Content
75%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-structured, actionable skill body: a clear six-step workflow, a concrete matplotlib template, and tight best-practice guidance with almost no padding. The main improvements are removing the redundancy between the Tips section and the workflow steps, adding a light validation checkpoint (e.g., confirming the data is non-empty and the chart file was written), and fixing the broken CONNECTORS.md link.
Suggestions
Remove the redundancy in the Tips section — 'If you want interactive charts... Claude will use plotly' duplicates §4 and 'Charts are saved to your current directory as PNG files' duplicates §6; consolidate these into the workflow steps once.
Add a light validation checkpoint in the workflow (e.g., verify the DataFrame is non-empty before plotting and confirm the PNG file was successfully written after savefig) to close the minor workflow-clarity gap.
Fix or remove the broken [CONNECTORS.md](../../CONNECTORS.md) link — the file does not exist at the resolved path, which breaks the skill's only external navigation.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and assumes competence — a lookup table for chart selection, a code template, and terse best-practice bullets with no explanation of what matplotlib or DataFrames are. Minor trimmable redundancy remains: the intro sentence restates the frontmatter description ('Create publication-quality data visualizations using Python. Generates charts with best practices...'), and the Tips section repeats §4 (interactive → plotly) and §6 (PNG output location). This fits anchor 4 ('efficient; minor instances of over-explanation that could be trimmed') rather than anchor 5, where every token earns its place. | 4 / 5 |
Actionability | Concrete, near-executable guidance throughout: a complete matplotlib boilerplate with specific style ('seaborn-v0_8-whitegrid'), palette, figsize, dpi, and spine-removal calls, plus explicit number-formatting rules ('45.2%' not '0.452', '$1.2M' not '1200000'). The '[chart-specific code]' placeholder and lack of any plotly example keep it from anchor 5's 'copy-paste ready code covering common cases', but it is well above anchor 3's pseudocode level. | 4 / 5 |
Workflow Clarity | The six-step workflow (understand → get data → select chart → generate → apply design → save/present) is clearly sequenced with branching input handling in step 2 and a decision table in step 3. No explicit validation checkpoints exist (e.g., verifying the DataFrame is non-empty or the PNG rendered correctly), but since this is a generative, non-destructive task, the destructive/batch cap does not apply — this matches anchor 4 ('clear sequence with most checkpoints present; minor validation gaps') rather than anchor 5's explicit validate/fix/retry loop. | 4 / 5 |
Progressive Disclosure | As a single-file skill with no references/, scripts/, or assets/ directories, all content is inline and organized under clean section headers of appropriate length (~150 lines) — reasonable for a skill of this scope, fitting the well-organized-structure criterion. However, the only external navigation is a link to [CONNECTORS.md](../../CONNECTORS.md), which does not exist at the resolved path (verified broken), and no reference files offload the sizable chart-selection table or design best practices. This places it at anchor 4 ('good structure; most content appropriately placed; minor organization gaps') rather than anchor 5's cleanly split, well-signaled references. | 4 / 5 |
Total | 16 / 20 Passed |