CtrlK
BlogDocsLog inGet started
Tessl Logo

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.

67

Quality

80%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

78%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 skill body is a well-organized, actionable design framework that leverages a clean one-level-deep reference bundle and closes with an output checklist. Its main weaknesses are minor redundancy of the mobile-state guidance across sections and the absence of an explicit validation feedback loop in the workflow.

Suggestions

Consolidate the repeated mobile reconnect/stale/offline guidance into one authoritative section and cross-reference it elsewhere to remove redundancy.

Add a short validate-and-retry or self-check step between Performance Defaults and Output Expectations so the workflow has an explicit feedback loop (e.g., 'verify frame budget against the chosen renderer before finalizing').

Tighten the Output Expectations list by merging the two mobile-first scan items so the checklist reads as a single pass over mobile and desktop legibility.

DimensionReasoningScore

Conciseness

The body is dense, directive prose that assumes Claude's competence and avoids explaining basic concepts, but mobile reconnect/stale/offline guidance recurs across Real-Time Design Rules, Output Expectations, and the references, so minor trimming is possible without anchoring 5.

4 / 5

Actionability

Concrete directives like 'Keep the last known good visualization visible during reconnects, with stale, delayed, partial, offline, and error states distinct' and named renderers (SVG, Canvas2D, WebGL, deck.gl, PixiJS, Sigma.js, Three.js) give specific, executable design guidance; it falls short of anchor 5 only because no runnable examples are provided, which is acceptable for an instruction-only skill.

4 / 5

Workflow Clarity

A clear sequence runs from Default Questions (discovery) through Design Rules to Output Expectations (a verification checklist), providing most checkpoints, but there is no explicit validate-and-retry feedback loop, which keeps it below anchor 5.

4 / 5

Progressive Disclosure

The body is a clear overview with a well-signaled, one-level-deep References section split into 'Shared theory' and 'Skill references', and all four bundled reference files (monitoring-vs-analysis, streaming-data-pipelines, interaction-patterns, performance-and-degradation) are real and consistently structured, matching the anchor 5 example.

5 / 5

Total

17

/

20

Passed

Description

82%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 explicitly pairs a clear capability with a concrete 'Use when' trigger clause and uses distinct, natural operational-visualization terms. Its only gap is that it lists one design action plus several trigger scenarios rather than multiple distinct concrete actions.

DimensionReasoningScore

Specificity

It names the domain ('dashboards and live visualization systems') with one concrete action ('Design'), but the remaining items ('monitoring views, streaming charts, coordinated interactions, downsampling, performance-aware operational visualization') are trigger scenarios rather than distinct concrete actions, so it does not reach the several-actions bar of anchor 4.

3 / 5

Completeness

It explicitly answers both what ('Design dashboards and live visualization systems') and when ('Use when the user needs monitoring views, streaming charts, coordinated interactions, downsampling, or performance-aware operational visualization') with concrete trigger phrases, matching the anchor 5 example pattern.

5 / 5

Trigger Term Quality

Phrases like 'monitoring views', 'streaming charts', 'coordinated interactions', 'downsampling', and 'performance-aware operational visualization' are natural terms users would say, giving good keyword coverage, though common synonyms like 'live dashboard', 'real-time', or 'ops dashboard' are missing.

4 / 5

Distinctiveness Conflict Risk

The niche is clear—real-time operational visualization—and the triggers ('streaming charts', 'downsampling', 'performance-aware operational visualization') are distinct from sibling data-viz skills, giving minimal conflict risk.

5 / 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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
openai/plugins
Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.