RRepoGEO

REPOGEO REPORT · LITE

yaodongC/awesome-instruction-dataset

Default branch main · commit 5ef69722 · scanned 5/17/2026, 5:02:45 PM

GitHub: 1,148 stars · 56 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)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
22 /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
1 / 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 yaodongC/awesome-instruction-dataset, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0).
  • highreadme#2
    Clarify README H1 to emphasize "curated collection"

    Why:

    CURRENT
    # awesome-text/visual-instruction-tuning-dataset
    COPY-PASTE FIX
    # Awesome Instruction Dataset: A Curated Collection for LLM Training
  • mediumtopics#3
    Correct typo and add missing topic for better categorization

    Why:

    CURRENT
    ["awsome-lists", "datasets", "gpt-3", "gpt-4", "instruction-following", "instruction-tuning", "language-model", "llama"]
    COPY-PASTE FIX
    ["awesome-lists", "datasets", "gpt-3", "gpt-4", "instruction-following", "instruction-tuning", "language-model", "llama", "multi-modal"]

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 yaodongC/awesome-instruction-dataset
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Alpaca-GPT4
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Alpaca-GPT4 · recommended 1×
  2. ShareGPT · recommended 1×
  3. Dolly 2.0 · recommended 1×
  4. FLAN · recommended 1×
  5. LIMA · recommended 1×
  • CATEGORY QUERY
    Where can I find open-source datasets for training instruction-following large language models?
    you: not recommended
    AI recommended (in order):
    1. Alpaca-GPT4
    2. ShareGPT
    3. Dolly 2.0
    4. FLAN
    5. LIMA
    6. WizardLM
    7. Self-Instruct

    AI recommended 7 alternatives but never named yaodongC/awesome-instruction-dataset. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good multi-modal instruction tuning datasets for developing chat-based AI assistants?
    you: not recommended
    AI recommended (in order):
    1. LLaVA (Large Language and Vision Assistant) Dataset
    2. MiniGPT-4 Dataset
    3. Visual Instruction Tuning (VIT) Dataset (from LLaVA-1.5)
    4. ShareGPT4V
    5. M3IT (Multi-Modal Multi-Task Instruction Tuning) Dataset
    6. Fuyu-8B Dataset (from Adept AI)

    AI recommended 6 alternatives but never named yaodongC/awesome-instruction-dataset. 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 yaodongC/awesome-instruction-dataset?
    pass
    AI did not name yaodongC/awesome-instruction-dataset — 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 yaodongC/awesome-instruction-dataset in production, what risks or prerequisites should they evaluate first?
    pass
    AI named yaodongC/awesome-instruction-dataset 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 yaodongC/awesome-instruction-dataset solve, and who is the primary audience?
    pass
    AI did not name yaodongC/awesome-instruction-dataset — 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?

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yaodongC/awesome-instruction-dataset — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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yaodongC/awesome-instruction-dataset — RepoGEO report