CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

data-visualizer

Use this skill when the user wants to create charts, graphs, or dashboards from data — bar charts, line charts, scatter plots, heatmaps, or multi-panel dashboards.

Quality

77%

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

Fix and improve this skill with Tessl

tessl review fix ./external/community/data-visualizer/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

68%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 body delivers lean, mostly executable code for static and interactive charts with good section organization. Its weaknesses are the absence of any workflow connecting the pieces (no library-choice guidance, no output validation) and small completeness gaps like the unimported plotly.express and a stale hardcoded model ID.

Suggestions

Add a short decision flow (e.g., 'Use matplotlib for static PNG/SVG output, Plotly for interactive HTML; start with the selector only when the user is unsure of the chart type') and an output-verification step so the sections form a sequence rather than a function library.

Fix the Quick Reference gaps: import plotly.express before using px.imshow, or provide a one-line heatmap/histogram snippet consistent with the other entries.

Replace the hardcoded 'claude-opus-4-6' model ID with current-model guidance (or a documented placeholder) so the selector code remains executable over time.

DimensionReasoningScore

Conciseness

The body is nearly all executable code with no explanations of concepts Claude already knows; the trimmable parts are minor — the buzzwordy "Includes AI-powered chart type selection" line and a chart-type selector that duplicates Claude's native judgment.

4 / 5

Actionability

The bar_chart, line_chart, and create_dashboard functions are concrete and copy-paste ready, but there are minor gaps: the Quick Reference uses px.imshow without any plotly.express import anywhere, no example invocations are shown, and the selector hardcodes a stale model ID ("claude-opus-4-6").

4 / 5

Workflow Clarity

The skill reads as a function library with no sequence: the chart-type selector is never connected to the plotting functions, there is no decision guidance for matplotlib vs Plotly beyond a parenthetical, and there is no output verification step. Each section is individually well-defined, which keeps it above a 2, but no checkpoints or flow exist to reach a 4.

3 / 5

Progressive Disclosure

No bundle files exist, and the ~130-line body is well organized with clear section headers plus a Quick Reference table for navigation. Not a 5 because the chart-type selector and dashboard code could reasonably live in reference files, but nothing is buried or misplaced.

4 / 5

Total

15

/

20

Passed

Description

75%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 solid description with concrete capability enumeration and an explicit 'Use when' trigger clause. Its main weakness is redundancy — the when-clause mirrors the what — and missing common synonyms like 'plot' or 'visualize' that would round out trigger coverage.

DimensionReasoningScore

Specificity

Enumerates concrete output types ("bar charts, line charts, scatter plots, heatmaps, or multi-panel dashboards") but relies on a single verb ("create") and omits libraries, output formats, and any styling or customization actions, leaving minor gaps in coverage rather than the comprehensive list of a 5.

4 / 5

Completeness

Both what ("create charts... from data") and an explicit when ("Use this skill when the user wants to create charts, graphs, or dashboards from data") are present. Not a 5 because the when-clause merely restates the what and adds no distinct trigger contexts or user-facing phrasings beyond the capability list itself.

4 / 5

Trigger Term Quality

Natural phrases users would say — "charts, graphs, dashboards, heatmap" — are all present, but common synonyms like "plot", "figure", and "visualize data" are missing, so coverage is good rather than comprehensive.

4 / 5

Distinctiveness Conflict Risk

The charting/data-visualization niche is clear and unlikely to trigger for unrelated skills, but broad terms like "charts" and "dashboards" overlap with general data-analysis or reporting skills, and no library (matplotlib/Plotly) is named to sharpen the boundary.

4 / 5

Total

16

/

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

ProwlrBot/prowlr-marketplace

Path

external/community/data-visualizer/SKILL.md

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.