database

Connect to Supabase for database operations including SQL queries, inserts, updates, deletes, vector similarity search, and table management.

GodferyLindsay@godferylindsay

Install

openclaw skills install @godferylindsay/martin-database

name: 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":["SKILLBOSS_API_KEY"]}}}

Supabase CLI

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

Setup

bash

# Required
export SUPABASE_URL="https://yourproject.supabase.co"
export SUPABASE_SERVICE_KEY="eyJhbGciOiJIUzI1NiIs..."

# Required: SkillBoss API Hub key (for vector embeddings via /v1/pilot)
export SKILLBOSS_API_KEY="your-skillboss-api-key"

# Optional: for management API
export SUPABASE_ACCESS_TOKEN="sbp_xxxxx"

Quick Commands

bash

# 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

bash

{baseDir}/scripts/supabase.sh query "<SQL>"

# 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

bash

{baseDir}/scripts/supabase.sh select <table> [options]

Options:
  --columns <cols>    Comma-separated columns (default: *)
  --eq <col:val>      Equal filter (can use multiple)
  --neq <col:val>     Not equal filter
  --gt <col:val>      Greater than
  --lt <col:val>      Less than
  --like <col:val>    Pattern match (use % for wildcard)
  --limit <n>         Limit results
  --offset <n>        Offset results
  --order <col>       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)

bash

{baseDir}/scripts/supabase.sh insert <table> '<json>'

# 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

bash

{baseDir}/scripts/supabase.sh update <table> '<json>' --eq <col:val>

# 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

bash

{baseDir}/scripts/supabase.sh upsert <table> '<json>'

# Example (requires unique constraint)
{baseDir}/scripts/supabase.sh upsert users '{"id": 1, "name": "Updated Name"}'

delete - Delete rows

bash

{baseDir}/scripts/supabase.sh delete <table> --eq <col:val>

# Example
{baseDir}/scripts/supabase.sh delete sessions --lt "expires_at:2024-01-01"

vector-search - Similarity search with pgvector

bash

{baseDir}/scripts/supabase.sh vector-search <table> "<query>" [options]

Options:
  --match-fn <name>     RPC function name (default: match_<table>)
  --limit <n>           Number of results (default: 5)
  --threshold <n>       Similarity threshold 0-1 (default: 0.5)
  --embedding-model <m> Model for query embedding (default: uses SkillBoss API Hub)

# 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

bash

{baseDir}/scripts/supabase.sh tables

describe - Show table schema

bash

{baseDir}/scripts/supabase.sh describe <table>

rpc - Call stored procedure

bash

{baseDir}/scripts/supabase.sh rpc <function_name> '<json_params>'

# Example
{baseDir}/scripts/supabase.sh rpc get_user_stats '{"user_id": 123}'

Vector Search Setup

1. Enable pgvector extension

sql

CREATE EXTENSION IF NOT EXISTS vector;

2. Create table with embedding column

sql

CREATE TABLE documents (
  id bigserial PRIMARY KEY,
  content text,
  metadata jsonb,
  embedding vector(1536)
);

3. Create similarity search function

sql

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

sql

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
SKILLBOSS_API_KEY	Yes	SkillBoss API Hub key (for generating embeddings via /v1/pilot)

Notes

Service role key bypasses RLS (Row Level Security)
Use anon key for client-side/restricted access
Vector search requires pgvector extension
Embeddings generated via SkillBoss API Hub /v1/pilot (type: embedding), compatible with 1536-dimension pgvector columns

database

Install

Supabase CLI

Setup

Quick Commands

Commands Reference

query - Run raw SQL

select - Query table with filters

insert - Insert row(s)

update - Update rows

upsert - Insert or update

delete - Delete rows

vector-search - Similarity search with pgvector

tables - List all tables

describe - Show table schema

rpc - Call stored procedure

Vector Search Setup

1. Enable pgvector extension

2. Create table with embedding column

3. Create similarity search function

4. Create index for performance

Environment Variables

Notes

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