REPOGEO REPORT · LITE

mthenw/awesome-layers

Default branch master · commit 7deb0685 · scanned 6/25/2026, 12:13:02 PM

GitHub: 2,260 stars · 185 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

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 mthenw/awesome-layers, 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

Emphasize the 'list' nature of the repo in the README introduction

Why:

CURRENT

# λ AWSome Lambda Layers

**A curated list of awesome AWS Lambda Layers**

COPY-PASTE FIX

# λ AWSome Lambda Layers

**A curated list of awesome AWS Lambda Layers: a comprehensive catalog of pre-built runtimes, utilities, monitoring, and security modules for your serverless applications.**

mediumtopics#2

Add specific layer categories to topics

Why:

CURRENT

awesome, aws, aws-lambda, cloud, serverless, serverless-application-model, serverless-framework, serverless-functions

COPY-PASTE FIX

awesome, aws, aws-lambda, cloud, serverless, serverless-application-model, serverless-framework, serverless-functions, lambda-layers, serverless-monitoring, serverless-security, pre-built-modules, serverless-utilities

lowreadme#3

Add a disambiguation note for 'Lambda Layers'

Why:

COPY-PASTE FIX

Add this sentence after the main title/description in the README: "Note: This list specifically focuses on AWS Lambda Layers, not neural network layers or other types of computational layers."

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 mthenw/awesome-layers

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

AWS Lambda Layers

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

AWS Lambda Layers · recommended 1×
serverless/serverless · recommended 1×
serverless/serverless-layers · recommended 1×
serverless-heaven/serverless-webpack · recommended 1×
lerna/lerna · recommended 1×

CATEGORY QUERY
How to reuse common code and dependencies across many serverless functions?
you: not recommended
AI recommended (in order):
1. AWS Lambda Layers
2. Serverless Framework (serverless/serverless)
3. serverless-layers (serverless/serverless-layers)
4. serverless-webpack (serverless-heaven/serverless-webpack)
5. Lerna (lerna/lerna)
6. Nx (nrwl/nx)
7. Webpack (webpack/webpack)
8. esbuild (evanw/esbuild)
9. AWS Lambda Container Image Support
10. Google Cloud Run
11. Azure Container Apps
12. Docker (docker/docker-ce)
13. Git Submodules
AI recommended 13 alternatives but never named mthenw/awesome-layers. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Where can I find pre-built security and monitoring modules for my serverless applications?
you: not recommended
AI recommended (in order):
1. Datadog
2. New Relic
3. Thundra
4. Lumigo
5. AWS CloudWatch
6. AWS X-Ray
7. AWS Security Hub
8. Azure Monitor
9. Azure Application Insights
10. Azure Security Center
AI recommended 10 alternatives but never named mthenw/awesome-layers. 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 mthenw/awesome-layers?
pass
AI named mthenw/awesome-layers explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts mthenw/awesome-layers in production, what risks or prerequisites should they evaluate first?
pass
AI named mthenw/awesome-layers 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 mthenw/awesome-layers solve, and who is the primary audience?
pass
AI named mthenw/awesome-layers explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of mthenw/awesome-layers. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/mthenw/awesome-layers.svg)](https://repogeo.com/en/r/mthenw/awesome-layers)

HTML

<a href="https://repogeo.com/en/r/mthenw/awesome-layers"><img src="https://repogeo.com/badge/mthenw/awesome-layers.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

mthenw/awesome-layers — 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