{
  "context": "Tests whether the agent uses asyncpg pool with lifecycle management, bulk insert/upsert for catalog import, transactions for stock transfers, server-side cursors for large exports, $1/$2 parameterized queries, and async context managers. The task describes business features without naming these patterns.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "asyncpg pool with lifecycle",
      "description": "Uses 'await asyncpg.create_pool()' with min_size/max_size in a FastAPI lifespan or startup handler, and 'await pool.close()' on shutdown",
      "max_score": 12
    },
    {
      "name": "Bulk upsert for catalog import",
      "description": "Product catalog import uses an efficient bulk pattern (executemany with ON CONFLICT, or copy_records_to_table with a temp table merge) -- NOT individual INSERT/UPDATE in a loop for 500-5000 products",
      "max_score": 14
    },
    {
      "name": "Transaction for stock transfer",
      "description": "Stock transfer wraps the decrease, increase, and movement record in 'async with conn.transaction():' so all three operations succeed or fail atomically",
      "max_score": 14
    },
    {
      "name": "Stock validation before transfer",
      "description": "Transfer checks source warehouse quantity within the transaction (using SELECT FOR UPDATE or equivalent) and raises an error if insufficient -- preventing negative stock",
      "max_score": 8
    },
    {
      "name": "Async context managers for connections",
      "description": "All database operations use 'async with pool.acquire() as conn:' context manager -- no bare acquire() without guaranteed release",
      "max_score": 10
    },
    {
      "name": "$1/$2 parameterized queries",
      "description": "All queries use $1, $2, $N numbered placeholders -- no f-strings, .format(), or string concatenation in SQL",
      "max_score": 12
    },
    {
      "name": "Server-side cursor for warehouse export",
      "description": "Warehouse inventory export uses a cursor or streaming pattern (conn.cursor with async iteration, or batched fetch) for 50,000+ products -- not loading all into memory with fetch()",
      "max_score": 10
    },
    {
      "name": "DATABASE_URL from environment",
      "description": "Connection string comes from environment variable, not hardcoded",
      "max_score": 7
    },
    {
      "name": "Pool timeout configuration",
      "description": "Pool has command_timeout or max_inactive_connection_lifetime configured",
      "max_score": 6
    },
    {
      "name": "No SQL injection vectors",
      "description": "No dynamic SQL built with string interpolation anywhere -- all values passed through parameterized queries",
      "max_score": 7
    }
  ]
}