REPOGEO REPORT · LITE

litmuschaos/litmus

Default branch master · commit 66939b11 · scanned 5/26/2026, 6:46:20 PM

GitHub: 5,412 stars · 858 forks

AI VISIBILITY SCORE

75 /100

Needs work

Category recall

2 / 2

Avg rank #2.5 when recommended

Rule findings

2 pass · 0 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 litmuschaos/litmus, 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
Strengthen README's opening statement to highlight unique platform features
Why:
CURRENT
```
### Open Source Chaos Engineering Platform
```
COPY-PASTE FIX
```
### The Kubernetes-Native Chaos Engineering Platform with a Community-Driven Chaos Hub
```

mediumabout#2

Update repository description to emphasize workflow-driven approach and Chaos Hub

Why:

CURRENT

Litmus helps SREs and developers practice chaos engineering in a Cloud-native way. Chaos experiments are published at the ChaosHub (https://hub.litmuschaos.io). Community notes is at https://hackmd.io/a4Zu_sH4TZGeih-xCimi3Q

COPY-PASTE FIX

Litmus is a Kubernetes-native, workflow-driven chaos engineering platform for SREs and developers. It enables cloud-native chaos practices with a community-driven ChaosHub (https://hub.litmuschaos.io) for shared experiments.

lowtopics#3

Add more specific topics related to workflow and orchestration

Why:

CURRENT

chaos-engineering, chaos-experiments, chaos-testing, chaoshub, cloud-native, cncf, devops, fault-injection, fault-simulation, golang, google-summer-of-code, hacktoberfest, k8s, kubernetes, lfx, litmuschaos, operator-sdk, reliability-engineering, resilience-testing, site-reliability-engineering

COPY-PASTE FIX

chaos-engineering, chaos-experiments, chaos-testing, chaoshub, cloud-native, cncf, devops, fault-injection, fault-simulation, golang, google-summer-of-code, hacktoberfest, k8s, kubernetes, lfx, litmuschaos, operator-sdk, reliability-engineering, resilience-testing, site-reliability-engineering, chaos-orchestration, workflow-automation

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

2 / 2

100% of queries surface litmuschaos/litmus

Avg rank

#2.5

Lower is better. #1 = top recommendation.

Share of voice

15%

Of all named tools, what % are you?

Top rival

Gremlin

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Gremlin · recommended 2×
chaos-mesh/chaos-mesh · recommended 2×
cloud-bulldozer/kube-burner · recommended 2×
shopify/toxiproxy · recommended 1×
alexei-sidorov/pumba · recommended 1×

CATEGORY QUERY
How can SREs and developers implement chaos engineering practices for cloud-native applications?
you: #3
AI recommended (in order):
1. Gremlin
2. Chaos Mesh (chaos-mesh/chaos-mesh)
3. LitmusChaos (litmuschaos/litmus) ← you
4. Kube-burner (cloud-bulldozer/kube-burner)
5. Toxiproxy (shopify/toxiproxy)
6. Pumba (alexei-sidorov/pumba)
7. AWS Fault Injection Simulator (FIS)
Show full AI answer
CATEGORY QUERY
What tools are available for injecting faults into Kubernetes clusters to improve system resilience?
you: #2
AI recommended (in order):
1. Chaos Mesh (chaos-mesh/chaos-mesh)
2. LitmusChaos (litmuschaos/litmus) ← you
3. Kube-burner (cloud-bulldozer/kube-burner)
4. Chaos Toolkit (chaostoolkit/chaostoolkit)
5. PowerfulSeal (powerfulseal/powerfulseal)
6. Gremlin
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 litmuschaos/litmus?
pass
AI did not name litmuschaos/litmus — 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 litmuschaos/litmus in production, what risks or prerequisites should they evaluate first?
pass
AI named litmuschaos/litmus 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 litmuschaos/litmus solve, and who is the primary audience?
pass
AI did not name litmuschaos/litmus — 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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HTML

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite