chemical-structure-converter

Convert between IUPAC names, SMILES strings, molecular formulas, and common names for chemical compounds. Supports SMILES validation, batch processing, structure standardization, and cheminformatics database preparation for drug discovery workflows.

Quality

75%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./scientific-skills/Other/chemical-structure-converter/SKILL.md

Quality

Content

75%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.

The content is well-structured, concise, and actionable, with a clear validated workflow and a real supporting script. Its main weakness is a mismatch between the documented SMILES-validation/conversion behavior and the actual script implementation, which slightly undermines actionability.

Suggestions

Align the documented validation checks with scripts/main.py: the body claims ring-closure and '@' atom validity checks, but the script only verifies parentheses and brackets — either implement the additional checks or correct the documentation.

Clarify that SMILES→IUPAC and SMILES→Formula conversions rely on the local mock database lookup rather than true structural conversion, so expectations match the 4-compound DB_VERSION reality.

Tighten or relocate the SMILES Quick Reference into a dedicated reference file to further reduce inline tokens and improve progressive disclosure.

Dimension	Reasoning	Score
Conciseness	The body is efficiently organized with tables, focused code snippets, and CLI examples, and does not pad with basic chemistry explanations Claude already knows; only minor sections (SMILES Quick Reference, some restated prose) could be trimmed to reach a 5.	4 / 5
Actionability	Provides concrete, mostly executable guidance — real Python imports, exact CLI flags, a parameter table — but the validate_smiles implementation only checks parentheses/brackets while the body claims ring-closure and '@' atom checks, and arbitrary SMILES→IUPAC conversion is not actually implementable, leaving minor gaps.	4 / 5
Workflow Clarity	A clear five-step workflow with a Quick Check validation checkpoint, an explicit fallback path, and a batch-mode feedback loop (the X/N summary line) is present; only minor validation gaps keep it from a 5.	4 / 5
Progressive Disclosure	Content is well-organized into clearly signaled sections with a one-level-deep reference to the real scripts/main.py bundle file; most content is appropriately placed inline for a skill of this size, with only minor organization gaps.	4 / 5
	Total	16 / 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.

The description is specific and distinctive, concretely enumerating the converter's actions, but it omits any explicit 'Use when...' trigger guidance, so it answers 'what' clearly but not 'when'. Adding a trigger clause would raise completeness and likely trigger_term_quality.

Suggestions

Add an explicit 'Use when...' clause naming natural user phrases (e.g., 'Use when converting between compound names, SMILES, IUPAC names, or molecular formulas, or when validating SMILES syntax for cheminformatics workflows').

Include common user synonyms and concrete trigger phrases such as 'compound lookup,' 'canonicalize a structure,' or 'get a SMILES from a name' to improve trigger-term coverage.

Consider naming the supported input/output formats more uniformly (e.g., mention InChI consistently since it appears in the body but not the description) to keep triggers and capabilities aligned.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete, distinct actions — 'Convert between IUPAC names, SMILES strings, molecular formulas, and common names,' plus 'SMILES validation, batch processing, structure standardization' — giving comprehensive coverage of the converter's capabilities.	5 / 5
Completeness	The 'what' is clearly and concretely stated, but there is no 'Use when...' clause or equivalent explicit trigger guidance describing when Claude should invoke it, which caps completeness at 3 per the rubric guideline.	3 / 5
Trigger Term Quality	Good natural keyword coverage ('IUPAC names,' 'SMILES strings,' 'molecular formulas,' 'common names,' 'batch processing') but missing common synonyms a user might naturally say and no file-extension-style triggers; not quite comprehensive enough for a 5.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche — chemical identifier interconversion — with distinct, specific triggers unlikely to collide with other skills, giving minimal conflict risk.	5 / 5
	Total	17 / 20 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: aipoch/medical-research-skills
Path: scientific-skills/Other/chemical-structure-converter/SKILL.md
Commit: f5ef65b

Reviewed: ~13 hours ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.