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creating-data-visualizations

Create analytical charts and plots from existing data. Use for exploratory or reporting visuals such as bars, lines, scatters, and dashboards; not for publication-grade scientific figures or AI-generated schematics.

50

Quality

56%

Does it follow best practices?

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SecuritybySnyk

Passed

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tessl review fix ./bundled/skills/creating-data-visualizations/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

37%

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

The body is concise and well-organized as a decision guide (when to use, boundaries, integration), but it fails as execution guidance: it contains no code or commands, never references the bundled scripts, and provides no sequenced workflow with validation. The skill ships a toolchain the body never teaches Claude to use.

Suggestions

Add concrete, executable guidance: name the libraries (matplotlib/seaborn/plotly) and show how to invoke the bundled scripts (data_validator.py, data_analyzer.py, chart_generator.py) with example commands.

Add a short sequenced workflow with a validation checkpoint (e.g., 1. validate data with data_validator.py, 2. analyze with data_analyzer.py, 3. generate with chart_generator.py), re-validating on errors.

Link the overview to the bundled materials so progressive disclosure is explicit (e.g., 'See references/visualization_best_practices.md for chart-type selection').

DimensionReasoningScore

Conciseness

The body is lean, assumes Claude's competence, and avoids explaining concepts Claude already knows; it never pads with library tutorials or basic definitions, so every token earns its place.

3 / 3

Actionability

The body gives only abstract guidance ("Plotting trends...", "Choosing a sensible chart type and axis treatment") with no executable code, commands, or library names, and it never references the shipped scripts (chart_generator.py, data_analyzer.py), so it describes rather than instructs.

1 / 3

Workflow Clarity

There is no sequenced workflow in the body, only categorical sections, and the bundled data_validator.py is never mentioned despite data validation being part of the toolchain, matching 'steps unclear or missing; no validation mentioned'.

1 / 3

Progressive Disclosure

The body is cleanly sectioned (not a wall of text), but it never signals or links to the bundled scripts, reference docs, or assets, so the overview-to-detail navigation the rubric rewards is missing even though a bundle ships with the skill.

2 / 3

Total

7

/

12

Passed

Description

75%

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 on completeness and distinctiveness thanks to an explicit 'Use for' trigger and clear negative boundaries. It is somewhat weaker on specificity and trigger-term coverage, naming only one action and a limited set of natural keywords.

Suggestions

Add one or two more concrete actions (e.g., 'generate, style, and export charts') so specificity reaches the multi-action bar for a 3.

Broaden trigger terms to include common user phrasings like 'graph', 'visualize', or 'plot this data' alongside 'charts' and 'plots'.

DimensionReasoningScore

Specificity

It names the domain and one concrete action ("Create analytical charts and plots from existing data") plus chart types, but does not list multiple distinct concrete actions the way a 3 would, so it lands at 'names domain and some actions, but not comprehensive'.

2 / 3

Completeness

It clearly states what it does ("Create analytical charts and plots from existing data") and gives an explicit 'Use for ...' trigger with both positive and negative guidance ("not for publication-grade scientific figures or AI-generated schematics"), answering both what and when.

3 / 3

Trigger Term Quality

Natural terms users would say are present ("charts", "plots", "dashboards", "bars", "lines", "scatters"), but common variations such as "graph", "visualize", or "plot this data" are absent, leaving coverage incomplete rather than thorough.

2 / 3

Distinctiveness Conflict Risk

The explicit negative boundary ("not for publication-grade scientific figures or AI-generated schematics") carves out a clear niche distinct from scientific-visualization and scientific-schematics, making wrong-skill triggering unlikely.

3 / 3

Total

10

/

12

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

14

/

16

Passed

Repository
foryourhealth111-pixel/Vibe-Skills
Reviewed

Table of Contents

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