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kpi-dashboard-design

Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use when building business dashboards, selecting metrics, or designing data visualization layouts.

77

1.16x
Quality

66%

Does it follow best practices?

Impact

97%

1.16x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./tests/ext_conformance/artifacts/agents-wshobson/business-analytics/skills/kpi-dashboard-design/SKILL.md

The canonical home for this skill is kpi-dashboard-design in wshobson/agents

SKILL.md
Quality
Evals
Security

Quality

Content

50%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 genuinely actionable — complete, runnable SQL and dashboard code — but it is a monolithic ~420-line file that spends much of its budget restating common KPI terminology and rendering ASCII mockups Claude does not need. There is no sequenced design workflow, and nothing is split into reference files.

Suggestions

Move the per-department KPI catalogs, SQL cookbook, and Streamlit implementation into separate reference files (e.g., references/kpi-catalogs.md, references/sql.md, references/streamlit-app.py) and keep SKILL.md as a concise overview with clearly signaled one-level-deep links.

Cut content Claude already knows: the SMART KPIs definition block, generic KPI name listings, and the three ASCII-art dashboard mockups, keeping only the layout principles they illustrate.

Add an explicit step-by-step design workflow (define audience and decision → pick 5-7 KPIs per level → choose layout pattern → implement → validate against thresholds) so the skill instructs rather than just catalogs.

DimensionReasoningScore

Conciseness

The ~420-line body pads several sections with knowledge Claude already has — the SMART KPIs definition block, four catalogs of standard departmental KPIs (MRR, CAC, churn, gross margin, etc.), and decorative ASCII-art dashboard mockups — matching "several unnecessary explanations or padded sections" rather than only some.

2 / 5

Actionability

The SQL (MRR, cohort retention, CAC) and the full Streamlit/Plotly app are concrete and executable, but minor gaps remain: the MRR CASE expression lacks an ELSE (silently dropping unmatched intervals) and `freq='M'` is deprecated in modern pandas.

4 / 5

Workflow Clarity

No explicit multi-step design process is given — the body is a concept/KPI catalog followed by implementation examples — so the sequence is only implicit (concepts → KPIs → layouts → code → best practices) with no validation checkpoints or decision procedure for building a dashboard.

3 / 5

Progressive Disclosure

Section headers provide reasonable structure, but there are no bundle files at all: the per-department KPI catalogs, the SQL cookbook, and the full Streamlit implementation are all inlined in SKILL.md when they clearly belong in one-level-deep reference files.

3 / 5

Total

12

/

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.

A strong description that clearly states capabilities and includes an explicit, natural-language "Use when" clause with concrete triggers. Trigger coverage is good but could add common synonyms (e.g., "executive dashboard", "reporting", "KPIs").

DimensionReasoningScore

Specificity

Lists several specific actions — "metrics selection", "visualization best practices", and "real-time monitoring patterns" — but "best practices" and "patterns" are partially generic phrasing, leaving minor gaps versus the comprehensive anchor 5.

4 / 5

Completeness

Explicitly answers both: what ("Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns") and when ("Use when building business dashboards, selecting metrics, or designing data visualization layouts"), with concrete trigger phrases matching the anchor-5 example pattern.

5 / 5

Trigger Term Quality

Natural phrases like "building business dashboards", "selecting metrics", and "designing data visualization layouts" are present, but common variations such as "executive dashboard", "reporting", or "KPIs" as a standalone trigger word are missing.

4 / 5

Distinctiveness Conflict Risk

The KPI-dashboard niche is mostly distinct with clear triggers, but "designing data visualization layouts" overlaps slightly with general charting/dataviz skills, keeping it below the minimal-conflict anchor 5.

4 / 5

Total

17

/

20

Passed

Validation

100%

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
Dicklesworthstone/pi_agent_rust
Reviewed

Table of Contents

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