REPOGEO REPORT · LITE

yaodongC/awesome-instruction-dataset

Default branch main · commit 5ef69722 · scanned 6/28/2026, 9:43:01 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

highreadme#1

Reposition the README H1 and opening sentence to emphasize it's a *list* or *collection*

Why:

CURRENT

# awesome-text/visual-instruction-tuning-dataset
A collection of open-source instruction tuning datasets to train (text and multi-modal) chat-based LLMs (GPT-4, ChatGPT,LLaMA,Alpaca).

COPY-PASTE FIX

# Awesome Instruction Dataset: A Curated List of Open-Source Datasets
This repository provides a comprehensive, curated list of open-source instruction tuning datasets for training (text and multi-modal) chat-based LLMs (GPT-4, ChatGPT, LLaMA, Alpaca).

highlicense#2

Add a LICENSE file to the repository

Why:

CURRENT

(no LICENSE file detected — the repo has no recognizable license)

COPY-PASTE FIX

Create a `LICENSE` file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0, or GPL-3.0).

mediumhomepage#3

Add a homepage URL to the repository's About section

Why:

COPY-PASTE FIX

Add a relevant URL (e.g., a project website, a related paper, or the GitHub repository URL itself) to the 'Homepage' field in the repository's 'About' section.

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

Of all named tools, what % are you?

Top rival

Alpaca-GPT4

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Alpaca-GPT4 · recommended 1×
Databricks Dolly 2.0 · recommended 1×
OpenAssistant Conversations Dataset (OASST1) · recommended 1×
ShareGPT · recommended 1×
FLAN (Fine-tuned LAnguage Net) · 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. Databricks Dolly 2.0
3. OpenAssistant Conversations Dataset (OASST1)
4. ShareGPT
5. FLAN (Fine-tuned LAnguage Net)
6. LIMA (Less Is More for Alignment)
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 building chat-based AI assistants?
you: not recommended
AI recommended (in order):
1. LLaVA Dataset (haotian-liu/LLaVA)
2. ShareGPT4V (OpenGVLab/ShareGPT4V)
3. MiniGPT-4 Dataset (Vision-CAIR/MiniGPT-4)
4. VQAv2
5. OK-VQA
6. Flickr30k Entities
7. ReferItGame
AI recommended 7 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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MARKDOWN (README)

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yaodongC/awesome-instruction-dataset — 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