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Postgres Expert

OrganizationPopular
RightNow-AI
postgres-expert

PostgreSQL expert for query optimization, indexing, extensions, and database administration

Overview

PublisherRightNow-AI
Repositoryopenfang
Skill namepostgres-expert
Stars
18.2K
Forks
2.3K
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by RightNow-AI on GitHub. Read the source before you install it.

Installation

Install the Postgres Expert AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/RightNow-AI/openfang.git /tmp/openfang
mkdir -p .claude/skills
cp -r /tmp/openfang/crates/openfang-skills/bundled/postgres-expert .claude/skills/postgres-expert
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Postgres Expert in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Postgres Expert on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Postgres Expert is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

Pitfalls to Avoid

  • Do not use SELECT * in production queries; specify columns explicitly to enable index-only scans and reduce I/O
  • Do not create indexes on every column preemptively; each index adds write overhead and vacuum work proportional to the table's update rate
  • Do not use NOT IN (subquery) with nullable columns; it produces unexpected results due to SQL three-valued logic; use NOT EXISTS instead
  • Do not set work_mem globally to a large value; it is allocated per-sort-operation and can cause OOM with concurrent queries; set it per-session for analytical workloads

Frequently asked questions

What does the Postgres Expert AI skill do?

PostgreSQL expert for query optimization, indexing, extensions, and database administration

Why use Postgres Expert on TypingMind?

Because you install it once and use it with any model. Postgres Expert is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Postgres Expert in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/RightNow-AI/openfang/tree/main/crates/openfang-skills/bundled/postgres-expert. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Postgres Expert?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Postgres Expert?

As many as you like. As long as a model supports skills, you can use Postgres Expert with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Postgres Expert AI skill free?

Yes. It is published on GitHub by RightNow-AI under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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