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

71%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./plugins/business-analytics/skills/kpi-dashboard-design/SKILL.md

Quality

Discovery

100%

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

This is an excellent skill description that clearly articulates specific capabilities, provides rich trigger terms that users would naturally use, and explicitly covers both what the skill does and when to use it with concrete scenarios. The description uses proper third-person voice and avoids vague language, making it highly effective for skill selection among many options.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: metrics selection, visualization best practices, real-time monitoring patterns, and gives detailed examples like tracking MRR/churn/LTV-CAC ratios, live service health monitoring, cohort retention analysis, and debugging contradictory metrics.	3 / 3
Completeness	Clearly answers both 'what' (design KPI dashboards with metrics selection, visualization best practices, real-time monitoring patterns) and 'when' with explicit trigger scenarios (executive SaaS metrics dashboard, operations center, cohort retention analysis, debugging contradictory metrics).	3 / 3
Trigger Term Quality	Excellent coverage of natural terms users would say: 'KPI dashboard', 'metrics', 'MRR', 'churn', 'LTV/CAC', 'operations center', 'service health', 'throughput', 'cohort retention', 'dashboard', 'real-time monitoring'. These are terms practitioners naturally use when requesting this kind of work.	3 / 3
Distinctiveness Conflict Risk	Highly distinctive with a clear niche around KPI dashboards and metrics monitoring. The specific domain terms (MRR, churn, LTV/CAC, cohort retention, service health) make it very unlikely to conflict with general data visualization or other analytics skills.	3 / 3
	Total	12 / 12 Passed

Implementation

42%

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

The skill excels in actionability with executable SQL, Python/Streamlit code, and practical troubleshooting scenarios that address real-world dashboard problems. However, it is severely bloated — much of the content (SMART framework, department KPI lists, basic dashboard concepts) is general knowledge Claude already possesses. The monolithic structure with no progressive disclosure means the entire ~350-line file loads into context even when only a fraction is needed.

Suggestions

Extract the department KPI lists, ASCII dashboard mockups, and full Streamlit implementation into separate referenced files (e.g., KPIS_BY_DEPARTMENT.md, LAYOUT_PATTERNS.md, STREAMLIT_EXAMPLE.py) and keep SKILL.md as a concise overview with links.

Remove explanations of concepts Claude already knows: SMART KPIs, what MRR/ARR/NPS are, basic do's/don'ts of dashboard design. Focus the main file on the unique value: SQL calculation patterns, troubleshooting mismatches, and the specific implementation guidance.

Add a clear sequential workflow for dashboard creation (e.g., 1. Define audience → 2. Select KPIs → 3. Write/validate SQL → 4. Build layout → 5. Add alerts → 6. Verify metrics match source of truth) with explicit validation checkpoints.

Consolidate the troubleshooting section as the primary differentiator of this skill — it's the strongest section — and promote it higher in the document structure.

Dimension	Reasoning	Score
Conciseness	The skill is extremely verbose at ~350+ lines. It explains basic concepts Claude already knows (SMART KPIs, what MRR/ARR/NPS are), includes exhaustive KPI lists by department that are general knowledge, and has lengthy ASCII art dashboard mockups. The YAML KPI lists alone consume significant tokens without adding actionable value beyond what Claude could generate on its own.	1 / 3
Actionability	The skill provides fully executable SQL queries for MRR, cohort retention, and CAC calculations, plus a complete Streamlit dashboard implementation with Plotly charts. The troubleshooting section includes concrete code fixes for specific problems. All code examples are copy-paste ready.	3 / 3
Workflow Clarity	There is no clear sequential workflow for building a dashboard from start to finish — the content is organized as reference material rather than a step-by-step process. The troubleshooting section provides good diagnostic sequences, but there are no validation checkpoints for the dashboard creation process itself (e.g., verify data accuracy before publishing).	2 / 3
Progressive Disclosure	The content is a monolithic wall of text with everything inline — exhaustive KPI lists, full ASCII mockups, complete SQL queries, and a full Streamlit app all in one file. The KPI lists by department, dashboard layout patterns, and implementation code should each be in separate referenced files. Only one related skill is mentioned at the end with no structured navigation.	1 / 3
	Total	7 / 12 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 — 11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository: wshobson/agents
Commit: 70444e5

Reviewed: about 6 hours ago

Table of Contents

Discovery Implementation Validation

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