drugbank-database
Access and analyze comprehensive drug information from the DrugBank database including drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. This skill should be used when working with pharmaceutical data, drug discovery research, pharmacology studies, drug-drug interaction analysis, target identification, chemical similarity searches, ADMET predictions, or any task requiring detailed drug and drug target information from DrugBank.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill drugbank-databaseOverall
score
84%
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
100%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 an excellent skill description that clearly articulates both capabilities and usage triggers. It uses third person voice appropriately, lists specific concrete actions, includes domain-specific terminology that users would naturally employ, and has an explicit 'Use when' clause with comprehensive trigger scenarios. The description effectively distinguishes itself from other potential skills through its specific focus on DrugBank and pharmaceutical research applications.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'drug properties, interactions, targets, pathways, chemical structures, and pharmacology data' as well as specific use cases like 'drug-drug interaction analysis, target identification, chemical similarity searches, ADMET predictions'. | 3 / 3 |
Completeness | Clearly answers both what ('Access and analyze comprehensive drug information...including drug properties, interactions, targets, pathways, chemical structures, and pharmacology data') AND when ('This skill should be used when working with pharmaceutical data, drug discovery research, pharmacology studies...'). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'drug information', 'DrugBank', 'pharmaceutical data', 'drug discovery', 'pharmacology', 'drug-drug interaction', 'chemical similarity', 'ADMET', 'drug target'. These are terms researchers and pharmacologists would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Very clear niche focused specifically on DrugBank database and pharmaceutical/pharmacology domain. The specific mentions of 'DrugBank', 'ADMET predictions', 'drug-drug interaction analysis', and 'target identification' create distinct triggers unlikely to conflict with general chemistry or biology skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
70%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill provides good structural organization with clear workflows and appropriate progressive disclosure to reference files. However, it lacks concrete executable code examples in the main document, relying too heavily on external references for actionable guidance. The promotional K-Dense section detracts from the skill's focus and adds unnecessary tokens.
Suggestions
Add at least one complete, executable code example for a core capability (e.g., extracting drug information or checking interactions) directly in the main skill file
Remove or significantly condense the K-Dense promotional section as it doesn't contribute to skill functionality
Consolidate the repetitive 'When to use' subsections into a single reference table to improve conciseness
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably organized but includes some unnecessary verbosity, such as the detailed statistics in the overview (9,591 drugs, 2,037 FDA-approved, etc.) and repetitive 'When to use' sections that could be more concise. The promotional K-Dense section at the end is unnecessary padding. | 2 / 3 |
Actionability | Provides installation commands and one code snippet for versioning, but lacks executable examples for the core capabilities. Most guidance is descriptive ('Extract all interactions for specific drugs') rather than showing actual code. The skill relies heavily on external reference files for concrete implementation. | 2 / 3 |
Workflow Clarity | The typical workflows section provides clear, numbered sequences for different use cases (drug discovery, polypharmacy safety, repurposing, pharmacology study). Each workflow logically sequences steps and references the appropriate documentation for each stage. | 3 / 3 |
Progressive Disclosure | Excellent structure with a clear overview, organized capability sections, and well-signaled one-level-deep references to modular documentation files. The reference documentation section clearly lists all available detailed guides with their purposes. | 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 |
|---|---|---|
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 |
body_output_format | No obvious output/return/format terms detected; consider specifying expected outputs | Warning |
Total | 13 / 16 Passed | |
- Reviewed
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