Lists multiple specific concrete actions: predict protein-ligand binding poses, confidence scores, virtual screening. Also specifies input formats (PDB/SMILES) and the broader domain (structure-based drug design), plus an explicit exclusion (not for affinity prediction).

The 'what' is well-covered (predict binding poses, confidence scores, virtual screening), but there is no explicit 'Use when...' clause or equivalent trigger guidance. The when is only implied by the listed capabilities. Per rubric guidelines, a missing 'Use when...' clause caps completeness at 2.

Includes strong natural keywords a computational chemist or drug designer would use: 'molecular docking', 'protein-ligand binding poses', 'PDB', 'SMILES', 'virtual screening', 'structure-based drug design', 'confidence scores'. These are the exact terms domain users would naturally mention.

Highly distinctive niche — diffusion-based molecular docking is a very specific computational chemistry task. The explicit exclusion of affinity prediction further sharpens the boundary. Unlikely to conflict with other skills.

The skill is extremely verbose at ~400+ lines. It explains concepts Claude would know (what molecular docking is, what PDB files are), includes a lengthy 'When to Use This Skill' section that restates obvious triggers, provides extensive resource descriptions that duplicate information already in the body, and includes a 10-item best practices list that largely restates earlier content. The overview section explains DiffDock's purpose redundantly.

The skill provides fully executable commands with concrete examples throughout—specific CLI invocations with real flags, CSV format specifications, Python code for ensemble docking, and exact bash commands for scoring integration. Commands are copy-paste ready with realistic parameters and file paths.

Multi-step workflows are clearly sequenced (batch processing has Step 1/Step 2, virtual screening has pre-compute then run), but validation checkpoints are weak. There's no explicit 'validate output before proceeding' step after docking runs. The batch CSV validation is good, but the main docking workflows lack feedback loops for error recovery—troubleshooting is separated into its own section rather than integrated into workflows.

The skill does reference external files (references/confidence_and_limitations.md, references/parameters_reference.md, references/workflows_examples.md) with clear signals, which is good. However, the main file itself is monolithic with too much inline content—the troubleshooting section, advanced techniques, detailed parameter customization, and extensive resource descriptions could be offloaded to reference files. The references section at the bottom essentially re-documents what the referenced files contain.