REPOGEO REPORT · LITE

meta-llama/synthetic-data-kit

Default branch main · commit 27a5541b · scanned 6/27/2026, 4:23:31 PM

GitHub: 1,606 stars · 224 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

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 meta-llama/synthetic-data-kit, 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 to emphasize LLM-powered generation for fine-tuning

Why:

CURRENT

Tool for generating high-quality synthetic datasets to fine-tune LLMs. Generate Reasoning Traces, QA Pairs, save them to a fine-tuning format with a simple CLI.

COPY-PASTE FIX

A powerful CLI tool for generating high-quality synthetic datasets *using LLMs as generators and judges*, specifically designed to create reasoning traces, QA pairs, and other fine-tuning formats for LLMs like Llama-3.

mediumtopics#2

Add more specific topics to improve categorization

Why:

CURRENT

data, generation, llm, python, synthetic

COPY-PASTE FIX

data, generation, llm, python, synthetic, fine-tuning, instruction-tuning, reasoning-traces, qa-generation, dataset-curation

lowreadme#3

Create a dedicated "Guides and Examples" section in the README

Why:

CURRENT

> Checkout our guide on using the tool to unlock task-specific reasoning in Llama-3 family

COPY-PASTE FIX

## Guides and Examples

- **Unlocking Task-Specific Reasoning in Llama-3:** Explore our guide on using Synthetic Data Kit to generate data that enhances Llama-3's reasoning capabilities and unlocks task-specific performance.

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 meta-llama/synthetic-data-kit

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenAI API

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

OpenAI API · recommended 2×
Anthropic Claude · recommended 1×
huggingface/transformers · recommended 1×
Snorkel AI · recommended 1×
RasaHQ/rasa · recommended 1×

CATEGORY QUERY
How to generate high-quality synthetic datasets for fine-tuning large language models?
you: not recommended
AI recommended (in order):
1. OpenAI API
2. Anthropic Claude
3. Hugging Face Transformers (huggingface/transformers)
4. Snorkel AI
5. Rasa (RasaHQ/rasa)
6. SynthAI (SynthAI-dev/SynthAI)
7. NLPAug (makcedward/nlpaug)
8. TextAttack (TextAttack/TextAttack)
AI recommended 8 alternatives but never named meta-llama/synthetic-data-kit. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the best Python tools for creating synthetic data to fine-tune LLMs?
you: not recommended
AI recommended (in order):
1. Hugging Face `datasets` library
2. OpenAI API
3. LangChain
4. `Faker`
5. `snorkel`
6. `guidance`
7. `synthcity`
AI recommended 7 alternatives but never named meta-llama/synthetic-data-kit. 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 meta-llama/synthetic-data-kit?
pass
AI named meta-llama/synthetic-data-kit explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts meta-llama/synthetic-data-kit in production, what risks or prerequisites should they evaluate first?
pass
AI named meta-llama/synthetic-data-kit 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 meta-llama/synthetic-data-kit solve, and who is the primary audience?
pass
AI named meta-llama/synthetic-data-kit explicitly
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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meta-llama/synthetic-data-kit — 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