supabaseConnect to Supabase for database operations, vector search, and storage. Use for storing data, running SQL queries, similarity search with pgvector, and managing tables. Triggers on requests involving databases, vector stores, embeddings, or Supabase specifically.
Install via ClawdBot CLI:
clawdbot install stopmoclay/supabaseGrade Good — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Accesses sensitive credential files or environment variables
${OPENAICalls external URL not in known-safe list
https://yourproject.supabase.coUses known external API (expected, informational)
api.openai.comAI Analysis
The skill's external API usage (Supabase) is consistent with its stated purpose and requires explicit user configuration of credentials. No hidden instructions, credential harvesting, or obfuscation are present. The primary risk is the inherent capability to execute arbitrary SQL queries, which is expected for a database tool but could be misused if the skill itself were compromised.
Generated Mar 1, 2026
Use Supabase to store product data and user interactions, then implement vector search for personalized recommendations based on embedding similarities. This enables real-time suggestions by querying product embeddings against user preferences, improving engagement and sales.
Store articles, documents, or media metadata in Supabase tables and use pgvector for semantic search to find relevant content based on meaning rather than keywords. This allows users to quickly locate information through natural language queries, enhancing content discovery.
Log customer support tickets in Supabase and apply vector search to identify similar past issues, speeding up resolution times. By embedding ticket descriptions, agents can retrieve related solutions and reduce response latency.
Leverage Supabase for CRUD operations on business data like sales or user metrics, then run SQL queries to generate insights. This supports dynamic dashboards that update automatically as data changes, aiding decision-making.
Use Supabase to store and query time-series data from IoT devices, with vector search for anomaly detection by comparing embeddings of normal vs. abnormal patterns. This helps monitor device health and trigger alerts efficiently.
Offer a subscription-based service using Supabase for backend data storage and vector search capabilities, allowing clients to build custom applications without managing infrastructure. Revenue comes from monthly or annual fees based on usage tiers.
Provide expertise in setting up and optimizing Supabase for clients, including database design, vector search implementation, and performance tuning. Revenue is generated through project-based contracts or hourly consulting rates.
Develop a proprietary analytics tool that uses Supabase for data aggregation and vector search to deliver insights, sold as a one-time purchase or with ongoing support. Revenue streams include licensing fees and premium feature add-ons.
💬 Integration Tip
Ensure environment variables are securely configured and test vector search functions with sample data before deployment to avoid performance issues.
Scored Apr 16, 2026
Audited Apr 16, 2026 · audit v1.0
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