Metrics Dashboard Agent Skill

Define and design a product metrics dashboard with key metrics, data sources, visualization types, and alert thresholds. Use when creating a metrics dashboard, defining KPIs, setting up product analytics, or building a data monitoring plan.

1k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

24819

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/phuryn/pm-skills --skill metrics-dashboard

The instruction itself

5 sections, as written by the author

Product Metrics Dashboard

Design a comprehensive product metrics dashboard with the right metrics, visualizations, and alert thresholds.

Context

You are designing a metrics dashboard for $ARGUMENTS.

If the user provides files (existing dashboards, analytics data, OKRs, or strategy docs), read them first.

Domain Context

Metrics vs KPIs vs NSM: Metrics = all measurable things. KPIs = a few key quantitative metrics tracked over a longer period. North Star Metric = a single customer-centric KPI that is a leading indicator of business success.

4 criteria for a good metric (Ben Yoskovitz, *Lean Analytics*): (1) Understandable — creates a common language. (2) Comparative — over time, not a snapshot. (3) Ratio or Rate — more revealing than whole numbers. (4) Behavior-changing — the Golden Rule: "If a metric won't change how you behave, it's a bad metric."

8 metric types: Vanity vs Actionable (only actionable metrics change behavior), Qualitative vs Quantitative (WHAT vs WHY — you need both; never stop talking to customers), Exploratory vs Reporting (explore data to uncover unexpected insights), Lagging vs Leading (leading indicators enable faster learning cycles, e.g. customer complaints predict churn).

5 action steps: (1) Audit metrics against the 4 good-metric criteria. (2) Update dashboards — ensure all key metrics are good ones. (3) Identify vanity metrics — be careful how you use them. (4) Classify leading vs lagging indicators. (5) Pick one problem and dig deep into the data.

For case studies and more detail: Are You Tracking the Right Metrics? by Ben Yoskovitz

Instructions

Identify the metrics framework — organize metrics into layers:

North Star Metric: The single metric that best captures core value delivery

Input Metrics (3-5): The levers that drive the North Star

Health Metrics: Guardrails that ensure overall product health

Business Metrics: Revenue, cost, and unit economics

For each metric, define:

|---|---|---|---|---|---|

Design the dashboard layout:

   ┌─────────────────────────────────────────────┐
   │  NORTH STAR: [Metric] — [Current Value]     │
   │  Trend: [↑/↓ X% vs last period]             │
   ├──────────────────┬──────────────────────────┤
   │  Input Metric 1  │  Input Metric 2          │
   │  [Sparkline]     │  [Sparkline]             │
   ├──────────────────┼──────────────────────────┤
   │  Input Metric 3  │  Input Metric 4          │
   │  [Sparkline]     │  [Sparkline]             │
   ├──────────────────┴──────────────────────────┤
   │  HEALTH: [Latency] [Error Rate] [NPS]       │
   ├─────────────────────────────────────────────┤
   │  BUSINESS: [MRR] [CAC] [LTV] [Churn]        │
   └─────────────────────────────────────────────┘

Set review cadence:
Daily: Operational health (errors, latency, critical flows)
Weekly: Input metrics and engagement trends
Monthly: North Star, business metrics, OKR progress
Quarterly: Strategic review and metric recalibration

Define alerts:
What thresholds trigger investigation?
Who gets alerted and through what channel?
What's the expected response time?

Recommend tools based on the user's context:
Amplitude, Mixpanel, PostHog for product analytics
Looker, Metabase, Mode for SQL-based dashboards
Datadog, Grafana for operational health

Think step by step. Save the dashboard specification as a markdown document.

Other skills for the same job

different authors, same section of the catalogue

XLSX

by anthropics

vendor ×15

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.

Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.

MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.

Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.

7k tokens

How to use it

Copy the folder

Take phuryn/metrics-dashboard from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Metrics Dashboard Agent Skill

Install

The instruction itself

Product Metrics Dashboard

Context

Domain Context

Instructions

Further Reading

Other skills for the same job

How to use it