Kpi dashboard

Skill m-binimran/finance-pack/skills/kpi-dashboard

Claude Code pack for financial analysts: data-integrity (citation), SEC/FINRA disclaimer, projection-labeling & MNPI/PII guardrails + skills for statement analysis, ratios, DCF/comps valuation, modeling, earnings, forecasting & FP&A, loops & review agents. Not investment advice.

Install

npx -y skills add m-binimran/finance-pack --skill kpi-dashboard

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Define the financial + operating KPIs that matter for a business and structure a dashboard - metric, definition, formula, target, and trend. Use when building reporting or a board/management dashboard.

SKILL.md

1.3 KB, as published. Nobody here has run it

kpi-dashboard

Measure what drives the business, defined consistently, with a target and a trend - not a vanity-metric wall.

Process

Choose KPIs tied to the model + goals (don't measure everything): e.g. revenue + growth, gross margin, CAC/LTV, payback, net revenue retention, burn/runway, ARR, EBITDA, FCF, DSO. Match to the business type.
Define each precisely: the formula, the inputs, the period, and any GAAP/non-GAAP note - so it's consistent month to month and not gameable.
Set targets/benchmarks and show the trend (and vs. plan/prior).
Group by audience: board (outcomes), management (drivers), team (operational).
Flag thresholds (red/amber/green) so attention goes to what's off.

Output

A KPI spec: metric -> definition/formula -> source -> target -> current + trend -> RAG status, grouped by audience. Every current value sourced.

Guardrails

Each KPI has a precise, consistent definition (accuracy-precision); current values sourced (data-integrity).
Targets/forecasts labeled as such; don't present a target as an actual.