zinc-database

Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery.

Quality

71%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

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tessl review fix ./backend/cli/skills/databases/zinc-database/SKILL.md

Quality

Content

68%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 body is highly actionable with executable curl/Python examples and well-organized sections backed by a real reference file, but it carries some over-explanation and—critically—its batch/download workflows lack the validation checkpoints the rubric requires, capping workflow clarity at 3.

Suggestions

Add explicit validation/feedback loops to the batch workflows, e.g. verify retrieved compound counts, confirm download integrity, and re-query on missing IDs before proceeding.

Trim basic-concept explanations (Lipinski's Rule of Five, tranche decoding rationale) and move duplicated API endpoint detail into references/api_reference.md to improve conciseness.

Consider a quick-start section at the top that points to the reference for advanced patterns, sharpening the progressive-disclosure boundary.

Dimension	Reasoning	Score
Conciseness	Mostly efficient reference material with executable examples, but it re-explains basic concepts (Lipinski's Rule of Five, tranche decoding, subset definitions) and overlaps the separate api_reference.md, so it could be tightened.	3 / 5
Actionability	Provides concrete, copy-paste-ready curl commands and Python functions covering the common search, similarity, random-sampling, and batch-retrieval cases throughout the body.	5 / 5
Workflow Clarity	Four workflows are clearly numbered with code, but batch and potentially destructive operations (large library generation, 3D structure downloads, supplier ordering) lack explicit validation checkpoints or feedback loops, capping the score at 3 per the rubric.	3 / 5
Progressive Disclosure	Good section structure with a real one-level-deep reference (references/api_reference.md) clearly signaled under Resources; minor organization gaps and some API detail duplicated inline rather than fully offloaded.	4 / 5
	Total	15 / 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 well-scoped to a distinct niche, but it omits an explicit 'Use when...' trigger clause, leaving the triggering guidance only weakly implied and capping completeness at 3. Trigger-term coverage is solid but lacks common synonyms.

Suggestions

Add an explicit trigger clause, e.g. 'Use when searching purchasable compounds for virtual screening, docking, or analog discovery by ZINC ID/SMILES.'

Broaden trigger terms with natural synonyms such as 'chemical supplier lookup', 'purchasable molecules', or 'compound catalog search' to improve keyword coverage.

Keep the concrete action list but restructure into a 'what + when' format to lift completeness.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery' — with comprehensive coverage of the skill's capabilities.	5 / 5
Completeness	Clearly answers 'what' with concrete actions, but has no explicit 'Use when...' trigger clause equivalent — the when is only weakly implied, which caps completeness at 3 per the rubric.	3 / 5
Trigger Term Quality	Good keyword coverage (ZINC ID, SMILES, similarity, docking, analog, virtual screening, drug discovery) but missing common synonyms and file/extension-style variations users might say.	4 / 5
Distinctiveness Conflict Risk	Targets a clear niche (ZINC purchasable compounds for docking/drug discovery) with distinct triggers and minimal conflict risk with other skills.	5 / 5
	Total	17 / 20 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/databases/zinc-database/SKILL.md
Commit: a820c2f

Reviewed: 3 days ago

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