kpi-dashboard-design

Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use this skill when building an executive SaaS metrics dashboard tracking MRR, churn, and LTV/CAC ratios; designing an operations center with live service health and request throughput; creating a cohort retention analysis view for a product team; or debugging a dashboard where metrics contradict each other due to inconsistent calculation methodology.

Quality

76%

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Quality

Content

68%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is well-structured and highly actionable with strong executable examples and a clean progressive-disclosure split into references/details.md. Its main weakness is workflow clarity: the skill reads as a pattern catalog rather than a sequenced process with validation checkpoints.

Suggestions

Add a short sequenced design workflow (e.g., select KPIs -> define formulas -> choose layout -> validate metrics against source of truth) with explicit validation checkpoints so dashboard builds follow a clear process.

Trim the introductory sentence and the spelled-out SMART acronym, which restate concepts Claude already knows.

De-duplicate the MRR normalization SQL between the body and references/details.md so each formula lives in one place.

Dimension	Reasoning	Score
Conciseness	The body is largely value-dense (tables, executable SQL/Python, ASCII diagrams) with only minor over-explanation that could be trimmed, such as the introductory sentence and the spelled-out SMART acronym which Claude already knows.	4 / 5
Actionability	Provides concrete, copy-paste-ready code (MRR normalization CASE SQL, anomaly-detection and refresh Python functions, full Streamlit example) covering common cases, with minor gaps where Do/Don't lists and ASCII layouts are advisory rather than executable.	4 / 5
Workflow Clarity	The body is organized by concept and troubleshooting patterns rather than as a sequenced design workflow, and the troubleshooting sections present problem->cause->fix patterns without explicit validation checkpoints or step sequencing.	3 / 5
Progressive Disclosure	A clearly signaled one-level-deep reference ('Detailed sections ... live in references/details.md') backs a real bundle file, and most detailed content is appropriately split; minor gaps include some SQL/Python detail duplicated between the body and details.md.	4 / 5
	Total	15 / 20 Passed

Description

83%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is strong: it clearly states what the skill does and gives multiple concrete, natural trigger scenarios a user would actually voice. Minor gaps in action granularity and slight overlap with related analytics skills keep specificity and distinctiveness just below the top anchor.

Dimension	Reasoning	Score
Specificity	Names the domain plus several concrete capabilities ('metrics selection, visualization best practices, and real-time monitoring patterns') rather than vague actions, though the listed actions are somewhat high-level and not as granular as the 5-anchor example.	4 / 5
Completeness	Explicitly answers both 'what' (design KPI dashboards with metrics selection, visualization, monitoring) and 'when' ('Use this skill when building... designing... creating... or debugging...') with concrete trigger scenarios, matching the top anchor.	5 / 5
Trigger Term Quality	Strong natural triggers ('executive SaaS metrics dashboard tracking MRR, churn, and LTV/CAC ratios', 'operations center with live service health and request throughput', 'cohort retention analysis', 'debugging a dashboard where metrics contradict each other') covering synonyms and scenario phrasings, with only a few common variations absent.	4 / 5
Distinctiveness Conflict Risk	The KPI-dashboard niche with specific triggers (MRR/churn/LTV-CAC, cohort retention, ops center) is mostly distinct with minimal conflict risk, though it has minor overlap with adjacent data-viz or data-storytelling skills.	4 / 5
	Total	17 / 20 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: wshobson/agents
Path: plugins/business-analytics/skills/kpi-dashboard-design/SKILL.md
Commit: d6837ae

Reviewed: about 12 hours ago

Table of Contents

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