Supabase DB

SKILL.md

---
name: supabase
version: "1.1.0"
description: Connect to Supabase for database operations, vector search, and storage. SQL queries, CRUD, pgvector similarity search, and table management. Note: Supabase is migrating to new project-scoped API keys starting March 11, 2026.
author: mvanhorn
license: MIT
repository: https://github.com/mvanhorn/clawdbot-skill-supabase
homepage: https://supabase.com
metadata:
  openclaw:
    emoji: "🟩"
    requires:
      env:
        - SUPABASE_URL
        - SUPABASE_SERVICE_KEY
    optionalEnv:
      - SUPABASE_API_KEY
      - SUPABASE_ACCESS_TOKEN
    primaryEnv: SUPABASE_URL
    tags:
      - database
      - postgres
      - vector-search
      - pgvector
      - supabase
---

# Supabase CLI

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

> **API Key Migration (March 2026):** Supabase is deprecating legacy service keys starting March 11, 2026.
> Get your new project-scoped key: Dashboard → Settings → API → API Keys.
> Set `SUPABASE_API_KEY` going forward. Legacy `SUPABASE_SERVICE_KEY` still works until late 2026.

## Setup

```bash
# Required
export SUPABASE_URL="https://yourproject.supabase.co"
export SUPABASE_SERVICE_KEY="eyJhbGciOiJIUzI1NiIs..."  # legacy — use SUPABASE_API_KEY for new projects

# New project-scoped key (preferred, March 2026+)
export SUPABASE_API_KEY="sbp_..."

# 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 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

```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 |
| 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)