Find Codebase Duplications Using Connectors.
Your codebase has 4 different implementations of date formatting, 3 versions of the retry logic, and 2 competing validation libraries , but nobody knows because grep only finds exact matches and these duplicates are semantic
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your AI agent queries Weaviate for vector embeddings of your codebase , functions, classes, and modules that have been indexed as vectors.
It runs similarity searches to find code blocks with high semantic similarity but different text: 'formatDate() in utils/dates.js and renderTimestamp() in components/Timeline.tsx are 94% semantically similar , both convert ISO 8601 to locale string with timezone adjustment.
Different function names, different files, same logic.' The agent reads both files from GitHub to verify the duplication and assess which implementation is better documented, tested, and maintained.
Then it creates a Linear ticket: 'Refactor: consolidate date formatting. 4 implementations found across 3 repositories. Recommended canonical: utils/dates.ts (has tests, handles edge cases).
Remove: Timeline.tsx inline version, billing/format.js, api/helpers/time.js. Estimated effort: 2 story points. Risk: low (all 4 produce identical output).' Code duplication that lives for years because nobody can search for concepts , only exact text matches , gets surfaced and resolved.
Connector Orchestration: 3 Connectors, one intelligent agent
Connect Weaviate, GitHub and Linear Connectors so your AI agent uses vector search on your Weaviate instance to find semantically similar code blocks across your repositories, identifies conceptual duplication that text search cannot find, and creates refactoring tickets in Linear with the duplicated code pairs and consolidation recommendations. Engineering teams with codebases over 100K lines where grep finds nothing but the same logic exists in 5 places with different variable names , and every bug fix needs to be applied in all 5 places without anyone knowing where they all are , get a semantic X-ray that finds conceptual debt invisible to traditional search.
Weaviate
triggerRuns vector similarity search to find semantically similar code blocks across the codebase
search_near_vector list_objects get_object_details get_class_schema Github
enrichmentReads the actual source files to verify duplication and identify code ownership
get_file_contents search_github_code get_repository_details list_pull_requests Linear
actionCreates prioritized refactoring tickets with duplication pairs, impact analysis and consolidation plan
create_issue list_issues list_teams list_labels Run This Automation Today
Connect Claude, ChatGPT, Cursor, or any AI agent to the Vinkius catalog and run this automation in minutes.
Build Your Own Connector
Convert any internal API into a Connector. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Connect & Automate
The 3 servers this recipe uses are ready in the catalog. Connect them once, paste a prompt, and your AI runs the full workflow.
- Weaviate, Github & Linear ready in the catalog right now
- Add more from 5,800+ servers whenever you need
- Connections are secured and compliant by default
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers and recipes added weekly
Superpowers you didn't know your AI had
The Vinkius catalog gives your agent access to 5,800+ Connectors and the intelligence to combine them. Imagine never logging into another dashboard. Your AI handles the work across all tools, in one conversation. That's what this connectivity layer was built for.
Cross-Platform Intelligence
Your agent doesn't just connect to tools. It understands the relationships between them. Data flows where it needs to go, automatically, with full context preserved across all platforms.
Contextual Reasoning
Each decision your agent makes considers the full picture. It reads CRM data, checks calendars, reviews conversation history, and acts on everything at once. Not step by step. All at once.
Productivity at Scale
What used to take 45 minutes across five different dashboards now takes one sentence. Your agent runs the entire workflow end to end while you focus on decisions that actually matter.
Zero-Config Reliability
No API keys to paste. No webhooks to configure. No YAML to debug. Connect your Connectors once, and your agent handles the rest. Each time, without intervention.
Made for
exactly this
Your AI agent taps into the entire Vinkius AI Connectors to handle these for you. You describe what you need. It does the rest.
Engineering teams with large codebases who want to find semantic code duplication invisible to grep and IDE search
Platform teams establishing shared libraries who need to identify consolidation candidates across microservices
Tech leads conducting codebase health audits who want quantified duplication metrics with refactoring recommendations
Teams migrating from monolith to microservices who need to identify code that should be extracted into shared packages
Frequently Asked Questions About This Connector Orchestration
Which Connectors do I need for this workflow?
Three: Weaviate, GitHub and Linear. Connect all three to your AI client. Your codebase must be indexed as vector embeddings in Weaviate , use a code embedding model like CodeBERT or similar.
Does this work with Claude Desktop, Cursor or Windsurf?
Yes. Any AI client that supports the Model Context Protocol works , Claude Desktop, Cursor, Windsurf, Cline and others. Connect the Connectors and paste a prompt.
How do I index my codebase in Weaviate?
Parse your code into functions and classes, generate embeddings using a code-specific model, and store them in Weaviate with metadata (file path, function name, language). The agent searches these embeddings for similarity.
Is my code secure?
Connectors authenticate through API keys. Weaviate and GitHub data stays in your infrastructure. Linear tickets contain references, not full source code. Vinkius does not access your code.
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Connectors used in this workflow
Weaviate
Weaviate MCP lets you manage your vector database and perform semantic searches directly through your AI client. It connects your Weaviate instance to your agent so you can query data by meaning rather than just keywords. It handles everything from checking cluster health to pulling specific object details or inspecting your full schema.
GitHub
GitHub MCP lets you manage your entire software development lifecycle through a chat interface. You can check the status of a pull request, list open issues, or search for specific code snippets without ever leaving your primary workspace. It gives your AI agent direct access to your repositories, making it easier to audit codebases or update project statuses on the fly.
Linear
Linear MCP lets your AI agent manage your project boards, sprints, and issue tracking without you having to switch tabs. It handles everything from creating tickets and assigning priorities to checking cycle progress and querying team data.