Agent skill

supabase-integration

Unified database operations for BidDeed.AI and Life OS ecosystems. Use when: (1) logging insights, activities, or learning sessions, (2) querying historical data from any table, (3) user says "log this" or "save to database", (4) storing auction results, task states, swim times, or health metrics. Database: mocerqjnksmhcjzxrewo.supabase.co

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/supabase-integration

SKILL.md

Supabase Integration

Overview

Centralized database layer for all Shapira ecosystem data persistence. Executes via GitHub Actions workflows - never local curl.

Connection Details

  • Project URL: https://mocerqjnksmhcjzxrewo.supabase.co
  • Service Role Key: Stored in GitHub secrets as SUPABASE_SERVICE_ROLE_KEY
  • Key IAT: 1764532526

Table Schema

BidDeed.AI Tables

Table Purpose Key Fields
historical_auctions Auction records (1,393+ rows) case_number, judgment, sale_price, outcome
auction_results Active analysis case_number, max_bid, recommendation, liens
daily_metrics Pipeline performance date, properties_analyzed, accuracy

Life OS Tables

Table Purpose Key Fields
tasks Task state machine id, description, state, complexity, domain
activities Activity log (12+ rows) id, category, description, timestamp
learning_sessions YouTube/podcast insights source, key_takeaways, business_application
health_logs Sleep, energy tracking date, sleep_hours, energy_1_10
michael_swim_times Race results event, time, meet, date
michael_nutrition Keto adherence date, day_type, meals
michael_recruiting D1 outreach school, contact, status
task_interventions ADHD intervention log task_id, level, timestamp

Shared Tables

Table Purpose Key Fields
insights Cross-domain knowledge category, content, tags
goals OKRs and targets domain, objective, key_result, progress

Workflow: Insert Data

Trigger: User says "log this", task completion, auction analysis, learning capture

  1. Identify target table from context
  2. Format payload matching schema
  3. Execute via GitHub Actions workflow
bash
# GitHub Actions dispatch (not local curl)
# See: .github/workflows/insert_insight.yml in both repos

Insert Patterns by Domain

BidDeed.AI - Auction Result:

json
{
  "table": "auction_results",
  "data": {
    "case_number": "05-2024-CA-012345",
    "property_address": "123 Main St, Melbourne, FL",
    "judgment_amount": 150000,
    "max_bid": 82500,
    "recommendation": "BID",
    "liens": [{"type": "HOA", "amount": 5000}]
  }
}

Life OS - Task:

json
{
  "table": "tasks",
  "data": {
    "description": "Review Q4 insurance renewals",
    "state": "INITIATED",
    "complexity": 6,
    "clarity": 8,
    "domain": "BUSINESS",
    "estimated_minutes": 45
  }
}

Life OS - Learning Session:

json
{
  "table": "learning_sessions",
  "data": {
    "source": "YouTube: How to 10x Your Real Estate Portfolio",
    "key_takeaways": ["Leverage OPM", "Focus on cash flow"],
    "business_application": "Apply to Everest Capital deal structuring"
  }
}

Workflow: Query Data

Trigger: "show me", "what were", "find", historical lookup

  1. Construct query with filters
  2. Execute via Supabase client
  3. Return formatted results

Query Examples

sql
-- Recent tasks by domain
SELECT * FROM tasks 
WHERE domain = 'BUSINESS' 
AND state != 'COMPLETED'
ORDER BY created_at DESC LIMIT 10;

-- Auction performance
SELECT recommendation, COUNT(*) 
FROM auction_results 
GROUP BY recommendation;

-- Michael's best times by event
SELECT event, MIN(time) as pr 
FROM michael_swim_times 
GROUP BY event;

LangGraph Integration

Input Schema

python
class SupabaseInput(TypedDict):
    operation: str  # "insert" | "query" | "update"
    table: str
    data: dict  # For insert/update
    filters: dict  # For query

Output Schema

python
class SupabaseOutput(TypedDict):
    success: bool
    rows_affected: int
    data: list  # Query results
    error: Optional[str]

Node Implementation

python
def supabase_node(state: dict) -> dict:
    """Execute Supabase operation as LangGraph node."""
    from supabase import create_client
    
    client = create_client(
        "https://mocerqjnksmhcjzxrewo.supabase.co",
        os.environ["SUPABASE_SERVICE_ROLE_KEY"]
    )
    
    if state["operation"] == "insert":
        result = client.table(state["table"]).insert(state["data"]).execute()
    elif state["operation"] == "query":
        query = client.table(state["table"]).select("*")
        for k, v in state["filters"].items():
            query = query.eq(k, v)
        result = query.execute()
    
    return {"success": True, "data": result.data}

Scripts

  • scripts/insert_insight.py - Generic insert with validation
  • scripts/query_builder.py - Dynamic query construction

References

  • references/schema_details.md - Full column definitions and constraints
  • references/migration_history.md - Schema evolution log

Best Practices

  1. Always use GitHub Actions - Never local curl to Supabase
  2. Validate before insert - Check required fields
  3. Use timestamps - All tables have created_at/updated_at
  4. Category consistency - Use defined categories (BUSINESS, MICHAEL, FAMILY, PERSONAL)

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