AI Skills

What are AI skills?

AI Skills are reusable, packaged instructions that teach your AI agent how to perform a specific task.

Think of them as playbooks the AI can pull off the shelf at the right moment. Instead of writing a long prompt every time to explain how you want a task done, you invoke the skill — or the AI invokes it automatically based on context — and the job gets done consistently.

Why this matters:

• Institutional knowledge as code. Turn your team's playbooks and domain expertise into reusable assets — not tribal knowledge locked in one person's head.
• Consistency. Every team member gets the same methodology, every time.
• Speed. Routine tasks that used to take hours become a single prompt.

Creating AI skills

Who can create

Admins and analysts.

Where to create

Go to Development → Add AI Skill.

What goes into a skill

• Name (required): Unique identifier for the skill.
• Label: Display name shown to users.
• Description (required): What the skill does and when to use it. The AI reads this to decide when to activate the skill.
• Content (required): Knowledge and instructions the AI follows when the skill is active.
• Invocation: How the skill gets triggered:
  • Auto (default): AI decides when to invoke based on the user's question.
  • Manual: End users must invoke it explicitly.
• Allow switching invocation: Allows end-users to toggle between automatic and manual invocation.
• User: Restrict who can access the skill by attribute expression.

Code example:

Skill fin_profit_and_loss {

  label: "Profit and loss"

  disabled: H.current_user.team != "Finance" //Only Finance team can use

  description: '''
    Produces a P&L for a given period.
    Use when a user asks for a P&L, income statement, or profitability 
    — e.g., "show me the P&L for Q2," "how profitable were we last month," "income statement YTD."
  '''

  content: @md
    Pull revenue, COGS, and all expenses from ${finance_dataset}.
    Compute gross profit, net profit and their margins. 
    Present as a Metric Sheet with a monthly time dimension over the last 12 periods.
    Show variance against plan and the prior period.      
  ;;

  invocation: "auto"

  allow_switching_invocation: true
}

Best practices

• Clearly define the job. State what the skill does and when it should trigger. Include the phrases users actually say — not just formal terminology.
• One job = one skill. If a skill does three things, split it into three. The AI picks the right specific skill faster than it navigates a mega-skill.
• Write instructions, not essays. Use imperative voice ("Pull revenue from the semantic model"), give step-by-step guidance, and explain reasoning only where it changes the output.
• Show the output format explicitly. Templates beat descriptions. If you want a specific structure, write it out literally.
• Give 1–2 worked examples. One input → output pair is worth hundreds of words of explanation.
• Reference assets. Point to metrics or other analytic assets in your semantic layer instead of redefining them inline. Duplicated logic drifts over time.
• Test and iterate. Try the skill on 3–5 real user phrasings before rolling it out. If it doesn't trigger when it should, refine the description first — that's where triggering is decided.

Common use cases

Teams use AI Skills to solve a few recurring problems: repeatable work that takes too long, team-specific context the AI keeps missing, and conventions the AI doesn't follow consistently.

Workflow

A repeatable analysis your team runs the same way each time — fixed steps, known output format.

Reach for this when the task is well-defined and the output should look the same every time.

Examples:

• Weekly business review — summarizes how key metrics moved, ready before your Monday meeting.
• Promotion campaign analysis — the standard readout you run after every promo.
• Anomaly investigation — walks through segments to find what caused a spike or drop.

Team library

A curated set of skills built for one team — its workflows, vocabulary, datasets, and reporting conventions. Scope skills to a team so only the right people see them, and the AI reaches for the right one when someone from that team asks a question.

Reach for this when a team has a recurring set of questions that share the same background knowledge.

Examples:

• Finance — P&L, balance sheet, cash flow, ARR waterfall, finance terms
• Marketing — campaign performance, attribution breakdown, channel mix, marketing terms.
• Product — activation funnel, feature adoption, retention cohorts, product terms.

Convention

Rules the AI should follow whenever it does a certain kind of work. These are the shared foundation other skills build on — usually invoked through chaining, not directly.

Reach for this when you keep correcting the AI the same way across different tasks.

Examples:

• Time period conventions — called whenever a query references quarters, years, or "last period."
• Auto-layout rules — called whenever a dashboard is built.
• Chart formatting standards — called whenever a visualization is produced.

Using AI skills

Three ways skills get invoked:

• Automatically: The AI picks up on what you're asking and activates the right skill. Ask "why did sales drop?" and anomaly_investigation kicks in.
• Chained: One skill calls another in the background. For example, dashboard_building automatically calls auto_layout when it's time to arrange the charts.
• Manually: Use slash (/) and pick a skill — e.g., /fin_profit_and_loss. Handy when you want explicit control, when more than one skill could apply, or when you'd rather pick than type.