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.
git clone --depth 1 https://github.com/d-padmanabhan/agent-engineering-handbookWrote 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/rules/d-padmanabhan/agent-engineering-handbook/475-sql)<a href="https://agentmods.dev/rules/d-padmanabhan/agent-engineering-handbook/475-sql"><img src="https://agentmods.dev/badge/rules/d-padmanabhan/agent-engineering-handbook/475-sql/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/rules/d-padmanabhan/agent-engineering-handbook/475-sql"><img src="https://agentmods.dev/badge/rules/d-padmanabhan/agent-engineering-handbook/475-sql.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 | $0.00033 | $0.01055 |
| Opus 5.5 | $0.00013 | $0.00422 |
| Sonnet 5.5 | $0.00007 | $0.00211 |
| Haiku 4.5 | $0.00003 | $0.00105 |
Grade A, and why
475-sql 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 1mo 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SQL Engineering Ruleset
Audience: analysts and engineers writing or reviewing SQL (any RDBMS) Goal: write SQL that is safe-by-default, reviewable, and operationally predictable
[!IMPORTANT] This rule is intentionally database-agnostic. For PostgreSQL specifics, also follow
470-postgresql.mdc.
[!NOTE] For data-platform specifics, also see
480-data-engineering.mdcand the platform rules (481-databricks.mdc,482-snowflake.mdc,483-kafka.mdc,484-teradata.mdc).
SQL command categories (mental model)
Use these categories to quickly understand risk, required privileges, and rollback strategy.
DQL - Data Query Language (read-only queries)
- Primary command:
SELECT - Common clauses:
FROM,WHERE,GROUP BY,HAVING,ORDER BY,LIMIT,OFFSET
DML - Data Manipulation Language (changes table contents)
INSERT,UPDATE,DELETE,MERGE(where supported)
[!CAUTION] DML is where “I destroyed production data” incidents happen. Always prove the rowset first and use transactions for risky changes.
DDL - Data Definition Language (changes schema/objects)
CREATE,ALTER,DROP,TRUNCATE,RENAME
[!WARNING] DDL can take locks, block traffic, and be irreversible (especially
DROPandTRUNCATE). Treat DDL as code: migration-reviewed, tested, and rolled out safely.
DCL - Data Control Language (permissions)
GRANT,REVOKE(and role/user management depending on DB)
TCL - Transaction Control Language (atomicity and recovery)
BEGIN/START TRANSACTION,COMMIT,ROLLBACK,SAVEPOINT
Destructive-operation guardrails (must follow)
DELETE / UPDATE safety
- Never run
DELETEorUPDATEwithout aWHERE. - Before destructive writes:
- run the exact
SELECTfirst to validate the target rows - record expected row count and a small sample of primary keys
- run the exact
- Prefer patterns that make review safer:
... WHERE ... RETURNING ...(when supported)- limit scope by immutable IDs, not names
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.
- 1mo ago First seen · 134 lines · 33 tokens per session scan A 2ef50fb8f429
475-sql is a cursor rule published in the GitHub repository d-padmanabhan/agent-engineering-handbook (19 stars, last pushed 2d ago), licensed MIT. It adds 33 tokens to every session and 1,055 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 2026-08-30.
Other cursor rules, from other repositories
as-contract-cast-smell
// ❌ WRONG — bypasses the family ContractSerializer seam const contract = JSON.parse(raw) as Contract; const contract = JSON.parse(raw) as Contract .
no-backward-compatibility
Do not add backward-compatibility shims or migration scaffolding.
postgresql
This guide defines the definitive best practices for writing clean, performant, and maintainable PostgreSQL SQL, focusing on modern conventions and avoiding common pitfalls.
ehs-ims-conventions
EHS IMS app — RBAC, data layer, tRPC, migrations, AI boundaries.
sync-timedb-archive-janitor-contract
Day-close / cold-path worker contracts for synctimedb (A invariants; B tick coordinator retired).
schema
Scalable and secure schema design patterns for relational databases, NoSQL, and APIs. / TR: İlişkisel veri tabanları, NoSQL ve API'ler için ölçeklenebilir ve güvenli şema tasarım kalıpları.