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diffdock

Predict small-molecule binding poses with DiffDock-L (Corso et al. 2023/2024, github.com/gcorso/DiffDock) — blind diffusion docking that places a ligand into a protein pocket without a predefined search box and ranks the samples with a learned confidence model. Reach for this skill to dock a SMILES or SDF against a PDB, to generate ranked 3D poses for a small fragment library, or to get a starting pose for downstream rescoring. DiffDock predicts geometry, not affinity.

74

Quality

93%

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SKILL.md
Quality
Evals
Security

Quality

Content

87%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is dense, executable, and well-structured: runnable commands, exact schemas, and high-value gotchas with copy-paste fixes, all kept lean. The only gap is an explicit post-run validation checkpoint for the batch path, which leaves workflow clarity just short of full marks.

Suggestions

Add an explicit validation step for batch runs (e.g. after `--protein_ligand_csv`, check that each complex produced a rank1.sdf and that no row was silently treated as ligand-less) so the batch workflow has a clear validate→fix→retry checkpoint.

Note how to confirm the SO(3) precompute completed vs. a genuine hang beyond 'do not assume a hang means a crash' — e.g. the stderr/log line or timestamp to look for — to make that checkpoint actionable rather than cautionary.

Make the rescoring hand-off at the end slightly more actionable by naming the concrete command or reference section for boltz's affinity head, rather than only describing it.

DimensionReasoningScore

Conciseness

Lean throughout with no padding of concepts Claude already knows (no 'what is a PDB/SMILES'); every section — the run command, YAML-overwrite caveat, silent-precompute gotcha, and errors table — earns its tokens. The only time-sensitive stamp (license verification 2026-06-30) lives in a YAML comment, not the body, so it does not penalize conciseness.

3 / 3

Actionability

Provides a fully executable `python3 -m inference` command with real flags, the exact four-column CSV schema, the exact output filename pattern `rank{N}_confidence{score}.sdf`, and copy-paste fixes in the errors table (e.g. sed the setrlimit constant to `min(64000, rlimit[1])`).

3 / 3

Workflow Clarity

The single-complex run is unambiguous and the gotchas act as implicit checkpoints ('do not assume a hang means a crash', the memory fix, the YAML-overwrite warning), but for the batch/CSV path there is no explicit validate→fix→retry checkpoint on outputs, so it sits at score 2 rather than 3.

2 / 3

Progressive Disclosure

The body is an overview that defers the two secondary entry points (batch/library screening, sequence-only ESMFold) to references/workflows.md, which is a real, one-level-deep, clearly signaled reference with no nested indirection.

3 / 3

Total

11

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12

Passed

Description

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.

The description is specific, trigger-rich, and complete: it states concrete capabilities, gives explicit 'when to use' guidance, and draws a clear boundary against sibling co-folding skills. It avoids verbosity and over-claims (notably capping scope to geometry, not affinity).

DimensionReasoningScore

Specificity

Lists several concrete actions — 'Predict small-molecule binding poses', 'dock a SMILES or SDF against a PDB', 'generate ranked 3D poses for a small fragment library', 'get a starting pose for downstream rescoring' — matching the score-3 anchor of multiple specific concrete actions.

3 / 3

Completeness

Explicitly answers both 'what' (blind diffusion docking that places a ligand and ranks samples with a confidence model) and 'when' via the explicit trigger 'Reach for this skill to dock a SMILES or SDF against a PDB, to generate ranked 3D poses..., or to get a starting pose for downstream rescoring.'

3 / 3

Trigger Term Quality

Uses natural domain terms a user would actually say — 'dock', 'SMILES', 'SDF', 'PDB', 'binding poses', 'fragment library' — with good coverage including common input-format variations, beyond the score-2 'some relevant keywords' bar.

3 / 3

Distinctiveness Conflict Risk

Occupies a clear niche (DiffDock-L blind diffusion pose prediction) with a crisp boundary ('DiffDock predicts geometry, not affinity') and explicit routing away from boltz/chai1, making wrong-skill triggering unlikely.

3 / 3

Total

12

/

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.

Validation13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

metadata_field

'metadata' should map string keys to string values

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

13

/

16

Passed

Repository
UnicomAI/wanwu
Reviewed

Table of Contents

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