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

—

No eval scenarios have been run

Securityby

Passed

No known issues

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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 defines its scope around KPI dashboard design and provides rich, specific trigger scenarios. It uses third-person voice correctly, includes domain-specific terminology that users would naturally employ, and explicitly addresses both what the skill does and when to use it with four distinct use cases. The description is comprehensive without being unnecessarily verbose.

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, cohort retention analysis, 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 using 'Use this skill when...' followed by four concrete use cases.	3 / 3
Trigger Term Quality	Excellent coverage of natural terms users would say: 'KPI dashboard', 'metrics', 'MRR', 'churn', 'LTV/CAC', '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 provides genuinely useful, executable code examples (SQL, Python/Streamlit) and an excellent troubleshooting section with specific diagnostic patterns. However, it is severely bloated with general business knowledge Claude already possesses (KPI definitions, SMART framework, department metric lists) and packs everything into a single file with no progressive disclosure. Cutting the content by 60% and splitting implementation details into bundle files would dramatically improve its effectiveness.

Suggestions

Remove or drastically reduce the 'Common KPIs by Department' section — Claude already knows standard business metrics. Keep only non-obvious metric relationships or calculation gotchas.

Extract the SQL queries, Streamlit code, and ASCII dashboard patterns into separate bundle files (e.g., `sql-examples.md`, `streamlit-template.py`, `layout-patterns.md`) and reference them from the main SKILL.md.

Remove the SMART KPIs section and the KPI Framework table — these are basic business concepts that add no value for Claude.

Add a clear end-to-end workflow with validation steps: e.g., 1) Identify audience → 2) Select metrics (with validation: check for vanity metrics) → 3) Define calculations (with validation: cross-check with finance) → 4) Build layout → 5) Set alert thresholds → 6) Review with stakeholders.

Dimension	Reasoning	Score
Conciseness	The skill is extremely verbose at ~400+ lines. It explains basic concepts Claude already knows (SMART KPIs, what MRR/ARR/CAC are), includes exhaustive KPI lists by department that are general business knowledge, and provides lengthy ASCII art dashboard mockups. The YAML-formatted 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 working Streamlit dashboard with Plotly charts. The troubleshooting section includes concrete code fixes for specific problems. All code examples are copy-paste ready and use real libraries.	3 / 3
Workflow Clarity	The skill lacks a clear sequential workflow for building a dashboard end-to-end. While individual sections are well-organized and the troubleshooting section provides good diagnostic sequences, there are no explicit validation checkpoints (e.g., verify data accuracy before publishing, test with stakeholders). The content reads more as a reference catalog than a guided process.	2 / 3
Progressive Disclosure	The entire skill is a monolithic wall of content with no bundle files to offload detail into. The extensive KPI lists by department, multiple ASCII dashboard patterns, full SQL queries, and complete Streamlit code should be split into separate reference files. Only one cross-reference exists (to `data-storytelling`), and there's no navigation structure to help find specific sections quickly.	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: 112197c

Reviewed: 1 day ago

Table of Contents

Discovery Implementation Validation

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