Tencent SkillHub · Developer Tools

Supabase

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.

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Install for OpenClaw

Quick setup

Download the package from Yavira.
Extract the archive and review SKILL.md first.
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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: SKILL.md, scripts/supabase.sh

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Trust & source

Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.0

Provenance

Publisher: lucassynnott
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 19 sections Open source page

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

query - Run raw SQL

{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

{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)

{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

{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

{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

{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

{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

{baseDir}/scripts/supabase.sh tables

describe - Show table schema

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

rpc - Call stored procedure

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

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

VariableRequiredDescriptionSUPABASE_URLYesProject URL (https://xxx.supabase.co)SUPABASE_SERVICE_KEYYesService role key (full access)SUPABASE_ANON_KEYNoAnon key (restricted access)SUPABASE_ACCESS_TOKENNoManagement API tokenOPENAI_API_KEYNoFor 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)

Category context

Code helpers, APIs, CLIs, browser automation, testing, and developer operations.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

1 Docs1 Scripts

SKILL.md Primary doc
scripts/supabase.sh Scripts