Skills
What's the difference between an AI that answers questions and one that runs your workflow?
A skill packages a repeatable workflow into a single slash command. Instead of re-prompting the same instructions every session, you invoke /review-pr or /commit — the AI follows a predefined set of steps, quality gates, and output formats. One command. Consistent results.
Better tools make better skills. Better skills make better Units of Work. Better Units of Work create more valuable outputs and outcomes.
Unit Of Work
A Unit of Work is incompressible value: the smallest action of value.
A skill is not the Unit of Work. A skill is the reusable capability used to perform, review, or improve the Unit of Work. The standard defines the fitting: inputs, outputs, proof, and the next interface the result must connect to.
Tool -> Skill -> Unit of Work -> Valuable Output -> Outcome
Example:
| Layer | Example | Standard Question |
|---|---|---|
| Tool | Link checker, browser, model, CLI, database | Can the skill act with enough leverage? |
| Skill | fix-links, docs-edit, validate-demand | Is the method repeatable and gated? |
| Unit of Work | Fix broken internal links in one docs page | Is this the smallest action of value? |
| Output | Page with valid links and no broken routes | Can the next workflow consume it cleanly? |
| Outcome | Reader stays in flow; agent trust increases | Did the work create value? |
The standard matters because the Unit of Work must connect to the next Unit of Work without translation. That is the business-engineering equivalent of fittings in physical engineering.
How Skills Work
AI coding tools like Claude Code expose skills as slash commands. Each skill contains:
- Instructions — the steps the AI must follow
- Quality gates — what "done" looks like before the AI reports success
- Argument hints — what input the skill expects (a file path, a PR number, free text)
- Trigger phrases — natural language that activates the skill automatically
The pattern is composable. A publishing workflow might invoke /fix-links, then /review-pr, then /commit — all from a single /publish command. Small, correct skills chain into larger workflows.
Tool quality sets the ceiling. If the tool cannot inspect the right state, write the right artifact, or verify the result, the skill becomes a script-shaped wish. If the tool is precise, observable, and well-scoped, the skill can become a reliable business capability.
The Market Pattern
Any instruction a team repeats more than three times is a candidate for a skill.
Without skills — re-prompt every session, inconsistent output, knowledge lives in one person, slow onboarding.
With skills — one invocation per workflow, gates enforce quality, skill is shareable across the team, new hires invoke rather than learn.
Skills shift AI from a question-answering tool to a workflow execution engine. The difference is whether judgment is embedded in the skill or re-applied every time.
Skill Maturity
Not all skills are equal. A maturity model applies:
L0 — Idea — exists as a repeated manual prompt
L1 — Defined — written down with steps and gates
L2 — Connected — invoked consistently across sessions
L3 — Tested — has caught at least one error via a gate
L4 — Proven — zero manual interventions, measurable improvement
L4 skills are institutional knowledge, encoded. They outlast any individual contributor.
Context
- Prompts — the building blocks skills are made from
- Autonomous Agents — when tasks need agents, not skills
- Coding Tools — where skills run
- Standards — the AAIF skill specification
Questions
What's the highest-value repeatable workflow in your current work that hasn't been packaged as a skill yet?
- How do you decide when a workflow is stable enough to package as a skill versus keeping it as an improvised prompt?
- What's the difference between a skill that saves time and one that just moves the work somewhere else?
- If L4 skills encode institutional knowledge — what happens to that knowledge when an AI tool is deprecated?