pass
    AI named e-p-armstrong/augmentoolkit explicitly

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

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

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

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e-p-armstrong/augmentoolkit — 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