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dashboards-and-real-time-visualization

Design dashboards and live visualization systems. Use when the user needs monitoring views, streaming charts, coordinated interactions, downsampling, or performance-aware operational visualization.

76

Quality

93%

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SKILL.md
Quality
Evals
Security

Quality

Content

87%

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

A well-structured, actionable instruction skill with efficient prose and proper progressive disclosure via real bundle references. The main gap is workflow clarity: it reads as a rule reference rather than a sequenced, checkpointed workflow.

Suggestions

Add an explicit ordered workflow (e.g. 1. Classify update model via Default Questions → 2. Apply matching Design Rules → 3. Verify against Output Expectations) with a checkpoint where you re-read the Output Expectations before declaring the design complete.

Insert at least one validation step in the Performance or Output section (e.g. 'confirm frame/memory budgets are stated and that the chosen renderer matches the mark-count tier before building').

Cross-link each body section to its dedicated reference (e.g. point the Performance Defaults list to ./references/performance-and-degradation.md inline) so the overview clearly signals where depth lives.

DimensionReasoningScore

Conciseness

The body is lean and rule-driven—no explanations of concepts Claude already knows (no "a dashboard is..." preamble), with tight bullets that assume competence and earn their tokens. It is long for a complex multi-faceted skill but not padded.

3 / 3

Actionability

Despite being code-free, the guidance is concrete and specific: "Budget for 16 ms frames", "Use ring buffers, viewport culling, and multi-resolution summaries", and explicit renderer picks (SVG/Canvas2D/WebGL/deck.gl/PixiJS/Sigma.js/Three.js), satisfying the actionable-instruction anchor.

3 / 3

Workflow Clarity

Sections are organized (Default Questions → Design Rules → Layout → Interaction → Performance → Output Expectations) and the Output Expectations act as a deliverable checklist, but there is no explicit sequenced workflow with validation checkpoints or feedback loops.

2 / 3

Progressive Disclosure

The body is an overview pointing to one-level-deep, clearly categorized references; the four local bundle paths (./references/monitoring-vs-analysis.md, streaming-data-pipelines.md, interaction-patterns.md, performance-and-degradation.md) all exist and are signaled under a grouped References section.

3 / 3

Total

11

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12

Passed

Description

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.

A strong description: third-person voice, concrete action verb, explicit "Use when" trigger with natural keywords, and a clearly distinct niche. No fluff or over-claims.

DimensionReasoningScore

Specificity

Names the domain with a concrete verb ("Design dashboards and live visualization systems") and enumerates multiple specific capability areas—monitoring views, streaming charts, coordinated interactions, downsampling, performance-aware operational visualization—matching the multiple-concrete-actions anchor.

3 / 3

Completeness

It explicitly answers both what ("Design dashboards and live visualization systems") and when ("Use when the user needs monitoring views..."), with an explicit trigger clause, satisfying the top anchor.

3 / 3

Trigger Term Quality

The "Use when" clause lists natural user-facing terms ("monitoring views", "streaming charts", "coordinated interactions", "downsampling", "operational visualization") that a user would plausibly say when needing this skill, with good coverage and no jargon-only phrasing.

3 / 3

Distinctiveness Conflict Risk

The real-time/dashboard niche is distinct and the body even routes overlapping testing work elsewhere (../testing-data-visualizations), making unintended triggering unlikely.

3 / 3

Total

12

/

12

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
openai/plugins
Reviewed

Table of Contents

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