Holistics AI Skills

Coming Soon

AI Skills is coming soon. Reach out to us via [email protected] if you're interested in early access.

Overview

If you've used Claude's custom skills, you'll get the idea. AI Skills brings the same concept to Holistics AI: instead of cramming everything into a single context.aml file, Analysts can define multiple skill definitions right in AML, each scoped to specific teams with its own instructions, data references, and access controls.

Key capabilities

• Domain-specific skills: create a finance_skill and an ecommerce_skill with different instructions, terminology, and dataset references. Each team gets AI responses tailored to their work.
• Per-user access control: use user attribute expressions to restrict skills by department, role, or any attribute (e.g., disabled: user.department != 'Finance').
• Flexible activation modes: skills can auto-activate when relevant, require manual invocation via slash commands, or stay hidden as background context. Six modes are available: auto, manual, always-on, hidden, disabled, and switchable variants.
• Dynamic context references: inject datasets, contexts, or files with preloaded (${}) or lazy (@Context:, @Dataset:, @File:) references. Lazy references keep the AI's context window lean until content is actually needed.
• Import from existing docs: upload Claude or markdown skill files and convert them into AML skill definitions.

How it works

Skills are defined as an AML type in the data model:

Skill finance_analysis {
  label: "Finance Analysis"
  description: "Specialized AI skill for finance team queries"
  disabled: user.department != 'Finance'
  content: |-
    You are a finance analyst assistant.
    Use @Dataset:finance_metrics for all queries.
    ...
}

End-users interact with skills through slash commands (/finance_analysis), UI toggles, or simply by chatting. The AI auto-selects relevant skills by default. Analysts control who sees which skills and how they are activated.

Use cases

• Domain specialization: finance, ecommerce, and support teams each get skills with domain-specific instructions and data references, instead of sharing one generic context.
• Standardized reporting: a client_performance_report skill defines exactly which KPIs to pull, how to group them, and what benchmarks to compare against, so every team member gets consistent output.
• Multi-step analysis: a quarterly_business_review skill guides the AI through a sequence: pull revenue by region, compare to targets, flag underperformers, and cross-reference with marketing spend.
• Data quality checks: a data_quality_audit skill runs a series of checks (missing IDs, duplicates, threshold breaches) and produces a structured summary with a health score.