pymatgen

Analyze, validate, convert, and transform materials structures and computed materials data with current pymatgen APIs, including local phase diagrams, symmetry sensitivity, electronic-structure I/O, and explicitly bounded Materials Project queries.

Quality

83%

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Securityby

Low

Low-risk findings worth noting

Quality

Content

92%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.

A highly actionable, well-structured body with executable examples, explicit validation checkpoints, and clean one-level-deep progressive disclosure; the only lever is minor conciseness trimming.

Dimension	Reasoning	Score
Conciseness	Dense, actionable content with little concept padding Claude already knows, but the body is long and a few sections (license preamble, citation instructions) could be trimmed, so it sits at efficient-with-minor-trimming rather than maximally lean.	4 / 5
Actionability	Copy-paste-ready executable code and CLI commands cover the common cases (Composition/Structure/Molecule, CifParser, SpacegroupAnalyzer, TransformedStructure, Vasprun, QCInput, mp_query) with specific, runnable examples throughout.	5 / 5
Workflow Clarity	A 12-step sequenced Required workflow with explicit validation checkpoints (e.g. 'Run validation before symmetry...'), plus feedback loops for risky operations (inspect parser warnings, round-trip-check conversions, dry-run-then-execute for MP).	5 / 5
Progressive Disclosure	SKILL.md is a clear overview with well-signaled, one-level-deep references to references/.md (verified: no nested .md references) and scripts/.py, plus a consolidated References section for easy navigation.	5 / 5
	Total	19 / 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 specific, third-person description with concrete actions and a distinct niche, but it omits any explicit 'Use when...' trigger guidance, which caps completeness and slightly limits trigger discoverability.

Suggestions

Add an explicit 'Use when...' clause naming natural trigger situations, e.g. 'Use when working with .cif/POSCAR files, building phase diagrams, assigning space groups, or querying Materials Project.'

Include common file extensions and synonyms (.cif, POSCAR, vasprun.xml, VASP, band structure) so the description matches phrases users actually say.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions ('Analyze, validate, convert, and transform') plus specific capabilities ('local phase diagrams, symmetry sensitivity, electronic-structure I/O, and explicitly bounded Materials Project queries'), giving comprehensive coverage of the domain.	5 / 5
Completeness	The 'what' is clearly stated, but there is no 'Use when...' clause or equivalent explicit trigger guidance, so per the rubric completeness is capped at 3 with 'when' only weakly implied.	3 / 5
Trigger Term Quality	Good natural-term coverage ('materials structures', 'phase diagrams', 'symmetry', 'Materials Project', 'pymatgen') but misses common file extensions users would say (.cif, POSCAR, vasprun.xml) and synonyms, falling just short of the comprehensive 5 anchor.	4 / 5
Distinctiveness Conflict Risk	Carves a clear pymatgen-specific niche with distinct triggers (phase diagrams, symmetry sensitivity, Materials Project queries) and minimal overlap risk with other skills.	5 / 5
	Total	17 / 20 Passed

Validation

100%

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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: K-Dense-AI/scientific-agent-skills
Path: skills/pymatgen/SKILL.md
Commit: 1e5eeff

Reviewed: ~10 hours ago

Table of Contents

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