SQL Integration Patterns

A set of patterns for connecting Python, R, and dbt programs to databases. It covers database connections, query safety, object-relational mappers, and data transformations.

In plain words
What is it for?
Use it when building applications or data pipelines that read from or write to databases with Python, R, or dbt.
Why use it?
It helps avoid SQL injection, leaked database connections, and tangled code that is hard to test or maintain.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/justanesta/claude-code-resources/sql-integration-patterns
Any agent
npx skills add justanesta/claude-code-resources --skill sql-integration-patterns
Clone the repo
git clone --depth 1 https://github.com/justanesta/claude-code-resources

Made for: Claude Code, Codex.

Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,304 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00035 $0.02304
Opus 5 $0.00017 $0.01152
Sonnet 5 $0.00007 $0.00461
Haiku 4.5 $0.00003 $0.00230

Measured yesterday against content hash 97611490dccb, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

SQL Integration Patterns scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/SQL/sql-integration-patterns/SKILL.md · 230 lines

How it starts

The opening of the file, as written. The whole thing — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.

SQL Integration Patterns

Core Principles

  1. Always use parameterized queries — Never interpolate user input into SQL strings. Use bind parameters to prevent SQL injection and improve query plan caching.
  2. Manage connections explicitly — Open connections late, close them early. Use context managers or connection pools to prevent resource leaks.
  3. Use ORMs for application logic, raw SQL for analytics — ORMs like SQLAlchemy and Django ORM excel at CRUD operations with business rules. Raw SQL is better for complex analytical queries and bulk operations.
  4. Separate transformation logic from connection logic — Keep database credentials, connection setup, and query execution in distinct layers so each can be tested and maintained independently.
  5. Prefer declarative over imperative — Tools like dbt, SQLAlchemy's declarative base, and dbplyr let you express what you want rather than how to get it, reducing bugs and improving readability.

Python Database Connections

Python offers multiple libraries for database access, each suited to different use cases. The core pattern is always the same: establish a connection, execute parameterized queries, and clean up resources.

import psycopg  # psycopg 3
import pandas as pd

# Context manager ensures connection closes even on error
conn_string = "host=warehouse.internal dbname=analytics user=etl_svc"

with psycopg.connect(conn_string) as conn:
    # Parameterized query — %s placeholders, tuple of values
    result = conn.execute(
        "SELECT order_id, total FROM orders WHERE region = %s AND status = %s",
        ("us-west", "shipped"),
    ).fetchall()

    # pandas integration for analytical workflows
    df = pd.read_sql(
        "SELECT date_trunc('month', order_date) AS month, sum(total) AS revenue "
        "FROM orders WHERE region = %(region)s GROUP BY 1",
        conn,
        params={"region": "us-west"},
    )

See python-db-connectors for: psycopg3 async patterns, pyodbc for SQL Server, mysql-connector, bulk inserts with COPY, and error handling strategies.

Read the full file on GitHub · 230 lines

Files

What ships with it

6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 230 lines · 35 tokens per session scan A 97611490dccb

Subscribe to this mod's changes

SQL Integration Patterns is a skill published in the GitHub repository justanesta/claude-code-resources (2 stars, last pushed 4mo ago), licensed MIT. It adds 35 tokens to every session and 2,304 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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