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paper-plot

Use when structured numeric data, arrays, or CSV-like measurements should be turned into a publication-quality figure by adapting a bundled paper-style plotting template instead of improvising a new chart from scratch.

76

Quality

95%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

100%

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

A well-crafted skill body: concise, actionable, with a clearly sequenced workflow and excellent one-level-deep progressive disclosure backed by real bundle files. No dimension shows a meaningful weakness.

DimensionReasoningScore

Conciseness

The body is lean: it never explains concepts Claude already knows (what a CSV/plot is, how matplotlib works) and every section — interaction discipline, use/do-not-use, styles table, workflow, substitution tips — earns its tokens.

3 / 3

Actionability

Guidance is concrete and executable: an exact styles-to-scripts table, 'Copy scripts/<script>.py into paper/figures/scripts/<figure_id>.py', 'replace only the clearly marked data and label section', and specific substitution rules (update width, color lists, tick and legend labels together). Per the rubric's instruction-skill note, the absence of inline code is not penalized because the direction is actionable and points to real bundled scripts.

3 / 3

Workflow Clarity

A clear 7-step numbered sequence with concrete file operations and an inspect checkpoint (step 6, 'inspect the rendered output') plus a conditional handoff (step 7). The operation is a non-destructive render, so the lack of an explicit retry loop does not trigger the destructive/batch cap.

3 / 3

Progressive Disclosure

SKILL.md is an overview that points to one-level-deep references/ and scripts/ files; the Available Styles table and Detailed Style Parameters section give clear navigation, and all eight referenced reference and script files verified present on disk.

3 / 3

Total

12

/

12

Passed

Description

90%

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, specific description with an explicit trigger and a clear, non-conflicting niche. Its only weakness is that it frames a single composite action rather than enumerating multiple concrete capabilities.

DimensionReasoningScore

Specificity

The description names the domain ('structured numeric data, arrays, or CSV-like measurements') and one concrete method ('turned into a publication-quality figure by adapting a bundled paper-style plotting template'), but it describes a single composite action rather than listing multiple distinct actions as the score-3 anchor requires.

2 / 3

Completeness

It opens with an explicit 'Use when' trigger and states what the skill does (turn data into a publication-quality figure by adapting a bundled template), clearly answering both when and what.

3 / 3

Trigger Term Quality

It covers natural terms a user would say when requesting this skill — 'numeric data', 'arrays', 'CSV', 'figure', 'chart', 'plotting template' — giving good coverage of likely user phrasing.

3 / 3

Distinctiveness Conflict Risk

The niche (paper-quality figures from structured data via bundled templates) is clearly scoped, and 'instead of improvising a new chart from scratch' actively differentiates it from generic plotting or final-polish skills.

3 / 3

Total

11

/

12

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository
ResearAI/DeepScientist
Reviewed

Table of Contents

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