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Postgresql Engineering

Community
peterbamuhigire
postgresql-engineering

Use when designing, implementing, administering, tuning, backing up, restoring, monitoring, or troubleshooting PostgreSQL schemas and production systems; use database-reliability for datastore-independent SLO and recovery policy.

Overview

Publisherpeterbamuhigire
Repositorychwezi-dev-engine
Skill namepostgresql-engineering
Stars
29
Forks
6
Bundled files
11
LicenseMIT
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.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by peterbamuhigire on GitHub. Read the source before you install it.

Installation

Install the Postgresql Engineering 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.
    https://github.com/peterbamuhigire/chwezi-dev-engine/tree/main/skills/backend-databases/postgresql-engineering
  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/peterbamuhigire/chwezi-dev-engine.git /tmp/chwezi-dev-engine
mkdir -p .claude/skills
cp -r /tmp/chwezi-dev-engine/skills/backend-databases/postgresql-engineering .claude/skills/postgresql-engineering
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Postgresql Engineering 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 Postgresql Engineering 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 Postgresql Engineering 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 Engineering

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178.

Use this parent skill as the active PostgreSQL engineering entrypoint. Keep implementation guidance here short; load the relevant reference module only when the task needs that depth.

Use When

  • Designing PostgreSQL-backed features, schemas, indexes, constraints, and access paths.
  • Reviewing SQL correctness, transaction boundaries, isolation assumptions, or extension usage.
  • Implementing functions, triggers, views, materialized views, or server-side routines.
  • Consolidating PostgreSQL-specific engineering advice before pairing with database-design-engineering or database-reliability.

Do Not Use When

  • The task is unrelated to this parent skill or is better handled by a narrower active parent named in the workflow.
  • The request only needs a trivial answer and no reference module needs to be loaded.

Required Inputs

  • Gather the concrete system, repository, environment, constraints, and deliverable before loading references.
  • Identify which absorbed reference file is needed; do not load every migrated reference by default.

Workflow

  1. Start with database-design-engineering for logical model, integrity, tenancy, and migration shape.
  2. Load only the reference file matching the task:
    • references/postgresql-fundamentals.md for baseline PostgreSQL usage and core concepts.
    • references/postgresql-patterns.md for schema and application patterns.
    • references/postgresql-advanced-sql.md for advanced query design.
    • references/postgresql-server-programming.md for functions, triggers, and server-side behaviour.
  3. Administration, performance incidents, tuning, backups, and production operations also route here (the retired postgresql-operations skill is an inactive alias of this skill).
  4. Pair with ai-rag-patterns only when pgvector or AI platform concerns are central.

Quality Standards

  • Preserve data integrity with constraints, types, and transaction design before relying on application checks.
  • Make query plans reviewable: expected indexes, cardinality assumptions, and failure cases must be explicit.
  • Treat migrations as reversible operational changes with lock, runtime, and rollback impact stated.

Anti-Patterns

  • Treating absorbed reference files as active skills or separate routing entrypoints.
  • Loading every migrated child reference instead of the one that matches the task.
  • Producing generic advice without constraints, evidence, or next verification steps.

Outputs

  • PostgreSQL schema, SQL, migration, or review notes with integrity and performance evidence.
  • Reference files loaded and companion skills used.

References

  • Load the retained PostgreSQL operations workflow for backup, restore, vacuum, replication, production tuning, monitoring, and incidents.
  • Load only the references/.md files named in the workflow when their depth is required.

Evidence Produced

CategoryArtifactFormatExample
Data safetyPostgreSQL schema and operations evidence packDDL, query plans, and runbookconstraints, migration, vacuum/replication evidence, restore result, and rollback

Inputs

InputRequiredPurpose
PostgreSQL version and schemayesSelect valid features
Query plans and cardinalitiesyesDesign indexes
Migration and availability constraintsyesPlan safe change

Capability contract

Review SQL and propose DDL by default. Execute migrations, extensions, routines, or data changes only with authorised scope and rollback.

Degraded mode

If EXPLAIN ANALYZE or statistics are unavailable, provide read-only hypotheses and validation SQL; do not claim measured improvement.

Decision rules

ConditionAction
Planner estimate is materially wrongRefresh or improve statistics
Index build may block writesUse a compatible concurrent path
Constraint encodes business truthEnforce it in the database

Domain Anti-Patterns

  • Running EXPLAIN ANALYZE on unsafe writes. Fix: use a rollback-safe test.
  • Adding extensions without trust review. Fix: verify source and privileges.
  • Indexing without workload evidence. Fix: compare read and write cost.
  • Hiding null semantics in application code. Fix: define them in schema.
  • Combining expand and contract in one release. Fix: stage compatibility.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Postgresql Engineering AI skill do?

Use when designing, implementing, administering, tuning, backing up, restoring, monitoring, or troubleshooting PostgreSQL schemas and production systems; use database-reliability for datastore-independent SLO and recovery policy.

Why use Postgresql Engineering on TypingMind?

Because you install it once and use it with any model. Postgresql Engineering 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 Postgresql Engineering in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/peterbamuhigire/chwezi-dev-engine/tree/main/skills/backend-databases/postgresql-engineering. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Postgresql Engineering?

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 Postgresql Engineering?

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

Is the Postgresql Engineering AI skill free?

Yes. It is published on GitHub by peterbamuhigire under the MIT 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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