Supabase

---

name: supabase description: Connect 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. metadata: {"clawdbot":{"requires":{"env":["SUPABASE_URL","SUPABASE_SERVICE_KEY"]}}}

---

Supabase CLI

Interact with Supabase projects: queries, CRUD, vector search, and table management.

Setup

# Required
export SUPABASE_URL="https://yourproject.supabase.co"
export SUPABASE_SERVICE_KEY="eyJhbGciOiJIUzI1NiIs..."
Optional: for management API
export SUPABASE_ACCESS_TOKEN="sbp_xxxxx"

Quick Commands

# SQL query
{baseDir}/scripts/supabase.sh query "SELECT * FROM users LIMIT 5"
Insert data
{baseDir}/scripts/supabase.sh insert users '{"name": "John", "email": "john@example.com"}'
Select with filters
{baseDir}/scripts/supabase.sh select users --eq "status:active" --limit 10
Update
{baseDir}/scripts/supabase.sh update users '{"status": "inactive"}' --eq "id:123"
Delete
{baseDir}/scripts/supabase.sh delete users --eq "id:123"
Vector similarity search
{baseDir}/scripts/supabase.sh vector-search documents "search query" --match-fn match_documents --limit 5
List tables
{baseDir}/scripts/supabase.sh tables
Describe table
{baseDir}/scripts/supabase.sh describe users

Commands Reference

query - Run raw SQL

{baseDir}/scripts/supabase.sh query ""
Examples
{baseDir}/scripts/supabase.sh query "SELECT COUNT(*) FROM users"
{baseDir}/scripts/supabase.sh query "CREATE TABLE items (id serial primary key, name text)"
{baseDir}/scripts/supabase.sh query "SELECT * FROM users WHERE created_at > '2024-01-01'"

select - Query table with filters

{baseDir}/scripts/supabase.sh select  [options]
Options:
  --columns     Comma-separated columns (default: *)
  --eq       Equal filter (can use multiple)
  --neq      Not equal filter
  --gt       Greater than
  --lt       Less than
  --like     Pattern match (use % for wildcard)
  --limit          Limit results
  --offset         Offset results
  --order 
Order by column
  --desc              Descending order
Examples
{baseDir}/scripts/supabase.sh select users --eq "status:active" --limit 10
{baseDir}/scripts/supabase.sh select posts --columns "id,title,created_at" --order created_at --desc
{baseDir}/scripts/supabase.sh select products --gt "price:100" --lt "price:500"
insert - Insert row(s)
{baseDir}/scripts/supabase.sh insert 
 ''
Single row
{baseDir}/scripts/supabase.sh insert users '{"name": "Alice", "email": "alice@test.com"}'
Multiple rows
{baseDir}/scripts/supabase.sh insert users '[{"name": "Bob"}, {"name": "Carol"}]'
update - Update rows
{baseDir}/scripts/supabase.sh update 
 '' --eq 
Example
{baseDir}/scripts/supabase.sh update users '{"status": "inactive"}' --eq "id:123"
{baseDir}/scripts/supabase.sh update posts '{"published": true}' --eq "author_id:5"
upsert - Insert or update
{baseDir}/scripts/supabase.sh upsert 
 ''
Example (requires unique constraint)
{baseDir}/scripts/supabase.sh upsert users '{"id": 1, "name": "Updated Name"}'
delete - Delete rows
{baseDir}/scripts/supabase.sh delete 
 --eq 
Example
{baseDir}/scripts/supabase.sh delete sessions --lt "expires_at:2024-01-01"
vector-search - Similarity search with pgvector
{baseDir}/scripts/supabase.sh vector-search 
 "" [options]
Options:
  --match-fn      RPC function name (default: match_)
  --limit            Number of results (default: 5)
  --threshold        Similarity threshold 0-1 (default: 0.5)
  --embedding-model  Model for query embedding (default: uses OpenAI)
Example
{baseDir}/scripts/supabase.sh vector-search documents "How to set up authentication" --limit 10
Requires a match function like:
CREATE FUNCTION match_documents(query_embedding vector(1536), match_threshold float, match_count int)
tables - List all tables
{baseDir}/scripts/supabase.sh tables
describe - Show table schema
{baseDir}/scripts/supabase.sh describe 
rpc - Call stored procedure
{baseDir}/scripts/supabase.sh rpc  ''
Example
{baseDir}/scripts/supabase.sh rpc get_user_stats '{"user_id": 123}'
Vector Search Setup
1. Enable pgvector extension
CREATE EXTENSION IF NOT EXISTS vector;
2. Create table with embedding column
CREATE TABLE documents (
  id bigserial PRIMARY KEY,
  content text,
  metadata jsonb,
  embedding vector(1536)
);
3. Create similarity search function
CREATE OR REPLACE FUNCTION match_documents(
  query_embedding vector(1536),
  match_threshold float DEFAULT 0.5,
  match_count int DEFAULT 5
)
RETURNS TABLE (
  id bigint,
  content text,
  metadata jsonb,
  similarity float
)
LANGUAGE plpgsql
AS $$
BEGIN
  RETURN QUERY
  SELECT
    documents.id,
    documents.content,
    documents.metadata,
    1 - (documents.embedding <=> query_embedding) AS similarity
  FROM documents
  WHERE 1 - (documents.embedding <=> query_embedding) > match_threshold
  ORDER BY documents.embedding <=> query_embedding
  LIMIT match_count;
END;
$$;
4. Create index for performance
CREATE INDEX ON documents 
USING ivfflat (embedding vector_cosine_ops)
WITH (lists = 100);
Environment Variables
| Variable | Required | Description |
|----------|----------|-------------|
| SUPABASE_URL | Yes | Project URL (https://xxx.supabase.co) |
| SUPABASE_SERVICE_KEY | Yes | Service role key (full access) |
| SUPABASE_ANON_KEY | No | Anon key (restricted access) |
| SUPABASE_ACCESS_TOKEN | No | Management API token |
| OPENAI_API_KEY | No | For generating embeddings |
Notes
Service role key bypasses RLS (Row Level Security)
Use anon key for client-side/restricted access
Vector search requires pgvector extension
Embeddings default to OpenAI text-embedding-ada-002 (1536 dimensions)
⚙️ Configuration
# Required
export SUPABASE_URL="https://yourproject.supabase.co"
export SUPABASE_SERVICE_KEY="eyJhbGciOiJIUzI1NiIs..."
Optional: for management API
export SUPABASE_ACCESS_TOKEN="sbp_xxxxx"
📋 Tips & Best Practices
Service role key bypasses RLS (Row Level Security)
Use anon key for client-side/restricted access
Vector search requires pgvector extension
Embeddings default to OpenAI text-embedding-ada-002 (1536 dimensions)
💬