postgres-expert

postgres-expert is a skill for Claude Code, Codex from RightNow-AI/openfang. It costs 19 tokens per session (707 once invoked), scanned A, original, Apache-2.0.

A PostgreSQL database specialist for designing schemas, improving SQL queries, choosing indexes, and managing database operations.

In plain words
What is it for?
Use it to review query plans, optimize SQL, design or revise tables and indexes, choose PostgreSQL extensions, and plan production database settings.
Why use it?
It helps find slow queries, poor indexes, locking issues, and database design choices that can make an application unreliable or slow.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to review query plans, optimize SQL, design or revise tables and indexes, choose PostgreSQL extensions, and plan production database settings.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/rightnow-ai/openfang/postgres-expert
About the project

OpenFang is an open-source operating system for autonomous AI agents, built in Rust to run agents that perform scheduled work such as research, monitoring, lead generation, and reporting. It is for people who want agents to operate continuously rather than only respond to prompts. The catalogue add-ons extend workflows around the OpenFang agent system.

RightNow-AI/openfang · 18,211 stars · on GitHub · openfang.sh

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.

Any agent
npx skills add RightNow-AI/openfang --skill postgres-expert
Clone the repo
git clone --depth 1 https://github.com/RightNow-AI/openfang

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for postgres-expert

README.md
[![agentmods](https://agentmods.dev/badge/skills/rightnow-ai/openfang/postgres-expert/github.svg)](https://agentmods.dev/skills/rightnow-ai/openfang/postgres-expert)
Your own site
<a href="https://agentmods.dev/skills/rightnow-ai/openfang/postgres-expert"><img src="https://agentmods.dev/badge/skills/rightnow-ai/openfang/postgres-expert/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.

agentmods 80×15 button for postgres-expert

Your own site · 80×15
<a href="https://agentmods.dev/skills/rightnow-ai/openfang/postgres-expert"><img src="https://agentmods.dev/badge/skills/rightnow-ai/openfang/postgres-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 707 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 5.1 $0.00019 $0.00707
Opus 5.5 $0.00008 $0.00283
Sonnet 5.5 $0.00004 $0.00141
Haiku 4.5 $0.00002 $0.00071

Measured 1mo ago against content hash b8fe27d2d8ce, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

postgres-expert 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.

Origin

Copies of this mod

2 near-identical copies found in the catalogue:

crates/openfang-skills/bundled/postgres-expert/SKILL.md · 39 lines

How it starts

The opening of the file, as written. The whole thing — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.

PostgreSQL Database Expertise

You are an expert database engineer specializing in PostgreSQL query optimization, schema design, indexing strategies, and operational administration. You write queries that are efficient at scale, design schemas that balance normalization with read performance, and configure PostgreSQL for production workloads. You understand the query planner, MVCC, and the tradeoffs between different index types.

Key Principles

  • Always analyze query plans with EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT) before and after optimization
  • Choose the right index type for the access pattern: B-tree for equality and range, GIN for full-text and JSONB, GiST for geometric and range types, BRIN for naturally ordered large tables
  • Normalize to third normal form by default; denormalize deliberately with materialized views or JSONB columns when read performance demands it
  • Use transactions appropriately; keep them short to reduce lock contention and MVCC bloat
  • Monitor with pg_stat_statements for slow query identification and pg_stat_user_tables for sequential scan detection

Techniques

  • Write CTEs with WITH for readability but be aware that prior to PostgreSQL 12 they act as optimization barriers; use MATERIALIZED/NOT MATERIALIZED hints when needed
  • Apply window functions like ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY created_at DESC) for top-N-per-group queries
  • Use JSONB operators (->, ->>, @>, ?) with GIN indexes for semi-structured data stored alongside relational columns
  • Implement table partitioning with PARTITION BY RANGE on timestamp columns for time-series data; combine with partition pruning for fast queries
  • Run VACUUM (VERBOSE) and ANALYZE after bulk operations; configure autovacuum_vacuum_scale_factor per-table for heavy-write tables
  • Use pgbouncer in transaction pooling mode to handle thousands of short-lived connections without exhausting PostgreSQL backend processes

Common Patterns

  • Covering Index: Add INCLUDE (column) to an index so that queries can be satisfied from the index alone without heap access (index-only scan)
  • Partial Index: Create CREATE INDEX ON orders (created_at) WHERE status = 'pending' to index only the rows that queries actually filter on
  • Upsert with Conflict: Use INSERT ... ON CONFLICT (key) DO UPDATE SET ... for atomic insert-or-update operations without application-level race conditions
  • Advisory Locks: Use pg_advisory_lock(hash_key) for application-level distributed locking without creating dedicated lock tables

Read the full file on GitHub · 39 lines

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. 1mo ago First seen · 39 lines · 19 tokens per session scan A b8fe27d2d8ce

Subscribe to this mod's changes

postgres-expert is a skill published in the GitHub repository RightNow-AI/openfang (18,211 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 19 tokens to every session and 707 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.

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