REPOGEO REPORT · LITE

stevesolun/ctx

Default branch main · commit b2fd4abd · scanned 6/18/2026, 4:21:43 PM

GitHub: 515 stars · 63 forks

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 stevesolun/ctx, 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

Clarify the project's core identity as an LLM recommendation engine in the README's opening

Why:

CURRENT

The README starts with `# ctx — Skill, Agent, MCP & Harness Recommendations` followed by links and then 'ctx watches what you are building...'.

COPY-PASTE FIX

Add the following sentence immediately after the H1: 'ctx is an AI-powered recommendation engine that helps developers integrate relevant skills, agents, and harnesses into their custom LLM applications by leveraging a massive knowledge graph.'

mediumtopics#2

Add more specific topics to reinforce the project's role as an LLM skill discovery and orchestration tool

Why:

CURRENT

agents, ai-agents, anthropic, automation, claude, claude-code, context-management, developer-tools, harness, knowledge-graph, llm, llm-wiki, mcp, micro-skills, obsidian, real-time, recommendation-engine, skill-management, skills, wiki

COPY-PASTE FIX

agents, ai-agents, anthropic, automation, claude, claude-code, context-management, developer-tools, harness, knowledge-graph, llm, llm-wiki, mcp, micro-skills, obsidian, real-time, recommendation-engine, skill-management, skills, wiki, llm-skill-discovery, ai-skill-discovery, llm-orchestration, ai-recommendation-system

lowabout#3

Rephrase the repository description to lead with its core function as a recommendation engine

Why:

CURRENT

Skill, agent, MCP, and harness recommendations for Claude Code/custom LLMs: 102,928-node LLM-wiki graph, 91,464 skills, 467 agents, 10,790 MCPs, 207 harnesses, and capped execution recommendations.

COPY-PASTE FIX

ctx is an AI-powered recommendation engine providing skill, agent, MCP, and harness recommendations for Claude Code/custom LLMs, leveraging a 102,928-node LLM-wiki knowledge graph with 91,464 skills, 467 agents, 10,790 MCPs, and 207 harnesses.

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 stevesolun/ctx

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

LangChain

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

LangChain · recommended 1×
LlamaIndex · recommended 1×
Microsoft Semantic Kernel · recommended 1×
OpenAI Assistants API · recommended 1×
Haystack · recommended 1×

CATEGORY QUERY
How can I find and integrate relevant AI skills and agents for my custom LLM applications?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Microsoft Semantic Kernel
4. OpenAI Assistants API
5. Haystack
6. CrewAI
AI recommended 6 alternatives but never named stevesolun/ctx. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the best tools for leveraging a knowledge graph to manage LLM context and recommendations?
you: not recommended
AI recommended (in order):
1. Neo4j (neo4j/neo4j)
2. Graph Data Science Library (neo4j/graph-data-science)
3. LangChain (langchain-ai/langchain)
4. TypeDB (vaticle/typedb)
5. Amazon Neptune
6. Ontotext GraphDB (Ontotext-AD/graphdb)
7. ArangoDB (arangodb/arangodb)
8. TigerGraph (tigergraph/tigergraph)
AI recommended 8 alternatives but never named stevesolun/ctx. 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 stevesolun/ctx?
pass
AI named stevesolun/ctx explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts stevesolun/ctx in production, what risks or prerequisites should they evaluate first?
pass
AI named stevesolun/ctx 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 stevesolun/ctx solve, and who is the primary audience?
pass
AI named stevesolun/ctx 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 stevesolun/ctx. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

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