diffdock
Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill diffdockOverall
score
86%
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 chemistry workflows. The main weakness is the absence of an explicit 'Use when...' clause, which would help Claude know exactly when to select this skill. The exclusion statement ('Not for affinity prediction') is a good practice for reducing false matches.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when user asks about docking simulations, predicting how molecules bind to proteins, or virtual screening campaigns.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Predict protein-ligand binding poses', 'confidence scores', 'virtual screening', and specifies input formats (PDB/SMILES). Also explicitly states what it doesn't do ('Not for affinity prediction'). | 3 / 3 |
Completeness | Clearly answers 'what' (predict binding poses, confidence scores, virtual screening) but lacks an explicit 'Use when...' clause. The triggers are implied through domain terminology but not explicitly stated. | 2 / 3 |
Trigger Term Quality | Includes strong domain-specific trigger terms users would naturally use: 'molecular docking', 'protein-ligand', 'PDB', 'SMILES', 'binding poses', 'virtual screening', 'drug design'. These are the exact terms a computational chemist would use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with a clear niche in computational chemistry/drug discovery. The specific terms 'diffusion-based molecular docking', 'PDB/SMILES', and 'protein-ligand binding' are unlikely to conflict with other skills. The exclusion clause further sharpens boundaries. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
85%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 for molecular docking with DiffDock. Its strengths include excellent workflow clarity with validation steps, comprehensive executable examples, and good progressive disclosure to reference materials. The main weakness is moderate verbosity in introductory sections and some explanatory content that assumes less of Claude's baseline knowledge than necessary.
Suggestions
Trim the 'Overview' and 'When to Use This Skill' sections - Claude doesn't need explanations of what molecular docking is or obvious trigger phrases
Consolidate the 'Key Distinction' note and 'Limitations' section to reduce redundancy about what DiffDock doesn't do
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is comprehensive but includes some unnecessary verbosity, such as the detailed explanation of what DiffDock is and extensive context that Claude would already understand. The 'When to Use This Skill' section lists obvious triggers, and some sections could be tightened. | 2 / 3 |
Actionability | Excellent actionability with fully executable commands, complete code examples, specific file paths, and copy-paste ready bash commands. The workflows include concrete steps with actual command-line syntax and Python code. | 3 / 3 |
Workflow Clarity | Clear multi-step workflows with explicit validation checkpoints (setup_check.py, prepare_batch_csv.py --validate). The batch processing workflow has numbered steps, and troubleshooting includes specific diagnostic commands. Feedback loops are present for error recovery. | 3 / 3 |
Progressive Disclosure | Well-structured with clear overview, core workflows, and explicit references to detailed documentation (references/confidence_and_limitations.md, references/parameters_reference.md). Content is appropriately split between main skill and reference files with clear navigation signals. | 3 / 3 |
Total | 11 / 12 Passed |
Validation
88%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 |
|---|---|---|
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 | 14 / 16 Passed | |
- Reviewed
Table of Contents
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