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.
npx skills add justanesta/claude-code-resources --skill sql-integration-patternsgit clone --depth 1 https://github.com/justanesta/claude-code-resourcesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/justanesta/claude-code-resources/sql-integration-patterns)<a href="https://agentmods.dev/skills/justanesta/claude-code-resources/sql-integration-patterns"><img src="https://agentmods.dev/badge/skills/justanesta/claude-code-resources/sql-integration-patterns/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/justanesta/claude-code-resources/sql-integration-patterns"><img src="https://agentmods.dev/badge/skills/justanesta/claude-code-resources/sql-integration-patterns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00035 | $0.02304 |
| Opus 5.5 | $0.00014 | $0.00922 |
| Sonnet 5.5 | $0.00007 | $0.00461 |
| Haiku 4.5 | $0.00003 | $0.00230 |
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 2mo ago.
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.
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
- Always use parameterized queries — Never interpolate user input into SQL strings. Use bind parameters to prevent SQL injection and improve query plan caching.
- Manage connections explicitly — Open connections late, close them early. Use context managers or connection pools to prevent resource leaks.
- 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.
- 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.
- 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.
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.
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.
- 2mo ago First seen · 230 lines · 35 tokens per session scan A 97611490dccb
SQL Integration Patterns is a skill published in the GitHub repository justanesta/claude-code-resources (2 stars, last pushed 6mo ago), licensed MIT. It adds 35 tokens to every session and 2,304 once invoked, about $0.0001 per session on Opus 5.5. 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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