pymatgen
Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill pymatgenOverall
score
79%
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
83%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 with excellent specificity and trigger term coverage for computational materials science users. The main weakness is the lack of an explicit 'Use when...' clause, which would help Claude know exactly when to select this skill. The description effectively communicates capabilities but relies on implicit context for triggering.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when working with crystal structure files, analyzing electronic properties, or querying materials databases.'
Consider adding common user phrasings like 'materials simulation', 'DFT calculations', or 'electronic structure' to capture more natural trigger terms.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: crystal structures with specific formats (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, and format conversion. These are concrete, domain-specific capabilities. | 3 / 3 |
Completeness | Clearly answers 'what does this do' with specific capabilities, but lacks an explicit 'Use when...' clause or equivalent trigger guidance. The 'when' is only implied through the domain context 'for computational materials science'. | 2 / 3 |
Trigger Term Quality | Includes natural keywords users in computational materials science would use: 'CIF', 'POSCAR', 'phase diagrams', 'band structure', 'DOS', 'Materials Project', 'crystal structures'. These are the exact terms domain experts would mention. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with domain-specific terminology (CIF, POSCAR, Materials Project, DOS, band structure) that creates a clear niche. Unlikely to conflict with other skills due to specialized computational materials science focus. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
73%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured, highly actionable skill with excellent code examples and good progressive disclosure to reference materials. The main weaknesses are moderate verbosity in introductory sections and missing validation checkpoints in multi-step workflows, particularly for computational workflows where verification of convergence or file integrity would be important.
Suggestions
Add explicit validation steps to workflows (e.g., 'Verify relaxation converged before proceeding: check OUTCAR for EDIFF reached')
Trim the 'When to Use This Skill' section - Claude can infer appropriate use cases from the capabilities
Add error recovery guidance to computational workflows (e.g., what to do if VASP calculation fails to converge)
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is comprehensive but includes some unnecessary explanations (e.g., 'Pymatgen is a comprehensive Python library...') and could be tightened. The 'When to Use This Skill' section lists obvious use cases Claude could infer. However, most code examples are lean and efficient. | 2 / 3 |
Actionability | Excellent actionability with fully executable, copy-paste ready code examples throughout. Every major capability includes working Python code with proper imports, and bash commands for scripts are complete with arguments. | 3 / 3 |
Workflow Clarity | Multi-step workflows are present (e.g., Band Structure Calculation Workflow) with numbered steps, but validation checkpoints are largely missing. The workflows show sequence but lack explicit 'verify before proceeding' steps or error recovery guidance for potentially destructive operations. | 2 / 3 |
Progressive Disclosure | Excellent structure with clear overview sections pointing to detailed references (references/*.md) and scripts (scripts/*.py). Navigation is well-signaled with explicit file paths and descriptions. Content is appropriately split between quick-start and detailed documentation. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 13 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
skill_md_line_count | SKILL.md is long (691 lines); consider splitting into references/ and linking | Warning |
description_trigger_hint | Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...') | Warning |
metadata_version | 'metadata.version' is missing | Warning |
Total | 13 / 16 Passed | |
- Reviewed
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