Advisor Dashboards

This handles the full stack of dashboard design for RIAs, from executive practice analytics down to daily advisor operational views. You'd reach for it when building KPI frameworks, tracking AUM decomposition (market vs. organic growth), analyzing client attrition patterns, or setting up exception alerts for things like rebalancing drift or NIGO issues. The source material is thorough on the metrics that matter: organic growth rate, revenue per advisor, effective fee rate trends, client retention breakdowns. It covers both the technical side (role-based access, data integration architecture) and the strategic side (benchmarking frameworks, capacity planning). Useful if you're past basic reporting and need to think through what metrics actually drive RIA decision making.

skills.sh installs: 690
GitHub stars: 224
last updated: Aug 21, 2026

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Install

$ npx -y skills add joellewis/finance_skills --skill advisor-dashboards --agent claude-code

Installs into .claude/skills of the current project.

Files

Advisor Dashboards

Core Concepts

1. Practice-Level KPIs

Key performance indicators for advisory practices fall into several categories, each measuring a different dimension of firm health. A well-designed KPI framework provides both a snapshot of current performance and the trend data needed to identify emerging risks or opportunities.

AUM (Assets Under Management). The foundational metric for any AUM-based advisory practice. Total firm AUM is the product of client count, average relationship size, and market performance. AUM should be tracked at multiple levels: firm total, by advisor or team, by client segment (high-net-worth, mass affluent, institutional), by account type (taxable, IRA, trust, plan), and by custodian. AUM changes decompose into two components — market appreciation/depreciation and net new assets — and tracking each separately reveals whether growth is organic (advisor-driven) or market-driven.

SKILL.md on GitHub