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Public skill engines

I use AI tools as part of a complete professional workflow

I am Peter Bamuhigire, a technology and business consultant. These public skills engines show how I turn AI tools into repeatable work: frame the problem, route it to the right specialist method, produce with evidence, review the result, and keep a human accountable for the decision.

View all repositories on GitHub ↗How the workflow works

What is an AI skills engine?

An AI skills engine is a structured catalogue of instructions, decision rules, evidence requirements, and quality gates that helps an agent or practitioner route work consistently. I use the engines as the reasoning and control layer around AI tools — not as a magic prompt, an unsupervised replacement for specialists, or a claim that every task can be automated.

Use this directory when you need to

  • • choose the right engine for a project;
  • • inspect the public source before adopting a workflow;
  • • keep a human in the middle of every consequential workflow.
  • • see the method I use across different professional domains.

My AI practice

The tool is only one part of the system

I use an AI tool for the work it is good at — drafting, coding, classifying, comparing, summarising, or exploring — then use the relevant skills engine to decide what must be checked, what evidence is required, which specialist route applies, and where a person must approve the result. Human review is not an optional final polish; it is the control point in the workflow.

See the complete operating method →

Context

The right engine gives the AI tool the job, constraints, inputs, and boundaries.

Evidence

Sources, tests, calculations, and repository facts keep fluent output from becoming false confidence.

Human in the middle

A person reviews, corrects, approves, escalates, rejects, stops, or rolls back consequential work.

Choose a route

Ten engines, ten different jobs

Start with the problem you are trying to solve. Follow the website explanation, then inspect the linked GitHub repository for the actual skills, references, and boundaries.

01 · Frame

Name the real work

Define the decision, audience, evidence, constraints, and consequence of getting the work wrong before selecting an engine.

02 · Route

Choose the smallest accurate engine

Use one specialist engine where possible and add companion engines only when the work crosses into finance, visual design, research, engineering, or formal documentation.

03 · Verify

Keep human authority visible

Check sources, review outputs, record uncertainty, test failure paths, and keep approval, release, and rollback decisions with accountable people.

Ready to discuss your project?

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