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 agentmods add skills/engineerwithai/engineerwith-agents/postgresqlnpx skills add EngineerWithAI/engineerwith-agents --skill postgresqlgit clone --depth 1 https://github.com/EngineerWithAI/engineerwith-agentsWrote 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/engineerwithai/engineerwith-agents/postgresql)<a href="https://agentmods.dev/skills/engineerwithai/engineerwith-agents/postgresql"><img src="https://agentmods.dev/badge/skills/engineerwithai/engineerwith-agents/postgresql.svg" alt="Measured on agentmods" 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 | $0.00031 | $0.04000 |
| Opus 5 | $0.00015 | $0.02000 |
| Sonnet 5 | $0.00006 | $0.00800 |
| Haiku 4.5 | $0.00003 | $0.00400 |
Grade A, and why
postgresql-table-design 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.
This is a copy
100% identical to postgresql-table-design — 243 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PostgreSQL Table Design
Core Rules
- Define a PRIMARY KEY for reference tables (users, orders, etc.). Not always needed for time-series/event/log data. When used, prefer
BIGINT GENERATED ALWAYS AS IDENTITY; useUUIDonly when global uniqueness/opacity is needed. - Normalize first (to 3NF) to eliminate data redundancy and update anomalies; denormalize only for measured, high-ROI reads where join performance is proven problematic. Premature denormalization creates maintenance burden.
- Add NOT NULL everywhere it’s semantically required; use DEFAULTs for common values.
- Create indexes for access paths you actually query: PK/unique (auto), FK columns (manual!), frequent filters/sorts, and join keys.
- Prefer TIMESTAMPTZ for event time; NUMERIC for money; TEXT for strings; BIGINT for integer values, DOUBLE PRECISION for floats (or
NUMERICfor exact decimal arithmetic).
PostgreSQL “Gotchas”
- Identifiers: unquoted → lowercased. Avoid quoted/mixed-case names. Convention: use
snake_casefor table/column names. - Unique + NULLs: UNIQUE allows multiple NULLs. Use
UNIQUE (...) NULLS NOT DISTINCT(PG15+) to restrict to one NULL. - FK indexes: PostgreSQL does not auto-index FK columns. Add them.
- No silent coercions: length/precision overflows error out (no truncation). Example: inserting 999 into
NUMERIC(2,0)fails with error, unlike some databases that silently truncate or round. - Sequences/identity have gaps (normal; don't "fix"). Rollbacks, crashes, and concurrent transactions create gaps in ID sequences (1, 2, 5, 6...). This is expected behavior—don't try to make IDs consecutive.
- Heap storage: no clustered PK by default (unlike SQL Server/MySQL InnoDB);
CLUSTERis one-off reorganization, not maintained on subsequent inserts. Row order on disk is insertion order unless explicitly clustered. - MVCC: updates/deletes leave dead tuples; vacuum handles them—design to avoid hot wide-row churn.
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.
- yesterday First seen · 205 lines · 31 tokens per session scan A 05006a2c3f1f
postgresql-table-design is a skill published in the GitHub repository EngineerWithAI/engineerwith-agents (4 stars, last pushed 7mo ago), licensed MIT. It adds 31 tokens to every session and 4,000 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to postgresql-table-design, differing in 243 lines, and is treated as a copy.
Other skills, from other repositories
alloydb-basics
Manages clusters, instances, and backups for AlloyDB for PostgreSQL, and integrates with AlloyDB Model Context Protocol (MCP) tools for automated database operations. Use when creating, configuring, or administering AlloyDB databases. Do NOT use for general PostgreSQL instances (e.g. Cloud SQL) or other GCP databases.
db-repair
Auto-fix gbrain's Postgres access so the brain stays available. When any gbrain command or MCP tool result carries a GBRAINDBACCESS marker (or an operator reports the brain database is down), run the hardcoded gbrain db-repair ladder: diagnose, apply the safe tier, verify. The action is ALWAYS the hardcoded command …
claimable-postgres
Provision instant temporary Postgres databases via Claimable Postgres by Neon (neon.new) with no login, signup, or credit card. Supports REST API, CLI, and SDK. Use when users ask for a quick Postgres environment, a throwaway DATABASEURL for prototyping/tests, or "just give me a DB now". Triggers include: "quick…
analyzing-insights-across-teams
Analyze PostHog insights, dashboards, or teams beyond the current project by querying the prod Postgres replicas synced into the dogfood data warehouse (US project 2, "PostHog App + Website"). Use when asked to analyze insights across all teams or projects, another team's insights, or fleet-wide insight/dashboard…
dsql
Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, diagnose cluster performance, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, foreign key…
nw-database-technology-selection
Database comparison catalogs, RDBMS vs NoSQL selection criteria, CAP/ACID/BASE theory, OLTP vs OLAP, and technology-specific characteristics.