pymatgen

Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science.

1.13x

Quality

70%

Does it follow best practices?

Impact

95%

1.13x

Average score across 3 eval scenarios

Securityby

Advisory

Suggest reviewing before use

Optimize this skill with Tessl

npx tessl skill review --optimize ./scientific-skills/pymatgen/SKILL.md

Quality

Discovery

82%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This is a strong, domain-specific description that effectively communicates concrete capabilities using natural terminology from computational materials science. Its main weakness is the absence of an explicit 'Use when...' clause, which would help Claude know precisely when to select this skill. The specificity and distinctiveness are excellent for a specialized scientific toolkit.

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about crystal structures, materials properties, CIF/POSCAR files, phase diagrams, band structure calculations, or Materials Project queries.'

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions and domains: crystal structures with specific formats (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, and format conversion. These are concrete, identifiable capabilities.	3 / 3
Completeness	The 'what' is well covered with specific capabilities, but there is no explicit 'Use when...' clause or equivalent trigger guidance. The description only implies when it should be used through listing capabilities. Per rubric guidelines, missing 'Use when...' caps completeness at 2.	2 / 3
Trigger Term Quality	Includes strong natural keywords a computational materials scientist would use: 'crystal structures', 'CIF', 'POSCAR', 'phase diagrams', 'band structure', 'DOS', 'Materials Project', 'format conversion', 'computational materials science'. These are the exact terms users in this domain would naturally mention.	3 / 3
Distinctiveness Conflict Risk	Highly distinctive niche in computational materials science with domain-specific triggers like CIF, POSCAR, Materials Project, band structure, and DOS. Very unlikely to conflict with other skills given the specialized terminology.	3 / 3
	Total	11 / 12 Passed

Implementation

57%

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

The skill excels in actionability with comprehensive, executable code examples covering the full breadth of pymatgen functionality, and has excellent progressive disclosure with clear references to detailed documentation. However, it is far too verbose—much of the content (best practices, integration lists, 'when to use' section, troubleshooting basics) is either obvious to Claude or adds minimal value, and multi-step workflows lack explicit validation/convergence checkpoints that are critical for computational materials science workflows.

Suggestions

Cut the 'When to Use This Skill', 'Integration with Other Tools', and 'Additional Resources' sections entirely—Claude knows when to use pymatgen and can find URLs. Remove generic best practices like 'Handle exceptions' and 'Cache results'.

Reduce the overview to 1-2 lines and eliminate the installation section (Claude knows how to pip install).

Add explicit validation/convergence checks to multi-step workflows, e.g., after relaxation: check that CONTCAR exists and that ionic steps converged before proceeding to static calculation.

Consolidate the 'Units and Conventions' into a single inline note where units first appear rather than a dedicated section.

Dimension	Reasoning	Score
Conciseness	The skill is extremely verbose at ~400+ lines, explaining many concepts Claude already knows (what pymatgen is, what PDF-like format detection means, basic Python patterns). Sections like 'When to Use This Skill', 'Integration with Other Tools' (just a list of names), 'Best Practices' with generic advice ('Handle exceptions', 'Cache results'), and 'Units and Conventions' add little value. The overview paragraph restates what the description already covers.	1 / 3
Actionability	The skill provides extensive executable code examples throughout—structure creation, file conversion, phase diagrams, band structure analysis, VASP input generation, surface analysis, and more. Code snippets are copy-paste ready with proper imports and realistic parameters.	3 / 3
Workflow Clarity	Multi-step workflows like the band structure calculation workflow are sequenced with numbered steps, but they lack explicit validation checkpoints. For example, there's no check that relaxation converged before proceeding to static calculation, and the surface energy workflow assumes calculations completed successfully without verification steps.	2 / 3
Progressive Disclosure	The skill has a clear structure with a Quick Start section, core capabilities with inline examples, and well-signaled one-level-deep references to separate files (references/core_classes.md, references/io_formats.md, etc.). Scripts are documented with usage examples and the reference files are clearly catalogued.	3 / 3
	Total	9 / 12 Passed

Validation

81%

Warnings & errors only

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

Validation — 9 / 11 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (690 lines); consider splitting into references/ and linking	Warning
metadata_version	'metadata.version' is missing	Warning

	Total	9 / 11 Passed

Repository: K-Dense-AI/claude-scientific-skills
Commit: 25e1c0f

Reviewed: 4 days ago

Table of Contents

Discovery Implementation Validation

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